Conservation agriculture has been promoted as an alternative to conventional cropping systems to mitigate environmental pressures stemming from intensive tillage. Management practices in conservation agriculture can affect weed presence both within and between growing seasons. This study examined how recently adopted conservation agriculture, integrating no-till and cover crops, affected weed dynamics in a Mediterranean annual cropping system. Weed density, cover, richness, diversity and composition were compared between conventional and conservation agriculture during the inter-crop period and the early establishment of maize, the cash crop. Weed surveys were conducted in large-scale experimental plots during both sampling seasons over two consecutive years (2021-2022). During the inter-crop period, weed density was consistently lower in conservation agriculture than in conventional agriculture (10.1 ± 1.9 vs. 63.0 ± 14.4 plants m⁻² in 2021, respectively), whereas weed cover responses varied between years. During the early maize establishment, conservation agriculture reduced weed density and cover (11.6 ± 3.17 vs. 25.2 ± 11.0 plants m⁻² and 1.0 ± 0.53% vs. 4.6 ± 2.47% cover in 2022). Weed species richness was low (5-16 species) and varied across cropping systems, sampling seasons and years, while Shannon diversity was overall higher under conservation agriculture. Species composition and dominant species differed significantly between cropping systems and sampling seasons. Maize yield was similar between cropping systems, although a decrease was observed under conservation agriculture in 2022. This study highlights the importance of the transitional phase following conservation agriculture adoption, which is characterized by rapid changes in weed species composition. Furthermore, conservation agriculture demonstrates potential as a sustainable option for early-season weed control.

Cattle grazing near and inside watercourses may potentially contribute fecal coliform (FC) to surface water (streams, lakes, etc.), posing risks to both ecosystems and public health. This study presents a novel approach for estimating the impact of grazing cattle on watercourses under diverse conditions and to assess the effects of mitigation measures. The approach is based on a conceptual framework integrating field observations, long-term laboratory analysis, and FC fate and transport prediction modeling. The field survey revealed uniform distribution of dung across the plots, irrespective of distance to water. Microbial fate in the cattle dung over time under varying temperatures describes growth followed by a decline. The developed predictive process-based model showed that indirect FC load reaching the stream after dry grazing days (~10¹⁰ CFU per cow per rain event) was approximately two orders of magnitude higher than FC load by direct in-stream deposition (~10⁸ CFU per cow per day). These loads were significantly reduced by creating a buffer zone of 50 m between the watercourse and grazing areas or by cattle exclusion from the watercourses for a limited time - at least during sensitive periods, i.e., when heavy rain is expected after several dry weeks and especially during the tourist period. The model results can help policy makers to quantify the effectiveness of management practices and will assist in identifying ways of revising existing measures to improve this effectiveness. The developed model can be adapted to different cases, considering the conditions prevailing on the sites.

BACKGROUND: The aggregated spatial pattern of weeds in agricultural systems is a widespread phenomenon. Depending on species characteristics, for example life form, weed patches may maintain their location across growing seasons. These spatial and temporal patterns underpin site-specific weed control, where herbicide applications are tailored to within-field infestation levels using variable rates and targeted spot spraying, thereby reducing overall herbicide use. The various practices that comprise conservation agriculture, like tillage reduction or cover crops, may affect the spatial patterns and temporal stability of weeds. This study examines short-term responses of weed spatial and temporal patterns following the implementation of conservation agriculture practices. Ten large experimental plots, five for each of conservation and conventional agriculture, located in northern Israel, were surveyed over two consecutive years (2021–2022) to evaluate the spatio-temporal patterns of weeds. RESULTS: Weed spatial patterns were influenced by both cropping system and species life form. Higher levels of weed aggregation were observed in conservation compared to conventional agriculture. Perennial species formed denser weed patches compared to annuals. Weed patch stability was mainly governed by species life form, with perennials demonstrating persistent patches. CONCLUSION: The transition to conservation agriculture may enhance the phenomena of weed aggregation and weed patch stability even within a short time. Consequently, under these conditions, the benefits of site-specific weed control become even more significant compared to conventional systems. This suggests that two key objectives, mitigating soil degradation and decreasing the use of herbicides, can be accomplished through conservation agriculture and site-specific herbicide applications.

Lake management actions are required to protect lake ecosystems that are being threatened by climate change. Freshwater lakes in semiarid regions are of upmost importance to their region. Simulations of the subtropical Lake Kinneret project that rising temperatures will cause change to phytoplankton species composition, including increased cyanobacteria blooms, endangering lake ecosystem services. Using lake ecosystem models, we examined several management actions under climate change, including two alternatives of desalinated water introduction into the lake, hypolimnetic water withdrawal, watershed management changes and low versus high lake water level. To account for prediction uncertainty, we utilized an ensemble of two 1D hydrodynamic—biogeochemical lake models along with 500 realizations of meteorological conditions. Results suggest that supplying desalinated water for local use, thus releasing more natural waters through the Jordan River, increasing nutrient flow, may reduce cyanobacteria blooms, mitigating climate change effects. However, these results are accompanied by considerable uncertainty.

Providing equitable food security for a growing population while minimizing environmental impacts and enhancing resilience to climate shocks is an ongoing challenge. Here, we quantify the resource intensity, environmental impacts and nutritional output of a small (0.075 ha) low-input subsistence Mediterranean agroecological farm in a developed nation that is based on intercropping and annual crop rotation. The farm provides one individual, the proprietor, with nutritional self-sufficiency (adequate intake of an array of macro- and micro-nutrients) with limited labor, no synthetic fertilizers or herbicides, and zero waste, effectively closing a full farm-table-farm cycle. We find that the agroecological farm outperforms conventional farming as practiced in the same country in terms of both lower environmental burdens, across all examined environmental metrics (63% lower on average) per kg produce, and higher nutritional score (66% higher on average). Per equal farmland, the environmental lopsidedness was even higher (79% lower than conventional farming on average), with nearly the same nutritional score (3% lower on average). Moreover, when considering total land area, which includes farmland and supporting non-agricultural lands, as well as postgate impacts and food losses, the advantage of the agroecological system over conventional farming is even more pronounced. Situated within a Mediterranean region that is undergoing rapid climate change, this food system is a unique case study of nutrition- and environment-oriented food production system. While its deployment potential is limited by lack of supportive policies, it nonetheless represents one of the most starkly bold alternatives to current food systems.

Climate change is anticipated to alter lake ecosystems by affecting water quality, potentially resulting in loss of ecosystem services. Subtropical lakes have high temperatures to begin with and are expected to exhibit higher temperatures all year round which might affect the thermal structure and ecological processes in a different manner than lakes in temperate zones. In this study the ecosystem response of the sub-tropical Lake Kinneret to climate change was explored using lake ecosystem models. Projection reliability was increased by using a weather generator and ensemble modelling, confronting uncertainty of both climate projections and lake models. The study included running two 1D hydrodynamic-biogeochemical models over one thousand realizations of two gradual temperature increase scenarios that span over 49 years. Our predictions show that an increase in air temperature would have subtle effects on stratification properties but may result in considerable changes to biogeochemical processes. Water temperature rise would cause a reduction in dissolved oxygen. Both of these changes would produce elevated phosphate and lowered ammonium concentrations. In turn, these changes are predicted to modify the phytoplankton community, expressed chiefly in increased cyanobacteria blooms at the expense of green phytoplankton and dinoflagellates; these changes may culminate in overall reduction of primary production. Identification of these trends would not be possible without the use of many realizations of climate scenarios. The use of ensemble modelling increased prediction reliability and highlighted elements of uncertainty. Though we use Lake Kinneret, the patterns identified most likely indicate processes that are expected in sub-tropical lakes in general.

At the present state of industrialized agriculture and specialized economy, achieving nutritional self-sufficiency on a personal level is widely considered a naïve goal, unsuited to the present technological era. Furthermore, nutritional self-sufficiency is considered overly demanding in terms of training, land, labor intensity, and time requirements. This study contests these common notions. Drawing on a study of a small (approximately 0.075 ha) low-input self-sufficient farm in an industrialized country, we show that achieving nutritional self-sufficiency on this farm required modest initial agricultural training, a small parcel of land, and, perhaps most surprisingly, a single day of labor per month, on average. We offer these results as proof of concept that, when properly planned, nutritional self-sufficiency may be sustained over an extended period (more than a decade) and that doing so may be compatible with working full-time outside the farm, as well as engaging in regular leisure activities. We also propose that some upscaling is viable at the industrial, small-scale, and household levels.

Climate-related benefits of afforestation depend on the balance of the often-contrasting effects of biogeochemical (carbon sequestration) and biogeophysical (radiation balance) effects. These effects are known to vary at the continental scale (e.g., from boreal to tropical regions). Here, we show in a four-year study that the biogeochemical vs. biogeophysical balance in paired forested and non-forested ecosystems across short distances (approximately 200 Km) and steep aridity gradient (aridity index 0.64 to 0.18) can change dramatically. The required time for the forestation cooling effects via carbon sequestration, to surpass warming effects associated with the forests’ reduced albedo and suppressed longwave radiation, decreased from 213 years in the driest sites to 73 years in the intermediate and 43 years in the wettest sites. Climate-related benefits of forestation, previously considered at large-spatial scales, should be considered at high-spatial resolutions in climate-change mitigation programs aimed at taking advantage of the vast non-forested dry regions.

Phil. Trans. R. Soc. B 378, 20210397. (Published 23 January 2023). (https://doi. org/10.1098/rstb.2021.0397) In the original version of this article, an affiliation was omitted for author Eörs Szathmáry. The correct affiliations are shown below. This has now been corrected on the publisher's website. Eörs Szathmáry5,6,7 5Institute of Evolution, Centre for Ecological Research, 1121 Budapest, Hungary 6Parmenides Foundation, 82343 Pöcking, Germany 7Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös University, Budapest 1117, Hungary.

Lake ecosystem models are essential for capturing ecosystem response to climate change and for studying eutrophication. Model ability to capture ecosystem dynamics is limited for various reasons. Aiming to increase confidence in simulations of future scenarios, the current study adapts a recently established four level validation framework, named CSPS (Conceptual, State, Process and System validation). Lake ecosystem model WET was calibrated for lake Kinneret and validated using the CSPS framework, inspecting 18 state variables, 11 processes and 15 emergent properties. Validation showed that most processes and emergent properties were correctly simulated with some weaknesses revealed such as light attenuation and particle dynamics. Modifying the model to overcome those weaknesses will result in increased model reliability. This conclusion would have been impossible without the process validation. We conclude that higher validation levels are necessary to receive reliable results when running process-based lake models on future scenarios.

