Transcending scale dependence in identifying habitat with resource selection functions

Multi-scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi-scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale-integrated resource selection functions (SRSFs) that synthesize results across scales for management-oriented habitat suitability mapping. We found a previously undocumented scale-specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut-blocks at broad scales according to first- and second-order RSFs and avoided linear features at fine scales according to third-order RSFs, corroborating predictions developed according to predator-mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single-scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.     

Grizzly bear habitat selection is scale dependent.

The purpose of our study is to show how ecologists' interpretation of habitat selection by grizzly bears (Ursus arctos) is altered by the scale of observation and also how management questions would be best addressed using predetermined scales of analysis. Using resource selection functions (RSF) we examined how variation in the spatial extent of availability affected our interpretation of habitat selection by grizzly bears inhabiting mountain and plateau landscapes. We estimated separate models for females and males using three spatial extents: within the study area, within the home range, and within predetermined movement buffers. We employed two methods for evaluating the effects of scale on our RSF designs. First, we chose a priori six candidate models, estimated at each scale, and ranked them using Akaike Information Criteria. Using this method, results changed among scales for males but not for females. For female bears, models that included the full suite of covariates predicted habitat use best at each scale. For male bears that resided in the mountains, models based on forest successional stages ranked highest at the study-wide and home range extents, whereas models containing covariates based on terrain features ranked highest at the buffer extent. For male bears on the plateau, each scale estimated a different highest-ranked model. Second, we examined differences among model coefficients across the three scales for one candidate model. We found that both the magnitude and direction of coefficients were dependent upon the scale examined; results varied between landscapes, scales, and sexes. Greenness, reflecting lush green vegetation, was a strong predictor of the presence of female bears in both landscapes and males that resided in the mountains. Male bears on the plateau were the only animals to select areas that exposed them to a high risk of mortality by humans. Our results show that grizzly bear habitat selection is scale dependent. Further, the selection of resources can be dependent upon the availability of a particular vegetation type on the landscape. From a management perspective, decisions should be based on a hierarchical process of habitat selection, recognizing that selection patterns vary across scales.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

深圳市盐田区植被格局分析

运用景观生态学的方法 ,选取斑块形状指数、斑块形状破碎化指数、分维数、景观多样性指数、均匀度、相对丰富度、优势度等指标 ,对深圳盐田区的植被格局进行分析。结果显示 :在城乡发展过程中 ,盐田区植被景观保存较好 ,类型丰富 ;植被景观总体上呈现出符合其生境特征的规则分布 ;群落景观异质性较高 ,拥有南亚热带的沟谷雨林、山地常绿阔叶林、红树林等特色植被景观 ;沟谷雨林和季风常绿林是本区较脆弱的生态系统 ,应积极加以保护和发展     

Spectrum of selection: new approaches to detecting the scale-dependent response to habitat

Detecting habitat selection depends on the spatial scale of analysis, but multi-scale studies have been limited by the use of a few, spatially variable, hierarchical levels. We developed spatially explicit approaches to quantify selection along a continuum of scales using spatial (coarse-graining) and geostatistical (variogram) pattern analyses at multiple levels of habitat use (seasonal range, travel routes, feeding areas, and microsites). We illustrate these continuum-based approaches by applying them to winter habitat selection by woodland caribou (Rangifer tarandus caribou) using two key habitat components, Cladina lichens and snow depth. We quantified selection as the reduction in variance in used relative to available sites, thus avoiding reliance on correlations between organism and habitat, for which interpretation can be impeded by cross-scale correlations. By consistently selecting favorable habitat features, caribou experienced reduced variance in these features. The degree to which selection was accounted for by the travel route, feeding area, or microsite levels varied across the scale continuum. Caribou selected for Cladina within a 13-km scale domain and selected shallower snow at all scales. Caribou responded most strongly at the dominant scales of patchiness, implicating habitat heterogeneity as an underlying cause of multi-scale habitat selection. These novel approaches enable a spatial understanding of resource selection behavior.     

Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model

Abstract:  Organisms respond to their surroundings at multiple spatial scales, and different organisms respond differently to the same environment. Existing landscape models, such as the "fragmentation model" (or patch-matrix-corridor model) and the "variegation model," can be limited in their ability to explain complex patterns for different species and across multiple scales. An alternative approach is to conceptualize landscapes as overlaid species-specific habitat contour maps. Key characteristics of this approach are that different species may respond differently to the same environmental conditions and at different spatial scales. Although similar approaches are being used in ecological modeling, there is much room for habitat contours as a useful conceptual tool. By providing an alternative view of landscapes, a contour model may stimulate more field investigations stratified on the basis of ecological variables other than human-defined patches and patch boundaries. A conceptual model of habitat contours may also help to communicate ecological complexity to land managers. Finally, by incorporating additional ecological complexity, a conceptual model based on habitat contours may help to bridge the perceived gap between pattern and process in landscape ecology. Habitat contours do not preclude the use of existing landscape models and should be seen as a complementary approach most suited to heterogeneous human-modified landscapes.     