An evolutionary transition in individuality (ETI) occurs when a previously independent organism becomes a lower level unit within a higher hierarchical level (for example, cells in an organism, ants in a colony). Using archaeological and historical accounts from the last 12 000 years, I empirically examine the proposition that human society increasingly functions as a higher hierarchical level within which individuals integrate as lower level units. I evaluate human societal development with respect to three criteria that together indicate complexity in biological systems and serve as an operationalization scheme for ETIs: size, inseparability and specialization. The size of the largest polity has increased seven orders of magnitude, from hundreds to billions. Inseparability became nearly complete since Mesopotamian city-states, following the first appearance of intricate specialization (division of labour). Connectivity within a polity has increased rapidly during the last few centuries, and particularly within the last few decades. In view of these results, I formulate the following hypothesis: human society is undergoing an evolutionary transition in individuality, driven by socio-cultural-technological processes. This proposition requires a detailed theoretical basis and further empirical testing. I propose four predictions derived from the hypothesis that may be used to test it. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Human societies are no doubt complex. They are characterized by division of labour, multiple hierarchies, intricate communication networks and transport systems. These phenomena and others have led scholars to propose that human society may be, or may become, a new hierarchical level that may dominate the individual humans within it, similar to the relations between an organism and its cells, or an ant colony and its members. Recent discussions of the possibility of this major evolutionary transition in individuality (ETI) raise interesting and controversial questions that are explored in the present issue from four different complementary perspectives. (i) The general theory of ETIs. (ii) The unique aspects of cultural evolution. (iii) The evolutionary history and pre-history of humans. (iv) Specific routes of a possible human ETI. Each perspective uses different tools provided by different disciplines: biology, anthropology, cultural evolution, systems theory, psychology, economy, linguistics and philosophy of science. Altogether, this issue provides a broad and rich application of the notion of ETI to human past, present and perhaps also future evolution. It presents important case studies, new theoretical results and novel questions for future research. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Forestation of the vast global drylands has been considered a promising climate change mitigation strategy. However, its actual climatic benefits are uncertain because the forests’ reduced albedo can produce large warming effects. Using high-resolution spatial analysis of global drylands, we found 448 million hectares suitable for afforestation. This area’s carbon sequestration potential until 2100 is 32.3 billion tons of carbon (Gt C), but 22.6 Gt C of that is required to balance albedo effects. The net carbon equivalent would offset ~1% of projected medium-emissions and business-as-usual scenarios over the same period. Focusing forestation only on areas with net cooling effects would use half the area and double the emissions offset. Although such smart forestation is clearly important, its limited climatic benefits reinforce the need to reduce emissions rapidly.

We explored if and how seasonal fires interact with microhabitat type (i.e. under Pistacia shrub, under Cistus shrub or a canopy gap) to influence the composition of the germinable soil seed bank (GSSB) community in a typical eastern Mediterranean woodland. We conducted a field experiment, involving prescribed spring and autumn burns, and thereafter quantified the seed germination patterns using soil samples collected from both burned and adjacent unburned control plots. Soil temperature was significantly higher during autumn burns, while being more variable during spring burns. Fire caused overall reductions in GSSB density, richness and diversity. The reductions in GSSB richness and diversity were significantly stronger under Pistacia and Cistus shrubs located within plots subjected to autumn burns, and these patterns were mainly evident among annuals. GSSB density of dwarf shrubs was higher in samples collected from burned plots, and this pattern was more pronounced in samples collected under Pistacia and Cistus shrubs. Together with the appearance of unique species, seasonal fires led to significant changes in the composition of the GSSB community. Our results illustrate that seasonal fires interact with spatial heterogeneity to influence the composition of the GSSB community mostly via differential effects on the germination densities of annuals and dwarf shrubs. These findings imply that the increase in the frequency of seasonal fires, which has occurred in the eastern Mediterranean basin during the last few decades, may translate into a shift in eco-evolutionary selection pressures, operating on plants inhabiting this unique ecosystem.

The framework of land sparing versus land sharing provides a useful analytical tool to address the crop-production/biodiversity trade-off. Despite multiple case studies testing the sparing–sharing trade-off, this framework still lacks the ability to identify the conditions in which sparing, or sharing, would be the preferred strategy for pareto-optimizing both food production and biodiversity. Under some conditions, ecosystem services may create a positive feedback between biodiversity and crop production, affecting the optimization. This study aims to identify the conditions and the relevant variables that determine the preferred land use strategy in terms of maximizing both biodiversity and food production, while accounting for positive feedback of ecosystem services in this analysis. We used a simulation model with data from a mixed cropping landscape (100 km²) covering seven crop types, five taxonomic groups, three biodiversity metrics and 23 bioindicators to explore the variables shaping the biodiversity–production trade-off and ecosystem services underlying it. We explored a continuum of sparing large semi-natural patches to sharing by maintaining uncultivated field margins of varying size. Land sparing outperformed land sharing in 62% of the scenarios and it was economically more predictable. The optimization was shaped by costs, associated with crop type, rather than by landscape composition and configuration, biodiversity metric, taxonomic group or bioindicator. Landscape configuration and taxonomic group results corroborate the notion that land sharing benefits mainly small organisms, and that the common width of field margins in many agri-environmental policies (10 m) is not cost-effective compared to land sparing. Land sharing was the optimal strategy whenever it resulted in minimal costs, despite contributing little to biodiversity. Yet, when field margins were >20 m wide (small-scale sparing), land sharing maintained higher biodiversity and was at least as cost-effective as sparing. Synthesis and applications. Our model highlights the importance of socio-economic variables compared to ecological variables in selecting land-management strategy to pareto-optimize both food production and biodiversity. Considering opportunity costs alongside economic benefits from ecosystem services in various cropping systems may therefore improve the cost-effectiveness of biodiversity conservation policies in agricultural landscapes.

Human-driven landscape changes strongly influence landscape functionality and aesthetics. While landscape planners have access to biophysical data for decision-making, they often do not have the necessary information about social variables, such as aesthetic tastes, feelings, or functions of a place. Visualizing future landscapes under alternative management scenarios could be a valuable tool for aiding land management decisions. Towards these ends, empirical, quantitative ecological data on vegetation composition, pattern, and processes in a Long-Term Ecological Research (LTER) site in Israel were integrated into a computerized, 3-D representation of current and future landscapes. Our objectives were (1) to visualize landscape-shaping processes, such as wildfire, grazing, and species colonization, that can assist managers, planners, and the public to envision the long-term visual significance of management alternatives and (2) to validate the similarity between the 3-D model and reality. The visual model we developed is based on 30 years of scientific knowledge and ecological data describing vegetation processes in Ramat Hanadiv, a case study of ecological conditions and processes relevant to the Mediterranean and other complex ecosystems worldwide. Before studying the role of the 3-D model in decision-making, validation was performed by comparing ‘current state’ model representation with real-world photos from the perspective of the observer. The model was found to be a valid representation of reality. Looking to the future, we suggest that the ability to create future landscapes using scientific data can assist to improve decision-making processes, balancing ecological and social needs.

Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the little attention given to the negative effects of reduced albedo associated with forestation in many regions, and ignoring the potential of drylands that account for 40% of the global potential land area for forestation. We propose an approach to identify suitable land for forestation and quantify its 'net equivalent carbon stock change' over 80 years of forest lifetime (NESC), accounting for both carbon sequestration and albedo changes. We combined remote-sensing tools with data-based estimates of surface parameters and with published climate matrices, to identify suitable land for forestation actions. We then calculated the cumulative (over 80 years) 'net sequestration potential' (ΔSP), the 'emission equivalent of shortwave radiation forcing' (EESF) due to changes in surface albedo, and, in turn, the combined NESC = ΔSP-EESF, of planting forests with >40% tree-cover. Demonstrating our approach in a large climatically diverse state (Queensland), we identified 14.5 million hectares of potential forestation land in its semi-arid land and show that accounting for the EESF, reduces the climatic benefits of the ΔSP by almost 50%. Nevertheless, it results in a total NESC of 0.72 Gt C accumulated by the end of the century, and 80 years of forestation cycle. This estimated NESC is equivalent to 15% of the projected carbon emissions for the same period in Queensland, for a scenario of no change in emission rates during that period. Our approach extends restoration efforts by identifying new land for forestation and carbon sequestration but also demonstrates the importance of quantifying the climatic value of forestation in drylands.

The order of affiliations of the authors of the above article were rearranged and Ofer Ovadia one of the co-authors was added as co-corresponding author in the authorship group and affiliation section. The original article has been corrected.

Fire effects on ecosystems range from destruction of aboveground vegetation to direct and indirect effects on belowground microorganisms. Although variation in such effects is expected to be related to fire severity, another potentially important and poorly understood factor is the effect of fire seasonality on soil microorganisms. We carried out a large-scale field experiment examining the effects of spring (early-dry season) versus autumn (late-dry- season) burns on the community composition of soil fungi in a typical Mediterranean woodland. Although the intensity and severity of our prescribed burns were largely consistent between the two burning seasons, we detected differential fire season effects on the composition of the soil fungal community, driven by changes in the saprotrophic fungal guild. The community composition of ectomycorrhizal fungi, assayed both in pine seedling bioassays and from soil sequencing, appeared to be resilient to the variation inflicted by seasonal fires. Since changes in the soil saprotrophic fungal community can directly influence carbon emission and decomposition rates, we suggest that regardless of their intensity and severity, seasonal fires may cause changes in ecosystem functioning.

A major challenge in rangeland ecology is understanding diverse responses of diversity to grazing, what factors determine these responses and what their relative importance is. Numerous site-level studies have explored grazing-diversity relations, but meta-analyses of these empirical results at a global scale are scarce. Our goals were to estimate the mean effect of grazing on plant richness and to assess the relative importance of different variables at a global scale. We conducted a global meta-analysis of 259 comparisons of plant richness in grazed vs. ungrazed sites from 96 published studies. Then, we performed analyses of subgroup, meta-regression and correlation for testing the relative roles of regional and local variables (e.g. evolutionary history of grazing, aridity, stocking rate, etc.). Globally, grazing significantly increased plant richness compared to grazing exclusion, especially in wet grasslands. The effect of evolutionary history of grazing was insignificant, which cannot support the major perception behind Milchunas-Sala-Lauenroth (MSL) model. Aridity, vegetation type, and stocking rate were three important variables that together explained 41 % of the global variation in plant richness. The roles of stocking rate and duration of grazing exclusion were revealed only when aridity and vegetation type were added to the analyses, which showed a stronger effect of stocking rate in wet areas but a stronger effect of the duration of grazing exclusion in dry areas. Changes in plant richness were significantly correlated with changes in soil carbon and nitrogen, both showing hump-shaped patterns. Our findings substantiate that scholars may improve existing theories (e.g. the MSL model, the intermediate disturbance hypothesis) through further expounding the interactions between grazing variables and aridity and vegetation type. Rangeland managers need to devise local-scale grazing strategies for conserving plant diversity according to site-specific conditions, rather than adopting a one-size-fits-all solution.

Compassionate conservation is based on the ethical position that actions taken to protect biodiversity should be guided by compassion for all sentient beings. Critics argue that there are 3 core reasons harming animals is acceptable in conservation programs: the primary purpose of conservation is biodiversity protection; conservation is already compassionate to animals; and conservation should prioritize compassion to humans. We used argument analysis to clarify the values and logics underlying the debate around compassionate conservation. We found that objections to compassionate conservation are expressions of human exceptionalism, the view that humans are of a categorically separate and higher moral status than all other species. In contrast, compassionate conservationists believe that conservation should expand its moral community by recognizing all sentient beings as persons. Personhood, in an ethical sense, implies the individual is owed respect and should not be treated merely as a means to other ends. On scientific and ethical grounds, there are good reasons to extend personhood to sentient animals, particularly in conservation. The moral exclusion or subordination of members of other species legitimates the ongoing manipulation and exploitation of the living worlds, the very reason conservation was needed in the first place. Embracing compassion can help dismantle human exceptionalism, recognize nonhuman personhood, and navigate a more expansive moral space.

Intensifying agricultural production in sustainable ways is pivotal to increasing food production while reducing environmental impacts. Ecological intensification is based on managing organisms that provide services underlying crop production to simultaneously intensify agricultural production and increase biodiversity. However, few studies address the interactions and trade-offs between biodiversity, multiple ecosystem services and crop production. We experimentally quantified the effect of uncultivated field margins, a prominent practice of ecological intensification, on agricultural production, biodiversity, as well as on multiple ecosystem services and disservices, in an intensive Mediterranean agro-ecosystem. We used a split-plot design and sampled butterflies, rodent and arthropod pests, arthropod natural enemies (both parasitoids and predators), weeds, damage to crop and crop yield in different distances into the field in 3 tomato and 11 wheat crops along the growing season. Field margins increased natural enemy densities, reduced pest-damage to crop and consequently increased yield in tomato crops. Notably, we found that pest control by one predator species was dominant in the field centre, whereas parasitoid natural enemies were confined to the field edges. Pest control was more prominent in the late crop-stage compared to early season sampling and field margins increased weed control in tomato crops by reducing weed cover. Field margins increased natural enemy densities in wheat at the beginning of the season, but effects on arthropod pests were inconsistent. Field margins slightly increased weed cover, but had no impact on rodent densities and total yield. Butterfly abundance, but not richness, was positively affected by vegetated field margins. Synthesis and applications. Promoting ecological intensification requires a holistic approach that considers the complex relationships among ecological and economic aspects of agro-ecosystems. We found that ecologically intensified field margins provided pest and weed control in the highly intensive tomato crop, yet they increased weed cover in wheat, which could potentially restrict yields at the field scale. Farmers' guidelines should therefore consider the interactive effects of multiple services on a variety of crops. Moreover, biodiversity components that do not provide crop production services should be independently targeted (e.g. by sowing plants that provide food resources).

In the context of grazing, the intermediate disturbance hypothesis (IDH) predicts that plant diversity peaks under moderate grazing, resulting in a hump-shape pattern for the grazing–diversity relationship. Although this has been debated due to contradictory empirical results, the IDH is still widely accepted among rangeland ecologists. The Milchunas–Sala–Lauenroth (MSL) model predicts that in arid areas grazing affects diversity negatively regardless of grazing intensity, whereas in mesic areas grazing effects on plant diversity are the same as predicted by the IDH. Very few studies have attempted to specifically evaluate the applicability of the IDH and MSL to grazing systems at a global scale, accounting for the possible effects of climate. We conducted a meta-analysis and vote-counting analysis to evaluate these two hypotheses. The results of both analyses show that the IDH cannot be applied globally and that its application largely depends on aridity. The IDH prediction of a hump-shape curve is supported in wet areas, while in dry areas there is a slight decrease in species richness with increasing grazing intensity. Overall, the MSL model correctly predicted grazing–diversity relations in both wet and dry areas. Looking at specific ecosystem types, we found that these results hold in grasslands, but not in woodlands. Differences between livestock types, not considered by the MSL model, were found to be important. Mixed sheep and goat grazing in dry areas resulted in a significant decline of species richness with grazing intensity, while grazing by sheep only had little effect on species richness. Cattle grazing and yak grazing in wet areas yielded a clear hump-shape pattern. Therefore, we conclude that the climate-specific MSL model better predicts the impact of grazing on diversity than the IDH in rangelands, and that the response patterns of plant richness to grazing are dependent on aridity, grazing intensity and grazer type.

Evolutionary transitions in individuality (hereafter, ETIs), such as the transition to multi-cellularity and the transition to social colonies, have been at the centre of evolutionary research, but only few attempts were made to systematically operationalize this concept. Here, we devise a set of four indicators intended to assess the change in complexity during ETIs: system size, inseparability, reproductive specialization and non-reproductive specialization. We then conduct a quantitative comparison across multiple taxa and ETIs. Our analysis reveals that inseparability has a crucial role in the process; it seems irreversible and may mark the point where a group of individuals becomes a new individual at a higher hierarchical level. Interestingly, we find that disparate groups demonstrate a similar pattern of progression along ETIs.

Careful consideration of the cost-effectiveness of wildlife-friendly practices is key to promote fit-for-purpose agro-ecological policies, but quantitative evaluations of economic costs and ecological benefits compared to other land management alternatives are scarce. We compared the cost-effectiveness of uncultivated field-margins, a widespread wildlife-friendly practice, to that of conserving large semi-natural patches at the landscape scale and over multiple seasons for six crop types in Mediterranean Israel. Increased production expenditures and revenue loss were used to assess costs. Ecological benefits were measured in terms of (1) potential biological pest-control, and (2) richness and abundance of plants, birds, butterflies, ground-dwelling and plant-associated arthropods. Field-margins increased biodiversity by 64 % compared to cultivated land and accounted for 78 % of the biodiversity recorded in semi-natural patches. The biodiversity benefits of field-margins varied across seasons and taxa. Arthropod richness in field-margins did not differ from semi-natural patches, but bird and plant richness were 42–46 % lower. Field-margins increased potential biological pest-control, but with no spillover into the fields. Field-margins were associated with revenue loss in most crop types, leading to lower cost-effectiveness compared to creating large semi-natural patches. Yet, in a few crop types which exhibited low or positive effect of field-margins on income, field-margins were more cost-effective than semi-natural patches. These results indicate that there is no one-size-fits-all agri-environmental policy. Measures need to be locally tailored (e.g. crop-specific) to maximize ecological and economic benefits at large spatial scales, while considering that in many cases setting aside contiguous areas for conservation is more cost-effective than field-scale wildlife-friendly practices.

Aim: Plant species regenerate after fire either through vegetative growth (i.e., obligate resprouters) or seed germination (i.e., obligate seeders), with some species adopting both strategies (i.e., facultative seeders). Fire season can have important consequences for the relative abundance of plant species adopting these regeneration strategies. The present study aimed to test for differential effects of fire season on perennial plant community composition. Location: Eastern Mediterranean woodland in the Judea Mountains, Israel. Methods: We conducted, for the first time, a large-scale field experiment, involving prescribed spring and autumn burns in a typical eastern Mediterranean woodland. We sampled the perennial plant community before and after the burns, quantifying temporal changes in community composition caused by seasonal fires. Results: Although fire intensity and severity were consistent between the two seasonal fires, plant community composition differed between areas subjected to spring or autumn burns. The abundances of all common species, Pistacia lenticus (obligate resprouter), Cistus spp. (obligate seeders) and Teucrium divaricatum (facultative seeder), were all reduced by both fires. Yet, their dominance in the post-fire perennial plant community was retained. Differential fire season effects were detected only among obligate seeders, which experienced a stronger reduction in abundance after spring than after autumn burns. Conclusions: Differential fire season effects on the perennial plant community resulted from phenological rather than fire intensity/severity effects. Such changes in community composition may have important implications for plant community dynamics, because they affect the circle of “fire event–plant regeneration–fire reoccurrence”, determining the probability and intensity of future fires. Furthermore, even though the eastern mediterranean ecosystem is considered highly resilient to disturbances, increased spatio-temporal variation in fire season, may result in a new vegetation mosaic, differing from the contemporary one, i.e., an alternative stable state.

Intense human activity in the marine environment poses a threat to marine ecosystem. The ecosystem-based planning and management approach has developed over the past decades with the goal of reducing this threat by defining planning and management of uses in a way that mitigates negative effects on ecosystem structure and function. For oceans and coasts, marine spatial planning (MSP) can further aid the implementation of ecosystem-based management, a widely accepted tenet of planning for the marine environment. It can do so by allocating different uses of space in a way that reduces conflicts for the benefit of the environment. Here, we propose an approach to MSP that incorporates principles of reconciliation ecology for the planning of marine (nearshore) enclosures. The approach supports conservation within and around anthropogenic elements outside of marine protected areas. Since human activity typically involves some damage to natural ecosystem, this research contributes by proposing a way to incorporate ecosystem modeling for MSP that includes human activity. Examining areas of human activity under different management scenarios allows identification of possible trends in human-natural ecosystem interactions. Using such an approach increases marine conservation opportunities, and directs educated and cautious MSP in ways that allow implementation of an ecosystem-based approach.

Active management of endangered species is required for the persistence of many rare species. Species translocation, a common practice used to mitigate the negative effects of small population sizes, entails risks of outbreeding depression due to dilution of local adaptations, and therefore must be weighed against the costs of a hands-off conservation approach. Iris lortetii is an endangered rhizomatous plant, growing in a small number of isolated populations in northern Israel. We implemented a quasi-in-situ reintroduction program by planting 234 rhizomes from diverse origins in five new sites. All new sites were selected to be as similar as possible to those of the largest natural population. We recorded plant survival and flowering in the field after four years. Flowering plants were artificially crossed either with plants from the same population of origin (within population) or with plants from different origin (between populations). We found no differences in survival between populations of origin and only some indication of local adaptations in the form of increased flowering of the local population. Nonetheless, seed set was significantly higher (a 73% increase) in crosses between populations of origin, compared to within-population crosses, suggesting low genetic diversity within the natural populations. The ability to combine active conservation with rigid testing of theoretical hypothesis, while avoiding all risk to natural populations, highlights the value of the quasi-in-situ approach for restoration. Our results indicate that, in the case of Iris lortetii, active relocation of genotypes, seeds or pollen can enhance the survival of natural populations over time.

Growing awareness of the role of marine spatial planning (MSP) in promoting sustainable development and ecosystem-based management highlights the need to use decision-support tools, and specifically ecological modelling tools, to consider the future impact of planning and management on the marine environment. However, how these tools can be incorporated into planning and their expected contribution is not always clear. Here, an Ecopath with Ecosim and Ecospace food-web model was used in a hypothetical planning process to examine the integration of food-web tools in specific stages of MSP. The model was used to examine spatial alternatives and management strategies for Orot Rabin coastal infrastructure facility in the Israeli Mediterranean coast, in an attempt to assess how such facilities might promote marine conservation. The results revealed the effect of different management protocols on the ecosystem, and provide the maximum allowable catch for sustaining the biomass of vulnerable fish species in the area, which can be used in MSP to address specific marine conservation goals. The model led to counterintuitive understandings regarding the management of the area. It demonstrated that intensive development under specific management strategies may promote conservation goals better than some management strategies directed towards ecological and recreational purposes. This study confirms the potential usefulness of food-web models for MSP; it specifies the stages and means by which planners can use models. Furthermore, it is suggested that tool's development should be planning-oriented and should include more applications to serve planners who aim to promote ecosystem-based management and marine conservation goals.