Complex effects of habitat amount and fragmentation on functional connectivity and inbreeding in a giant panda population

The relationships between habitat amount and fragmentation level and functional connectivity and inbreeding remain unclear. Thus, we used genetic algorithms to optimize the transformation of habitat area and fragmentation variables into resistance surfaces to predict genetic structure and examined habitat area and fragmentation effects on inbreeding through a moving window and spatial autoregressive modeling approach. We applied these approaches to a wild giant panda population. The amount of habitat and its level of fragmentation had nonlinear effects on functional connectivity (gene flow) and inbreeding. Functional connectivity was highest when approximately 80% of the surrounding landscape was habitat. Although the relationship between habitat amount and inbreeding was also nonlinear, inbreeding increased as habitat increased until about 20% of the local landscape contained habitat, after which inbreeding decreased as habitat increased. Because habitat fragmentation also had nonlinear relationships with functional connectivity and inbreeding, we suggest these important responses cannot be effectively managed by minimizing or maximizing habitat or fragmentation. Our work offers insights for prioritization of protected areas.     

Efficacy of Land-Cover Models in Predicting Isolation of Marbled Salamander Populations in a Fragmented Landscape

Abstract:  Amphibians worldwide are facing rapid declines due to habitat loss and fragmentation, disease, and other causes. Where habitat alteration is implicated, there is a need for spatially explicit conservation plans. Models built with geographic information systems (GIS) are frequently used to inform such planning. We explored the potential for using GIS models of functional landscape connectivity as a reliable proxy for genetically derived measures of population isolation. We used genetic assignment tests to characterize isolation of marbled salamander populations and evaluated whether the relative amount of modified habitat around breeding ponds was a reliable indicator of population isolation. Using a resampling analysis, we determined whether certain land-cover variables consistently described population isolation. We randomly drew half the data for model building and tested the performance of the best models on the other half 100 times. Deciduous forest was consistently associated with lower levels of population isolation, whereas salamander populations in regions of agriculture and anthropogenic development were more isolated. Models that included these variables and pond size explained 65–70% of variation in genetically inferred isolation across sites. The resampling analysis confirmed that these habitat variables were consistently good predictors of isolation. Used judiciously, simple GIS models with key land-cover variables can be used to estimate population isolation if field sampling and genetic analysis are not possible.     

Linking occurrence and fitness to persistence: habitat-based approach for endangered greater sage-grouse.

Detailed empirical models predicting both species occurrence and fitness across a landscape are necessary to understand processes related to population persistence. Failure to consider both occurrence and fitness may result in incorrect assessments of habitat importance leading to inappropriate management strategies. We took a two-stage approach to identifying critical nesting and brood-rearing habitat for the endangered Greater Sage-Grouse (Centrocercus urophasianus) in Alberta at a landscape scale. First, we used logistic regression to develop spatial models predicting the relative probability of use (occurrence) for Sage-Grouse nests and broods. Secondly, we used Cox proportional hazards survival models to identify the most risky habitats across the landscape. We combined these two approaches to identify Sage-Grouse habitats that pose minimal risk of failure (source habitats) and attractive sink habitats that pose increased risk (ecological traps). Our models showed that Sage-Grouse select for heterogeneous patches of moderate sagebrush cover (quadratic relationship) and avoid anthropogenic edge habitat for nesting. Nests were more successful in heterogeneous habitats, but nest success was independent of anthropogenic features. Similarly, broods selected heterogeneous high-productivity habitats with sagebrush while avoiding human developments, cultivated cropland, and high densities of oil wells. Chick mortalities tended to occur in proximity to oil and gas developments and along riparian habitats. For nests and broods, respectively, approximately 10% and 5% of the study area was considered source habitat, whereas 19% and 15% of habitat was attractive sink habitat. Limited source habitats appear to be the main reason for poor nest success (39%) and low chick survival (12%). Our habitat models identify areas of protection priority and areas that require immediate management attention to enhance recruitment to secure the viability of this population. This novel approach to habitat-based population viability modeling has merit for many species of concern.     

The effects of habitat loss, fragmentation, and community homogenization on resilience in estuaries.