Marine infrastructures are becoming ubiquitous as a result of the increasing exploitation of resources in the nearshore environment. These infrastructures are frequently linked with habitat degradation, pollution, and the establishment and spread of alien species. As marine infrastructures are perceived as threats to marine ecosystems, they are typically disregarded in conservation planning schemes. Here, the presence of invertebrates and fish was surveyed in infrastructure areas with prohibited public access, as well as in nearby rocky reef areas that had no infrastructure and were open to the public. It was found that species richness, Shannon diversity, and uniqueness of both fish and invertebrates were significantly higher in the infrastructure habitats than in the rocky reef habitats in most cases. Surprisingly, the findings show that the proportion of alien species was higher in the unprotected rocky reef habitats compared with that in the infrastructure habitats. These counterintuitive findings suggest that marine infrastructures that limit unauthorized access to the surrounding territory may contribute to conservation if they are acknowledged and managed, according to their potential to provide a habitat for marine species. This suggests that these areas should be considered by planners as opportunities to enhance the connectivity of populations and to supplement marine protected areas in heavily impacted marine environments.

Systematic conservation planning is a framework for optimally locating and prioritizing areas for conservation. An often-noted shortcoming of most conservation planning studies is that they do not address future uncertainty. The selection of protected areas that are intended to ensure the long-term persistence of biodiversity is often based on a snapshot of the current situation, ignoring processes such as climate change. Scenarios, in the sense of being accounts of plausible futures, can be utilized to identify conservation area portfolios that are robust to future uncertainty. We compared three approaches for utilizing scenarios in conservation area selection: considering a full set of scenarios (all-scenarios portfolio), assuming the realization of specific scenarios, and a reference strategy based on the current situation (current distributions portfolio). Our objective was to compare the robustness of these approaches in terms of their relative performance across future scenarios. We focused on breeding bird species in Israel’s Mediterranean region. We simulated urban development and vegetation dynamics scenarios 60 years into the future using DINAMICA-EGO, a cellular-automata simulation model. For each scenario, we mapped the target species’ available habitat distribution, identified conservation priority areas using the site-selection software MARXAN, and constructed conservation area portfolios using the three aforementioned strategies. We then assessed portfolio performance based on the number of species for which representation targets were met in each scenario. The all-scenarios portfolio consistently outperformed the other portfolios, and was more robust to ‘errors’ (e.g., when an assumed specific scenario did not occur). On average, the all-scenarios portfolio achieved representation targets for five additional species compared with the current distributions portfolio (approximately 33 versus 28 species). Our findings highlight the importance of considering a broad and meaningful set of scenarios, rather than relying on the current situation, the expected occurrence of specific scenarios, or the worst-case scenario.

The competitive exclusion principle is one of the most influential concepts in ecology. The classical formulation suggests a correlation between competitor species similarity and competition severity, leading to rapid competitive exclusion where species are very similar; yet neutral models show that identical species can persist in competition for long periods. Here, we resolve the conflict by examining two components of similarity – niche overlap and competitive similarity – and modeling the effects of each on exclusion rate (defined as the inverse of time to exclusion). Studying exclusion rate, rather than the traditional focus on binary outcomes (coexistence versus exclusion), allows us to examine classical niche and neutral perspectives using the same currency. High niche overlap speeds exclusion, but high similarity in competitive ability slows it. These predictions are confirmed by a well-known model of two species competing for two resources. Under ecologically plausible scenarios of correlation between these two factors, the strongest exclusion rates may be among moderately similar species, while very similar and highly dissimilar competitors have very low exclusion rates. Adding even small amounts of demographic stochasticity to the model blurs the line between deterministic and probabilistic coexistence still further. Thus, focusing on exclusion rate, instead of on the binary outcome of coexistence versus exclusion, allows a variety of outcomes to result from competitive interactions. This approach may help explain species coexistence in diverse competitive communities and raises novel issues for future work.

The food web of Lake Kinneret contains intraguild predation (IGP). Predatory invertebrates and planktivorous fish both feed on herbivorous zooplankton, while the planktivorous fish also feed on the predatory invertebrates. In this study, a complex mechanistic hydrodynamic-ecological model, coupled to a bioenergetics-based fish population model (DYCD-FISH), was employed with the aim of revealing IGP dynamics. The results indicate that the predation pressure of predatory zooplankton on herbivorous zooplankton varies widely, depending on the season. At the time of its annual peak, it is 10–20 times higher than the fish predation pressure. When the number of fish was significantly higher, as occurs in the lake after atypical meteorological years, the effect was a shift from a bottom-up controlled ecosystem, to the top-down control of planktivorous fish and a significant reduction of predatory and herbivorous zooplankton biomass. Yet, seasonally, the decrease in predatory-zooplankton biomass was followed by a decrease in their predation pressure on herbivorous zooplankton, leading to an increase of herbivorous zooplankton biomass to an extent similar to the base level. The analysis demonstrates the emergence of non-equilibrium IGP dynamics due to intra-annual and inter-annual changes in the physico-chemical characteristics of the lake, and suggests that IGP dynamics should be considered in food web models in order to more accurately capture mass transfer and trophic interactions.

Analysis of stability under linearized dynamics is central to ecology. We highlight two key limitations of the widely used traditional analysis. First, we note that while stability at fixed points is often the focus, ecological systems may spend less time near fixed points, and more time responding to stochastic environmental forcing by exhibiting wide zero-mean fluctuations about those states. If non-steady, uniquely precarious states along the nonlinear flow are analyzed instead of fixed points, transient growth is possible and indeed common for ecosystems with stable attractive fixed points. Second, we show that in either steady or non-steady states, eigenvalue based analysis can misleadingly suggest stability while eigenvector geometry arising from the non-self-Adjointness of the linearized operator can yield large finite-Time instabilities. We offer a simple alternative to eigenvalue based stability analysis that naturally and straightforwardly overcome these limitations.

Species distribution models (SDMs) are often limited by the use of coarse-resolution environmental variables and by the number of observations required for their calibration. This is particularly true in the case of elusive animals. Here, we developed a SDM by combining three elements: a database of explanatory variables, mapped at a fine resolution; a systematic sampling scheme; and an intensive survey of indirect observations. Using MaxEnt, we developed the SDM for the population of the Asiatic wild ass (Equus hemionus), a rare and elusive species, at three spatial scales: 10, 100, and 1000 m per pixel. We used indirect observations of feces mounds. We constructed 14 layers of explanatory variables, in five categories: water, topography, biotic conditions, climatic variables and anthropogenic variables. Woody vegetation cover and slopes were found to have the strongest effect on the distribution of wild ass and were included as the main predictors in the SDM. Model validation revealed that an intensive survey of feces mounds and high-resolution predictor layers resulted in a highly accurate and informative SDM. Fine-grain (10 and 100 m) SDMs can be utilized to: (1) characterize the variables influencing species distribution at high resolution and local scale, including anthropogenic effects and geomorphologic features; (2) detect potential population activity centers; (3) locate potential corridors of movement and possible isolated habitat patches. Such information may be useful for the conservation efforts of the Asiatic wild ass. This approach could be applied to other elusive species, particularly large mammals.

The spatial pattern of urban development has important ecological and conservation implications. Urban sprawl, characterized by scattered and low-density urban development, is commonly criticized for its negative ecological impact. In response, growth management policies have been proposed in order to promote compact development, which is generally considered more favorable from an ecological perspective. Spatial simulations of land cover change are useful for comparing urban development scenarios and their potential effects. One aspect that has not received much attention is how the rate of development may affect differences between compact development and urban sprawl in terms of their potential impact to biodiversity conservation at the landscape scale. Our goal in this study was to compare the spatial pattern and landscape-scale conservation and ecological implications of sprawling development (expected under unregulated development) versus compact development (promoted by growth management policies) at different development rates. We focused on Israel's Mediterranean region-a region characterized by high human population density and heterogeneous land cover. Using a cellular automata model, DINAMICA-EGO, we calibrated and validated an urban development model for the period between 1998 and 2007. Using this period as a reference, we simulated two scenarios 20 years into the future: unregulated (resulting in a more sprawling development pattern) versus regulated development (resulting in a more compact development pattern). For each scenario we analyzed a range of development rates, and compared built-up area patterns, and several landscape-level attributes of natural habitats, conservation priority areas, and protected areas. We found that at development rates comparable to those observed during 1998-2007, there was no major difference between the two scenarios. At higher development rates, some differences between the scenarios emerged: natural core areas were more fragmented and smaller in their extent, and a higher proportion of conservation priority areas were expected to undergo development in the unregulated scenario. Overall, the regulated scenario was more favorable for conservation. Since the regulated and unregulated scenarios exhibited only minor differences in lower development rates, modifications to policy measures included in the regulated scenario should be considered in order improve its effectiveness.

The recent availability of species occurrence data from numerous sources, standardized and connected within a single portal, has the potential to answer fundamental ecological questions. These aggregated big biodiversity databases are prone to numerous data errors and biases. The data-user is responsible for identifying these errors and assessing if the data are suitable for a given purpose. Complex technical skills are increasingly required for handling and cleaning biodiversity data, while biodiversity scientists possessing these skills are rare. Here, we estimate the effect of user-level data cleaning on species distribution model (SDM) performance. We implement several simple and easy-to-execute data cleaning procedures, and evaluate the change in SDM performance. Additionally, we examine if a certain group of species is more sensitive to the use of erroneous or unsuitable data. The cleaning procedures used in this research improved SDM performance significantly, across all scales and for all performance measures. The largest improvement in distribution models following data cleaning was for small mammals (1 g-100 g). Data cleaning at the user level is crucial when using aggregated occurrence data, and facilitating its implementation is a key factor in order to advance data-intensive biodiversity studies. Adopting a more comprehensive approach for incorporating data cleaning as part of data analysis, will not only improve the quality of biodiversity data, but will also impose a more appropriate usage of such data.

The dynamics of fluorescent organic matter (FOM) in lakes have generally been studied over extended periods of weeks to months rather than short time scales. We employed spectrofluorometry to examine whether short-term in situ variations in the three components of FOM (protein-like, terrestrial, and marine fluorescence) were related to changes in production in Lake Kinneret, Israel. Protein-like fluorescence correlated strongly with phytoplankton biomass, chlorophyll a, and Secchi depth in situ, suggesting it might serve as an indicator of aquatic production. In vitro dark incubations of filtered and unfiltered surface lake water (without added nutrients or organic matter) revealed considerable protein-like and marine fluorescence production in the unfiltered samples, suggesting that FOM is linked to particulate matter dynamics and to microbial processing of this suspended material. Terrestrial fluorescence did not significantly change in incubation, indicating that this component is probably produced deeper in the lake. Moreover, the lack of marine FOM accumulation in surface waters, despite producing in vitro, suggests that FOM photobleaching may regulate this component.

Global patters of species distributions and their underlying mechanisms are a major question in ecology, and the need for multi-scale analyses has been recognized. Previous studies recognized climate, topography, habitat heterogeneity and disturbance as important variables affecting such patterns. Here we report on analyses of species composition - environment relationships among different taxonomic groups in two continents, and the components of such relationships, in the contiguous USA and Australia. We used partial Canonical Correspondence Analysis of occurrence records of mammals and breeding birds from the Global Biodiversity Information Facility, to quantify relationships between species composition and environmental variables in remote geographic regions at multiple spatial scales, with extents ranging from 10⁵ to 10⁷ km² and sampling grids from 10 to 10,000 km². We evaluated the concept that two elements contribute to the impact of environmental variables on composition: the strength of species' affinity to an environmental variable, and the amount of variance in the variable. To disentangle these two elements, we analyzed correlations between resulting trends and the amount of variance contained in different environmental variables to isolate the mechanisms behind the observed relationships. We found that climate and land use-land cover are responsible for most explained variance in species composition, regardless of scale, taxonomic group and geographic region. However, the amount of variance in species composition attributed to land use / land cover (LULC) was closely related to the amount of intrinsic variability in LULC in the USA, but not in Australia, while the effect of climate on species composition was negatively correlated to the variability found in the climatic variables. The low variance in climate, compared to LULC, suggests that species in both taxonomic groups have strong affinity to climate, thus it has a strong effect on species distribution and community composition, while the opposite is true for LULC. We tested the effects of different environmental factors on the species composition of birds and mammals at multiple spatial scales, from local to continental, in two continents. We used a relatively novel approach for analyzing the relationship, breaking them down to components of ecological affinity and amount of variance. We found that climate has the strongest per unit effect on both birds and mammals at all scales, however, as variance in land use is high, LULC variables also have a string effect on species composition.

The continuum hypothesis has been proposed as a means to reconcile the contradiction between the niche and neutral theories. While past research has shown that species richness affects the location of communities along the niche-neutrality continuum, there may be extrinsic forces at play as well. We used a spatially explicit continuum model to quantify the effects of environmental heterogeneity, comprising abundance distribution and spatial configuration of resources, on the degree of community neutrality. We found that both components of heterogeneityaffect the degree of community neutrality and that species' dispersal characteristics affect the neutrality-heterogeneity relationship. Narrower resource abundance distributions decrease neutrality, while spatial configuration, which is manifested by spatial aggregation of resources, decreases neutrality at higher aggregation levels. In general, the degree of community neutrality was affected by complex interactions among spatial configuration of resources, their abundance distributions and the dispersal characteristics of species in the community. Our results highlight the important yet overlooked role of the environment in dictating the location of communities along the hypothesized niche-neutrality continuum.

Threats to biodiversity are often enhanced in human-dominated and densely-populated regions. The prospects for establishing new protected areas are generally more limited in such regions, due to competition with other land-uses. Improving the conservation value of agricultural lands has been proposed as a complementary strategy. Our goal was to compare alternatives for expanding an existing protected area system. We used the conservation planning software Marxan to select candidate sites for addition to an existing protected area system, based on the following three strategies: (1) focusing on remaining natural habitats; (2) prioritizing agricultural lands for wildlife-friendly farming and agri-environmental measures that can improve conservation value; and (3) a strategy combining the former two. We used area as a surrogate for cost with the aim of minimizing the total area needed to meet our conservation objectives. We evaluated the sites found via each strategy with respect to their landscape structure and the coverage they provided to the target species' habitats. We focused on breeding bird species in Israel's Mediterranean region, a challenging and relevant case study due to the area's high level of urbanization, population density, and its heterogeneous landscape. We found that the existing protected areas provided adequate coverage to only 23% of the target species, clearly demonstrating the need for action. Of the three strategies, expanding the existing protected area system based on the combined strategy was the most beneficial since it provided greater coverage to the target species' habitats, and resulted in a larger, more compact, and less patchy conservation area network. In addition to protected area planning, our approach can be used to target agricultural lands for agri-environmental schemes, particularly in human-dominated and densely-populated regions.

Species distribution models (SDMs) that employ climatic variables are widely used to predict potential distribution of invasive species. However, climatic variables derived from climate datasets do not account for anthropogenic influences on microclimate. Irrigation is a major anthropogenic activity that influences microclimate conditions and alters the distribution of species in anthropogenic landuses. SDM-based studies appear to ignore the effects of irrigation on microclimatic conditions. This study incorporated irrigation as a correction to precipitation data, to improve the predictive capacity of SDM. As a case study, we examined a SDM of Wasmannia auropunctata, an invasive species that originates in South and Central America, which has invaded tropical and subtropical regions around the world. The potential distribution of W. auropunctata was predicted using Maxent. The model was built based on climatic variables and species records from non-irrigated sites in the native range and then projected on a global scale. Invasive species records were used to evaluate the performance of the model. Precipitation-related variables were modified to approximate actual water input in irrigated areas. Precipitation correction relied on an estimate of irrigation inputs. The model with irrigation correction performed better than the corresponding model without correction, on a global scale and when it was examined in five different geographical regions of the model. These results demonstrate the importance of irrigation correction for assessing the distribution of W. auropunctata in various geographical regions. Accounting for irrigation is expected to improve SDMs for a variety of species.

Movement of animals is a key process affecting population dynamics. Information on factors that affect pathway use is essential for identifying and protecting pathways, and important for maintaining connectivity among populations. We present an innovative, non-invasive, approach for predicting pathways of reintroduced Asiatic wild ass (Equus hemionus) in Israel, which is based on understanding the effects of landscape factors on pathways use. The approach includes: Predicting pathways, by employing a least cost pathway (LCP) GIS models based on several landscape factors, so as to efficiently direct a field survey and explore the wild ass's general preferences of pathway types; Collecting empirical data by surveying the dung density of wild ass along each of the predicted pathways and using the data as an index of pathway use; Evaluating the predicted pathways against the empirical data collected, to estimate the general pathway preferences of the wild ass; and Developing and evaluating alternative generalized linear models, according to a priori hypotheses based on empirical data so as to quantify the effect of different landscape factors on pathway use. The analyses were conducted for the entire landscape, and then for two distinct landscape types, open landscape and landscape-barriers (mountain ridges), as subsets of the entire landscape. There were clear differences in the mean number of faeces counts between the LCPs, indicating that the wild ass prefers certain pathway types as a function of landscape features. We further found that the factors affecting E. hemionus pathway usage-vegetation; slopes; canyons; and 4-wheel drive routes-varied largely between the two major landscape types studied, demonstrating the importance of studying space use patterns at different landscape terrains. This information can be applicable to landscape planning measures that aim to enhance protection of the species. This approach provides a framework for studying animal space-use patterns of a variety of species, including elusive species, in a heterogeneous landscape.

It is thought that the science of ecology has experienced conceptual shifts in recent decades, chiefly from viewing nature as static and balanced to a conception of constantly changing, unpredictable, complex ecosystems. Here, we ask if these changes are reflected in actual ecological research over the last 30 years. We surveyed 750 articles from the entire pool of ecological literature and 750 articles from eight leading journals. Each article was characterized according to its type, ecological domain, and applicability, and major topics. We found that, in contrast to its common image, ecology is still mostly a study of single species (70% of the studies); while ecosystem and community studies together comprise only a quarter of ecological research. Ecological science is somewhat conservative in its topics of research (about a third of all topics changed significantly through time), as well as in its basic methodologies and approaches. However, the growing proportion of problem-solving studies (from 9% in the 1980s to 20% in the 2000 s) may represent a major transition in ecological science in the long run.

AimsStudies of species distribution patterns traditionally have been conducted at a single scale, often overlooking species-environment relationships operating at finer or coarser scales. Testing diversity-related hypotheses at multiple scales requires a robust sampling design that is nested across scales. Our chief motivation in this study was to quantify the contributions of different predictors of herbaceous species richness at a range of local scales.MethodsHere, we develop a hierarchically nested sampling design that is balanced across scales, in order to study the role of several environmental factors in determining herbaceous species distribution at various scales simultaneously. We focus on the impact of woody vegetation, a relatively unexplored factor, as well as that of soil and topography. Light detection and ranging (LiDAR) imaging enabled precise characterization of the 3D structure of the woody vegetation, while acoustic spectrophotometry allowed a particularly high-resolution mapping of soil CaCO₃ and organic matter contents.Important FindingsWe found that woody vegetation was the dominant explanatory variable at all three scales (10, 100 and 1000 m²), accounting for more than 60% of the total explained variance. In addition, we found that the species richness-environment relationship was scale dependent. Many studies that explicitly address the issue of scale do so by comparing local and regional scales. Our results show that efforts to conserve plant communities should take into account scale dependence when analyzing species richness-environment relationships, even at much finer resolutions than local vs. regional. In addition, conserving heterogeneity in woody vegetation structure at multiple scales is a key to conserving diverse herbaceous communities.

Fundamental ecological research is both intrinsically interesting and provides the basic knowledge required to answer applied questions of importance to the management of the natural world. The 100th anniversary of the British Ecological Society in 2013 is an opportune moment to reflect on the current status of ecology as a science and look forward to high-light priorities for future work. To do this, we identified 100 important questions of fundamental importance in pure ecology. We elicited questions from ecologists working across a wide range of systems and disciplines. The 754 questions submitted (listed in the online appendix) from 388 participants were narrowed down to the final 100 through a process of discussion, rewording and repeated rounds of voting. This was done during a two-day workshop and thereafter. The questions reflect many of the important current conceptual and technical pre-occupations of ecology. For example, many questions concerned the dynamics of environmental change and complex ecosystem interactions, as well as the interaction between ecology and evolution. The questions reveal a dynamic science with novel subfields emerging. For example, a group of questions was dedicated to disease and micro-organisms and another on human impacts and global change reflecting the emergence of new subdisciplines that would not have been foreseen a few decades ago. The list also contained a number of questions that have perplexed ecologists for decades and are still seen as crucial to answer, such as the link between population dynamics and life-history evolution. Synthesis. These 100 questions identified reflect the state of ecology today. Using them as an agenda for further research would lead to a substantial enhancement in understanding of the discipline, with practical relevance for the conservation of biodiversity and ecosystem function.

Ecology seeks to study the factors affecting the distribution of species. In terms of their effects on species richness and composition, woody plants can be considered as dominant factors which extensively affect their environment, changing the distribution of resources in space and time. In structurally rich and diverse plant communities, such as Mediterranean ecosystems, the specific species that form the woody patch can be important. The specific woody species patch type may impose a set of abiotic conditions, which in turn would result in specific traits of the given herbaceous community under that patch. Evidence revealed in this study suggests that the general notion of a two-patch type (woody and non-woody) system for describing ecosystems is simplistic, and may be misleading. We found that patterns of herbaceous species richness and composition are related to specific woody species patch type. We found that herbaceous species richness under each patch type coincided with respective differences in solar radiation reaching the sub-canopy. Accounting for specific patch characteristics may largely enhance our understanding of plant community structure.

In Mediterranean regions, the combination of disturbances, life histories, plant regeneration traits, and microhabitat variability form highly heterogeneous vegetation mosaics which shift in space and time. Consequently, structure-based forest management is emerging as a superior alternative to management of vegetation formations in such areas. Delineation of management units in these areas is often based on manual interpretation of aerial imagery coupled with field surveys. Here, we propose an alternative approach that is based on segmentation of remotely sensed height and cover maps derived from light detection and ranging (LiDAR) imagery. A large suite of alternative segmentation maps was generated using multiresolution segmentation (MS) with different parameters, and an area-fit approach used to select the map that most successfully captured a reference set of structural units delineated manually. We assessed the feasibility of this approach in a nature reserve in northern Israel, compared the resulting map with a traditional vegetation formations map, and explored the performance of the segmentation algorithm under various parameter combinations. Pronounced differences between the structure and formation maps highlight the suitability of this approach as an alternative to the existing methods of delineating vegetation units in Mediterranean systems, and possibly in other systems as well.

The impact of herbivores on herbaceous plant communities is usually attributed to direct consumption of plants. We hypothesized that goats affect herbaceous plants both directly (consumption by foraging) and indirectly, by changing environmental conditions through modification of woody plant structure. We assessed the effects of goats browsing on environmental conditions, landscape structure, and herbaceous plants to link the direct and indirect effects of goats on herbaceous communities. Our model system was the Mediterranean woodland in Mt. Carmel, Israel. This is a two-phase mosaic landscape, composed of herbaceous (open) and woody patches. We delineated 10 plots of 1000 m², goats were introduced to five plots and five plots remained without goats. We monitored plant species richness and composition in two adjacent patch types (woody and open) in each plot. For each patch type, in all plots, we collected data on environmental conditions. We analyzed landscape structure using landscape metrics derived from a high-resolution vegetation map. We found that goats modified the structure of woody plants and hence the landscape mosaic. This alteration was associated with changes in environmental conditions, with more light penetration and higher temperatures. The impact of goats on the herbaceous plant community depended on patch type. In open patches, goats affected the herbaceous community mostly by direct consumption, whereas in woody patches they affected the herbaceous community mainly by modification of abiotic conditions. Our results stress the importance of considering landscape and patch structure in analyzing the effect of herbivory on plant communities.

The recent devastating wildfire on Mt. Carmel provided a unique opportunity to evaluate a fire-risk map constructed for the region, published two years ago in this journal. This largest forest fire in the history of Israel, occurred during December 2010, covering 2180. ha, burning more than half-million trees and causing the loss of life of 45 people.A study of fire risk in this area was conducted between 2007 and 2009 utilizing a combination of Monte Carlo simulation of spatial spread of fire ignition with fire behavior model (FARSITE). The fire risk map produced in 2009 is assessed here with reference to the area burnt during December 2010. The results showed that most of burnt areas corresponded to high risk levels in the risk map. According to a null model, the five lower risk levels taken together would have corresponded to 50% of the burnt area, while in fact they were presented in only 5.6% of the area. In contrast, the three highest risk levels, for which the null model expectation would be a representation of 30%, were represented in 87% of the area. Comparing the fire risk map against the map of the real recent fire provided support to the general approach, and strengthened the confidence of our fire risk model.

Relationships between species composition and its environmental determinants are a basic objective of ecology. Such relationships are scale dependent, and predictors of species composition typically include variables such as climate, topographic, historical legacies, land uses, human population levels, and random processes. Our objective was to quantify the effect of environmental determinants on U.S. mammal composition at various spatial scales. We found that climate was the predominant factor affecting species composition, and its relative impact increased in correlation with the increase of the spatial scale. Another factor affecting species composition is land-use-land-cover. Our findings showed that its impact decreased as the spatial scale increased. We provide quantitative indication of highly significant effect of climate and land-use-land-cover variables on mammal composition at multiple scales.

Conservation planning requires knowledge of the distribution of all species in the area of interest. Surrogates for biodiversity are considered as a possible solution. The two major types are biological and environmental surrogates. Here, we evaluate four different methods of hierarchical clustering, as well as one non-hierarchical method, in the context of producing surrogates for biodiversity. Each clustering method was used to produce maps of both surrogate types. We evaluated the representativeness of each clustering method by finding the average number of species represented in a set of sites, one site of each domain, which was carried out with Monte-Carlo permutations procedure. We propose an additional measure of surrogate performance, which is the degree of evenness of the different domains, e.g., by calculating Simpson's diversity index. Surrogates with low evenness leave little flexibility in site selection since often some of the domains may be represented by a single or very few sites, and thus surrogate maps with a high Simpson's index value may be more relevant for actual decision making. We found that there is a trade-off between species representativeness and evenness. Centroid clustering represented the most species, but had very low values of evenness. Ward's method of minimum variance represented more species than a random choice, and had high evenness values. Using the typical evaluation measures, the Centroid clustering method was most efficient for surrogate production. However, when Simpson's index is also considered, Ward's method of minimum variance is more appropriate for managers.

Aim Studying relationships between species and their physical environment requires species distribution data, ideally based on presence-absence (P-A) data derived from surveys. Such data are limited in their spatial extent. Presence-only (P-O) data are considered inappropriate for such analyses. Our aim was to evaluate whether such data may be used when considering a multitude of species over a large spatial extent, in order to analyse the relationships between environmental factors and species composition. Location The study was conducted in virtual space. However, geographic origin of the data used is the contiguous USA. Methods We created distribution maps for 50 virtual species based on actual environmental conditions in the study. Sampling locations were based on true observations from the Global Biodiversity Information Facility. We produced P-A data by selecting ∼1000 random locations and recorded the presence/absence of all species. We produced two P-O data sets. Full P-O set was produced by sampling the species in locations of true occurrences of species. Partial P-O was a subset of full P-O data set matching the size of the P-A data set. For each data set, we recorded the environmental variables at the same locations. We used CCA to evaluate the amount of variance in species composition explained by each variable. We evaluated the bias in the data set by calculating the deviation of average values of the environmental variables in sampled locations compared to the entire area. Results P-A and P-O data sets were similar in terms of the amount of variance explained by the different environmental variables. We found sizable environmental and spatial bias in the P-O data set, compared to the entire study area. Main conclusions Our results suggest that although P-O data from collections contain bias, the multitude of species, and thus the relatively large amount of information in the data, allow the use of P-O data for analysing environmental determinants of species composition.

A strategy for global sensitivity analysis of a multi-parameter ecological model was developed and used for the hydrodynamic-ecological model (DYRESM-CAEDYM, DYnamic REservoir Simulation Model-Computational Aquatic Ecosystem Dynamics Model) applied to Lake Kinneret (Israel). Two different methods of sensitivity analysis, RPART (Recursive Partitioning And Regression Trees) and GLM (General Linear Model) were applied in order to screen a subset of significant parameters. All the parameters which were found significant by at least one of these methods were entered as input to a GBM (Generalized Boosted Modeling) analysis in order to provide a quantitative measure of the sensitivity of the model variables to these parameters. Although the GBM is a general and powerful machine learning algorithm, it has substantial computational costs in both storage requirements and CPU time. Employing the screening stage reduces this cost. The results of the analysis highlighted the role of particulate organic material in the lake ecosystem and its impact on the over all lake nutrient budget. The GBM analysis established, for example, that parameters such as particulate organic material diameter and density were particularly important to the model outcomes. The results were further explored by lumping together output variables that are associated with sub-components of the ecosystem. The variable lumping approach suggested that the phytoplankton group is most sensitive to parameters associated with the dominant phytoplankton group, dinoflagellates, and with nanoplankton (Chlorophyta), supporting the view of Lake Kinneret as a bottom-up system. The study demonstrates the effectiveness of such procedures for extracting useful information for model calibration and guiding further data collection.

The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM₁₀, emitted both by industry and traffic, and to SO₂, a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin's lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO₂-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM₁₀, with the relative risk associated with an increase of 1μg/m³ of PM₁₀ being 12%. Special consideration of wards with expected rates <1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.

Due to the challenges faced by resource managers in maintaining post-fire ecosystem health, there is a need for methods to assess the ecological consequences of disturbances. This research examines an approach for assessing changes in post-fire vegetation dynamics for sites in Spain, Israel and the USA that burned in 1998, 1999 and 2002 respectively. Moderate Resolution Imaging Spectroradiometer satellite Normalized Difference Vegetation Index (NDVI) time-series data (200007) are used for all sites to characterise and track the seasonal and spatial changes in vegetation response. Post-fire trends and metrics for burned areas are evaluated and compared with unburned reference sites to account for the influence of local environmental conditions. Time-series data interpretation provides insights into climatic influences on the post-fire vegetation. Although only two sites show increases in post-fire vegetation, all sites show declines in heterogeneity across the site. The evaluation of land surface phenological metrics, including the start and end of the season, the base and peak NDVI, and the integrated seasonal NDVI, show promising results, indicating trends in some measures of post-fire phenology. Results indicate that this monitoring approach, based on readily available satellite-based time-series vegetation data, provides a valuable tool for assessing post-fire vegetation response.

The eastern Mediterranean region has been subjected to intensive human disturbance in the past 10,000 years, mainly in the forms of agro-pastoral activities such as grazing, shrub clearing, and prescribed burning. This disturbance history resulted in the formation of highly heterogeneous landscapes, characterized by high biodiversity. Recent changes in human activities have resulted in a decrease of landscape heterogeneity, leading to decreasing biodiversity and increasing fire risk. To conserve heterogeneity, land managers apply disturbance based management practices, using the same activities that created and maintained landscape heterogeneity in the past. However, the long-term and large-scale outcomes of these disturbances are often unknown, due to the complex response of Mediterranean vegetation to disturbance. Here we report on a spatially explicit, hybrid, and spatially hierarchical ecological model developed by us. The model attempts to predict the outcome of various disturbance based management activities on the long-term spatio-temporal dynamics of five common Mediterranean vegetation types. The model uses a spatially explicit state and transition formulation, with continuous transition functions. Model simulations were conducted on a Mediterranean landscape in Northern Israel, incorporating various disturbance practices that are common in the region. Simulation results highlight the potential of disturbance based management as a tool for conserving landscape heterogeneity, as well as the complex interactions between disturbances and the spatial structure of the landscape in Mediterranean regions.

We focus on a class of multicriteria methods that are commonly used in environmental decision making - those that employ the weighted linear average algorithm (and this includes the popular analytic hierarchy process (AHP)). While we do not doubt the potential benefits of using formal decision methods of this type, we draw attention to the consequences of not using them well. In particular, we highlight a property of these methods that should not be overlooked when they are applied in environmental and wider decision-making contexts: the final decision or ranking of options is dependent on the choice of performance scoring scales for the criteria when the criteria weights are held constant. We compare this "sensitivity" to a well-known criticism of the AHP, and we go on to describe the more general lesson when it comes to using weighted linear average methods - a lesson concerning the relationship between criteria weights and performance scoring scales.

The distribution of threatened species often serves to drive conservation decisions. Much of the distribution of many threatened species is already protected. These species may need fostering, but not necessarily further protected areas. We propose a simple and generic means of assessing the degree of protection presently offered to a threatened species, namely, the proportion of its distribution that is unprotected. This index classifies threatened species into two classes: most of their distribution range is either (1) inside protected areas (protected), or (2) outside of protected areas (unprotected). We propose that evaluation of land for planning and conservation should be based chiefly on the distribution of those threatened species that are not yet protected by the current reserve network. Our approach is exemplified in a case study of vertebrate species in Israel. We constructed a "hotspots map" using only the threatened species that have more than 60% of their distribution unprotected (n = 57), and compared it to a similar map with all threatened vertebrates (n = 118). This latter map had all hotspots around the Rift Valley in the eastern part of the country, while the former map had some hotspots in the western parts of the country as well. This study highlights the importance of a clear decision regarding which species should be used in prioritizing areas for conservation.

Understanding the spatial pattern of fire is essential for Mediterranean vegetation management. Fire-risk maps are typically constructed at coarse resolutions using vegetation maps with limited capacity for prescribing prevention activities. This paper describes and evaluates a novel approach for fire risk assessment that may produce a decision support system for actual fire management at fine scales. FARSITE, a two-dimensional fire growth and behavior model was activated, using ArcView VBA code, to generate Monte Carlo simulations of fire spread. The study area was 300 km² of Mt. Carmel, Israel. FARSITE fuel models were adjusted for Mediterranean conditions. The simulation session consisted of 500 runs. For each simulation run, a calendar date, fire length, ignition location, climatic data and other parameters were selected randomly from known distributions of these parameters. Distance from road served as a proxy for the probability of ignition. The resulting 500 maps of fire distribution (the entire area burnt in a specific fire) were overlaid to produce a map of 'hotspots' and 'cold spots' of fire frequency. The results revealed a clear pattern of fires, with high frequency areas concentrated in the northwestern part. The spatial pattern of the fire frequency map bears partial resemblance to the fuel map, but seems to be affected by several other factors as well, including the location of urban areas, microclimate, topography and the distribution of ignition locations (which is affected by road pattern). These results demonstrate the complexities of fire behavior, showing a very clear pattern of risk level even at fine scales, where neighboring areas have different risk levels due to combinations of vegetation cover, topography, microclimate and other factors. Comparing the distribution of historic fires in the region against the map of simulated fire frequency indicated that most fires tended to coincide with higher risk levels. This fact supports the hypothesis that simulated fire frequency may serve as a reliable surrogate for fire risk. Thus, Monte Carlo simulations of a fire spread model may produce high-resolution fire-risk maps that could be used for long-term strategic planning of fire prevention activities.

The output of stochastic models is a distribution of values, rather than a single value such as in deterministic models. Local sensitivity analyses of such models typically ignore the higher moments of the output distribution and instead use the distribution mean to represent model output. This might be simplistic, since the shape of the distribution might also be sensitive to changes in model parameters. Here, we construct a simple sensitivity index that captures also the shape of the output distribution, by incorporating its variance in addition to its mean. To evaluate its performance, we reconstructed an existing stochastic individual-based model for mosquitofish (Gambusia holbrooki) population. We compared the performance of the new sensitivity index to the standard sensitivity index (∂Y/∂P) that was calculated using the mean of the output distribution, by ranking model parameters according to their impact on the output. Sensitivity analyses using both methods identified different parameters as the most influential on model output, and rankings were inconsistent between methods regardless of the number of simulations used for generating the output distributions. It is shown that the new index indeed captured better the effect of parameters on model output since it accounted for the variance of the output distribution.

Disturbances such as grazing, invading species, and clear-cutting, often act at small spatial scales, and means for quantifying their impact on fine scale vegetation patterns are generally lacking. Here we adopt a set of landscape metrics, commonly used for quantifying coarse scale fragmentation, to quantify fine scale fragmentation, namely the fine scale vegetation structure. At this scale, patches often consist of individual plants smaller than 1 m ², requiring the grain of the analysis to be much smaller. We used balloon aerial photographs to map fine details of Mediterranean vegetation (pixel size <0.04 m) in experimental plots subjected to grazing and clear-cutting and in undisturbed plots. Landscape metrics are sensitive to scale. Therefore, we aggregated the vegetation map into four coarser scales, up to a resolution of 1 m, and analyzed the effect of scale on the metrics and their ability to distinguish between different disturbances. At the finest scale, six of the seven landscape metrics we evaluated revealed significant differences between treated and undisturbed plots. Four metrics revealed differences between grazed and control plots, and six metrics revealed differences between cleared and control plots. The majority of metrics exhibited scaling relations. Aggregation had mixed effects on the differences between metric values for different disturbances. The control plots were the most sensitive to scale, followed by grazing and clearing. We conclude that landscape metrics are useful for quantifying the very fine scale impact of disturbance on woody vegetation, assuming that the analysis is based on sufficiently high spatial resolution data.

Low recruitment of new plants as currently observed in several California oak species might partly result from insufficient storage of vital resources (reduced ability to regrow after disturbance) or from low adaptation to environmental stress, such as drought. We conducted two studies under divergent environmental conditions to compare seedlings of California oaks with ecologically equivalent species from the eastern Mediterranean region, where recruitment has been substantial over the past 25-30 years. Resource concentrations (nonstructural carbohydrates and nitrogen) in taproots and the ratio of root resource content to shoot biomass did not provide evidence that Californian oak seedlings have smaller reserves for resprouting than eastern Mediterranean species in these studies. However, we observed lower seedling survival among California species, when compared with Mediterranean species, under conditions of higher drought stress. In the California deciduous Quercus douglasii Hook. & Arn., water-use efficiency as measured by leaf gas exchange and leaf δ¹³C was lower compared with its Mediterranean equivalent Quercus ithaburensis Decne. In addition, stomatal conductance in the California evergreen Quercus agrifolia Née appeared to be less sensitive to vapor pressure deficit than in its Mediterranean equivalent Quercus calliprinos Webb. Our results suggest lower adaptation to drought in the California species tested here as compared with Mediterranean species, which will negatively impact recruitment of oaks from California under the currently drier soil conditions.

We estimate the relation between binary responses and corresponding covariate vectors, both observed over a large spatial lattice. We assume a hierarchical generalized linear model with probit link function, partition the lattice into blocks, and adopt the working assumption of independence between the blocks to obtain an easily solved estimating equation. Standard errors are obtained using the "sandwich" estimator together with window subsampling (Sherman, 1996, Journal of the Royal Statistical Society, Series B 58, 509-523). We apply this to a large data set describing long-term vegetation growth, together with two other approximate-likelihood approaches: pairwise composite likelihood (CL) and estimation under a working assumption of independence. The independence and CL methods give similar point estimates and standard errors, while the independent-block approach gives considerably smaller standard errors, as well as more easily interpretable point estimates. We present numerical evidence suggesting this increased efficiency may hold more generally.

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence-absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data-erroneous species presence-absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

Studies of forest biomass dynamics typically use long-term forest inventory data, available in only a few places around the world. We present a method that uses photogrammetric measurements from aerial photographs as an alternative to time-series field measurements. We used photogrammetric methods to measure tree height and crown diameter, using four aerial photographs of Yatir Forest, a semi-arid forest in southern Israel, taken between 1978 and 2003. Height and crown-diameter measurements were transformed to biomass using an allometric equation generated from 28 harvested Aleppo pine (Pinus halepensis Mill.) trees. Mean tree biomass increased from 6.37 kg in 1978 to 97.01 kg in 2003. Mean plot biomass in 2003 was 2.48 kg/m² and aboveground primary productivity over the study period ranged between 0.14 and 0.21 kg/m² per year. There was systematic overestimation of tree height and systematic underestimation of crown diameter, which was corrected for at all time points between 1978 and 2003. The estimated biomass was significantly related to field-measured biomass, with an R² value of 0.78. This method may serve as an alternative to field sampling for studies of forest biomass dynamics, assuming that there is sufficient spatial and temporal coverage of the investigated area using high-quality aerial photography, and that the tree tops are distinguishable in the photographs.

Recent evidence suggests that fragmentation effects on pollinators may drive plant species to extinction even if their immediate habitats are not affected. We hypothesize that species at high risk of suffering this "pollination crisis" are characterized by the following set of traits: self-incompatibility with synchronous flowering; reliance on a small number of pollinator species whose pollinators are specialists; and the existence of only a few, sparse populations. Iris bismarckiana fits this description perfectly. We studied two populations separated by recent urban development in order to (a) determine if the species is suffering a "pollination crisis", (b) assess which stage of the process limits reproductive success, and (c) evaluate artificial pollination as a viable conservation means for this species. We performed pollination experiments in two populations of I. bismarckiana. The overall natural fruit-set of open-pollinated inflorescences was around 13%, while supplementary cross-pollination by pollen from the same population substantially increased the fruit-set to 44%. An even greater fruit-set of 78% resulted from cross-pollination with pollen from the second population. Artificial cross-pollination also increased the number of seeds per capsule. Our results suggest that fragmentation may link the effects of inbreeding depression and pollinator activity to generate a "pollination crisis", expressed as significant reduction in sexual reproduction. We propose that supplementary cross-pollination, as well as transplanting of seeds, seedlings, and adult plants, may increase seed production and improve the survival chances of I. bismarckiana.

This paper summarizes our efforts to investigate the nature, behavior, and implications of positional error and attribute error in spatiotemporal datasets. Estimating the combined influence of these errors on map analysis has been hindered by the fact that these two error types are traditionally expressed in different units (distance units, and categorical units, respectively. We devised a conceptual model that enables the translation of positional error into terms of thematic error, allowing a simultaneous assessment of the impact of positional error on thematic error -a property that is particularly useful in the case of change detection. Linear algebra-based error model combines these two error types into a single measure of overall thematic accuracy. This model was tested in a series of simulations using artificial maps, in which complex error patterns and interactions between the two error types, were introduced. The model accommodated most of these complexities, but interaction between the two error types was found to violate model assumptions, and reduced its performance. A systematic study of the spatiotemporal structure of error in actual datasets was thus conducted. Only weak and insignificant interactions were found between the two error types. Application of this error model to real-world time series data indicated that such data are much less accurate than is typically thought. These results highlight the importance of reducing positional error. The second part of our paper presents an analysis of how to reduce the impacts of positional error through aggregation (i.e., increasing the observation grain). Aggregation involves information loss, and thus, the choice of a proper cell size for aggregation is important. A model was developed to quantify the decay in impact of positional error, with increasing cell size. Applying the model to actual data sets, a major reduction in effective positional error was found for cell sizes ranging between 3-10 times the average positional error (RMSE). The model may help users in deciding on an optimal aggregation level given the tradeoff between information loss and accuracy gains.

A recent debate concerns the relative merit of the two major types of surrogates for biodiversity, biological surrogates and environmental surrogates. Evidence, in the form of direct comparison of these two surrogate types, is scarce. We conducted a direct comparison of the performance of a series of biological and environmental surrogates, at a local scale (300 km²), which is often the relevant scale for land planning and management. Performance was referred to as the degree of surrogate congruence with a spatial pattern of diversity of woody species, of geophytes, and of land snails. "Environmental domains", surrogates based on numerical classification of environmental variables (topography, soil, and vegetation cover), outperformed other environmental surrogates (qualitatively delineated vegetation units and physiographic land types). The environmental domains surrogates were robust to subjective decisions on a number of classes and on input variables that drove the classification. The best biological surrogate was the woody species diversity pattern, with performance similar to that of the environmental domains. Our results support the notion that environmental domains may be reliable and cost-effective surrogates for biodiversity at small scales, particularly in data-poor regions.

In this note we compare two mathematical models of foraging that reflect two competing theories of animal behavior: optimizing and robust satisficing. The optimal-foraging model is based on the marginal value theorem (MVT). The robust-satisficing model developed here is an application of info-gap decision theory. The info-gap robust-satisficing model relates to the same circumstances described by the MVT. We show how these two alternatives translate into specific predictions that at some points are quite disparate. We test these alternative predictions against available data collected in numerous field studies with a large number of species from diverse taxonomic groups. We show that a large majority of studies appear to support the robust-satisficing model and reject the optimal-foraging model.

Spatial mapping of variables that vary in space and time is a common procedure in many research fields. Very often it is of interest to map the time-average or time-integration of the variable over the whole period of interest. Normally, such a map is produced by spatially interpolating the whole period averages of the observed data. An alternative option is to first spatially interpolate narrow time slice averages of the variable and then sum the resultant maps. This paper discusses the latter option, and the accuracy of the spatio-temporal variable interpolation as a function of the width of the time-averaging window. Theoretically, using a linear and data-value independent operator to interpolate a complete data set (i.e. without missing data), the accuracy is independent of the width of the time-averaging window. However, using a nonlinear or a data-value dependent interpolation operator, and/or in the presence of missing data, the accuracy of the interpolation can vary with the averaging window width. The concept is demonstrated using a set of half-hourly SO2 concentrations measured at 20 monitoring stations in Haifa Bay area, Israel, during the years 1996-2002. Cross-validated interpolation accuracy measures calculated for this data set vary significantly with the time-averaging window width, showing a clear minimum at daily averaging. The results and their general implications for the interpolation of spatio-temporal variables are discussed.

"Energy Tower" is a technology for producing renewable and clean electricity by means of cooling hot and dry air, which is continuously supplied to arid lands. We assess the potential of an Energy Tower by incorporating topographic and meteorological parameters into a computational model, providing evaluations for the net power production and the electricity production cost. We formulate a highly simplified model for the Energy Tower's flow, setup and process a spatial dataset of topographic and Meteorological upper air parameters. The model was applied to the Australian continent. A model simulation of one annual cycle enabled the ranking and selection of promising sites. The highest potential for energy towers is in the Port Hedland region, where favorable meteorological and topographic conditions would result in high average net power (≈370 ± 160 MW), potentially providing the electricity needs of ∼0.5 million people, for an economically competitive costs (3.5 ¢kWh).

We suggest an empirical method to correct topographic effects on vegetation classification of panchromatic aerial photographs. The method is based on the use of spatial interpolation technique that constructs a luminance surface from targets of high brightness values. The luminance surface is then used to correct the topographic effects differentially, by increasing brightness values in shaded areas and decreasing brightness values of lightened areas. For this purpose, the use of a trapezoidal function was found successful in the reduction of standard deviation of brightness values of trees, shrubs, and herbaceous plants after empirical correction. This method outperformed a frequently used digital elevation model-based topographic correction in terms of overall classification accuracy of the resulting images.

The CLC (Combined Location Classification) error model provides indices for overall data uncertainty in thematic spatio-temporal datasets. It accounts for the two major sources of error in such datasets, location error and classification error. The model assumes independence between error components, while recent studies revealed various degrees of correlation between error components in actual datasets. The goal of this study is to determine if the likely violation of model assumptions biases model predictions. A comprehensive algorithm was devised to simulate the entire process of error formation and propagation. Time series thematic maps were constructed, and modified maps were derived as realizations of underlying error patterns. Error rate and pattern (positive autocorrelation) were controlled for location error and for classification error. The magnitude of correlation between errors from different sources and correlation between error at different time steps was also controlled. A very good agreement between model predictions and simulation results was found in the absence of correlation in error between time steps and between error types, while the inclusion of such correlations was shown to affect model fit slightly. Given our current knowledge of spatio-temporal error patterns in real data, the CLC error model can be used reliably to assess the overall uncertainty in thematic change detection analyses.

A long line of inquiry on the notion of ecological convergence has compared ecosystem structure and function between areas that are evolutionarily unrelated but under the same climate regime. Much of this literature has focused on quantifying the degree to which animal morphology or plant physiognomy is alike between disjunct areas. An important property of ecosystems is their behavior following disturbance. Yet, this aspect of ecosystems has not been investigated in a comparative study of convergence. If different ecosystems are under similar environmental controls, then one would predict that the rates and patterns of response to disturbance would also be similar. The objective of this study is to compare landscape dynamics following disturbance using spatiotemporal models to quantify vegetation change in Mediterranean ecosystems found in California and Israel. We model the process of tree and shrub regeneration at the landscape scale in two similar study sites in Israel (Mount Meron) and California (Hasting Nature Reserve). During the periods studied (1964-1992 for Israel and 1971-1995 for California), average annual change in tree cover was 5 times larger in Israel than in California. Based on multiple regression models, differences were found in the relative importance of specific variables predicting vegetation change. In Hastings (California), initial tree cover accounted for most of the explained variability in 1995 tree cover (partial R ² = 0.71), while in Meron (Israel), grazing type and intensity, topography indices, and initial vegetation each accounted for about a third of the explained variability. These findings support the notion that traits such as regeneration pattern and rate, both at the individual level and at the landscape level, were largely affected by the human land use history of the region.

Aggregation may be used as a means of enhancing remotely sensed data accuracy, but there is a tradeoff between loss of information and gain in accuracy. Thus, the choice of the proper cell size for aggregation is important. This letter explores the change in data accuracy that accompanies aggregation and finds an increase in image thematic accuracy with increasing cell size, resulting from 1) reduction in the impact of misregistration on thematic error and 2) mutual cancelation of inverse classification errors occurring within the same cell. A model is developed to quantify these phenomena. The model is exemplified using a vegetation map derived from an aerial photo. The model revealed a major reduction in effective location error for cell sizes in the range of 3-10 times the size of mean location error; reduction in effective classification error was minor.

The goals of this letter are to study the spatial patterns of location error and of classification error in spatio-temporal datasets, assess the role of environmental factors in determining error rate and pattern, and test for possible correlation within and between these error types in space and time. A multiple regression was used to determine the effects of local environmental factors (topography, vegetation cover) on each error type. Topographic structure and vegetation cover had significant effects on location error, where larger error was associated with north-facing aspects, steeper slopes and woody vegetation cover. Classification error was also affected by topography, and vegetation cover. Slope was the major factor that affected classification quality. Strong correlation was found between error in different time steps, for both error types. Correlation between these two error types in the same time step was much smaller and in most cases insignificant.

Thematic raster maps are digital images where each pixel is assigned to one of a k discrete classes. This type of maps is ubiquitous, and numerous remote sensing applications yield products such as maps of land cover, vegetation, or soil, all in the format of thematic raster maps.

Empirical modeling of vegetation dynamics can be used for predictive purposes. The goal of the present study is to construct and evaluate possible approaches for empirical modeling of vegetation dynamics, and to investigate their potential use in planning and management. An empirical model of mediterranean vegetation dynamics was constructed using a case study of vegetation change in an area in the Galilee mountains, northern Israel, between 1964 and 1992. Present vegetation in any location was modeled as a function of past vegetation and environmental factors (e.g., topography and various disturbances); future vegetation was then modeled as a function of current vegetation and effects of environmental factors. In order to assess model performance, we compared the actual vegetation map with maps representing model realizations for the study area and for an external validation area. Three types of measures were used to compare the predicted and actual vegetation maps: overall vegetation composition, pattern indices, and cell-by-cell match. We compared the performance of logistic vs. linear models and of stochastic vs. deterministic realizations of a logistic model. Our results indicate that landscape-scale vegetation dynamics can be fairly well modeled using a few biologically important variables. The logistic and linear models had similar performance, in spite of the reduced information on which the logistic models were based. The use of only a 4% sample of the database resulted in a negligible reduction in model performance. Model performance was reduced, but was still fair, when applied to an external area. The merits and limitations of this modeling approach are discussed in comparison with other approaches for modeling vegetation dynamics.

Detection and quantification of temporal change in spatial objects is the subject of a growing number of studies. Much of the change shown in such studies may be an artifact of location error and classification error. The basic units of these two measures are different (distance units for location error and pixel counts for classification error). The lack of a single index summarizing both error sources poses a constraint on assessing and interpreting the apparent change. We present an error model that addresses location and classification error jointly. Our approach quantifies location accuracy in terms of thematic accuracy, using a simulation of the location error process. We further develop an error model that combines the location and classification accuracy matrices into a single matrix, representing the overall thematic accuracy in a single layer. The resulting time-specific matrices serve to derive indices for estimating the overall uncertainty in a multi-temporal dataset. In order to validate the model, we performed simulations in which known amounts of location and classification error were introduced into raster maps. Our error model estimates were highly accurate under a wide range of parameters tested. We applied the error model to a study of vegetation dynamics in California woodlands in order to explore its value for realistic assessment of change, and its potential to provide a means for quantifying the relative contributions of these two error sources.

The dynamics of Mediterranean vegetation over 28 years was studied in the Northern Galilee Mountains, Israel, in order to identify and quantify the major factors affecting it at the landscape scale. Image analysis of historical and current aerial photographs was used to produce high resolution digital vegetation maps (pixel size = 30 cm) for an area of 4 km² in the Galilee Mountains, northern Israel. GIS tools were used to produce corresponding maps of grazing regime, topographic indices and other relevant environmental factors. The effects of those factors were quantified using a multiple regression analyses. Major changes in the vegetation occurred during the period studied (1964-1992); tree cover increased from 2% in 1964 to 41% in 1992, while herbaceous vegetation cover decreased from 56% in 1964 to 24% in 1992. Grazing, topography and initial vegetation cover were found to significantly affect present vegetation patterns. Both cattle grazing and goat grazing reduced the rate of increase in tree cover, yet even intensive grazing did not halt the process. Grazing affected also the woody-herbaceous vegetation dynamics, reducing the expansion of woody vegetation. Slope, aspect, and the interaction term between these two factors, significantly affected vegetation pattern. Altogether, 56% and 72% of the variability in herbaceous and tree cover, respectively, was explained by the regression models. This study indicates that spatially explicit Mediterranean vegetation dynamics can be predicted with fair accuracy using few biologically important environmental variables.

Conservation programs for insectivorous bats should be based on protecting their foraging habitats as well as their roosts. In this paper we present a method for assessing habitat use of a bat community using a bat detector. We found distinct patterns of habitat use for seven bat species in Mt Meron Nature Reserve, Israel. Using these patterns, we recommend conserving and fostering riparian vegetation, since several endangered species forage mainly in this increasingly threatened habitat.

Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighborhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbor classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.