When changes in the frequency and extent of disturbance outstrip the recovery potential of resident communities, the selective removal of species contributes to habitat loss and fragmentation across landscapes. The degree to which habitat change is likely to influence community resilience will depend on metacommunity structure and connectivity. Thus ecological connectivity is central to understanding the potential for cumulative effects to impact upon diversity. The importance of these issues to coastal marine communities, where the prevailing concept of open communities composed of highly dispersive species is being challenged, indicates that these systems may be more sensitive to cumulative impacts than previously thought. We conducted a disturbance-recovery experiment across gradients of community type and environmental conditions to assess the roles of ecological connectivity and regional variations in community structure on the recovery of species richness, total abundance, and community composition in Mahurangi Harbour, New Zealand. After 394 days, significant differences in recovery between sites were apparent. Statistical models explaining a high proportion of the variability (R2 > 0.92) suggested that community recovery rates were controlled by a combination of physical and ecological features operating across spatial scales, affecting successional processes. The dynamic and complex interplay of ecological and environmental processes we observed driving patch recovery across the estuarine landscape are integral to recovery from disturbances in heterogeneous environments. This link between succession/recovery, disturbance, and heterogeneity confirms the utility of disturbance-recovery experiments as assays for cumulative change due to fragmentation and habitat change in estuaries.     

Spatial heterogeneity influences native and nonnative plant species richness

Spatial heterogeneity may have differential effects on the distribution of native and nonnative plant species richness. We examined the effects of spatial heterogeneity on native and nonnative plant species richness distributions in the central part of Rocky Mountain National Park, Colorado, USA. Spatial heterogeneity around vegetation plots was characterized using landscape metrics, environmental/topographic variables (slope, aspect, elevation, and distance from stream or river), and soil variables (nitrogen, clay, and sand). The landscape metrics represented five components of landscape heterogeneity and were measured at four spatial extents (within varying radii of 120, 240, 480, and 960 m) using the FRAGSTATS landscape pattern analysis program. Akaike's Information Criterion adjusted for small sample size (AICc) was used to select the best models from a set of multiple linear regression models developed for native and nonnative plant species richness at four spatial extents and three levels of ecological hierarchy (i.e., landscape, land cover, and community). Both native and nonnative plant species richness were positively correlated with edge density, Simpson's diversity index and interspersion/juxtaposition index, and were negatively correlated with mean patch size. The amount of variation explained at four spatial extents and three hierarchical levels ranged from 30% to 70%. At the landscape level, the best models explained 43% of the variation in native plant species richness and 70% of the variation in nonnative plant species richness (240-m extent). In general, the amount of variation explained was always higher for nonnative plant species richness, and the inclusion of landscape metrics always significantly improved the models. The best models explained 66% of the variation in nonnative plant species richness for both the conifer land cover type and lodgepole pine community. The relative influence of the components of spatial heterogeneity differed for native and nonnative plant species richness and varied with the spatial extent of analysis and levels of ecological hierarchy. The study offers an approach to quantify spatial heterogeneity to improve models of plant biodiversity. The results demonstrate that ecologists must recognize the importance of spatial heterogeneity in managing native and nonnative plant species.     

Continuous versus binary representations of landscape heterogeneity in spatially-explicit models of mobile populations

How a landscape is represented is an important structural assumption in spatially-explicit simulation models. Simple models tend to specify just habitat and non-habitat (binary), while more complex models may use multiple levels or a continuum of habitat quality (continuous). How these different representations influence model projections is unclear. To assess the influence of landscape representation on population models, I developed a general, individual-based model with local dispersal and examined population persistence across binary and continuous landscapes varying in the amount and fragmentation of habitat. In binary and continuous landscapes habitat and non-habitat were assigned a unique mean suitability. In continuous landscapes, suitability of each individual site was then drawn from a normal distribution with fixed variance. Populations went extinct less often and abundances were higher in continuous landscapes. Production in habitat and non-habitat was higher in continuous landscapes, because the range of habitat suitability sampled by randomly dispersing individuals was higher than the overall mean habitat suitability. Increasing mortality, dispersal distance, and spatial heterogeneity all increased the discrepancy between continuous and binary landscapes. The effect of spatial structure on the probability of extinction was greater in binary landscapes. These results show that, under certain circumstances, model projections are influenced by how variation in suitability within a landscape is represented. Care should be taken to assess how a given species actually perceives the landscape when conducting population viability analyses or empirical validation of theory.     

Landscape ecology of eastern coyotes based on large-scale estimates of abundance.

Since their range expansion into eastern North America in the mid-1900s, coyotes (Canis latrans) have become the region's top predator. Although widespread across the region, coyote adaptation to eastern forests and use of the broader landscape are not well understood. We studied the distribution and abundance of coyotes by collecting coyote feces from 54 sites across a diversity of landscapes in and around the Adirondacks of northern New York. We then genotyped feces with microsatellites and found a close correlation between the number of detected individuals and the total number of scats at a site. We created habitat models predicting coyote abundance using multi-scale vegetation and landscape data and ranked them with an information-theoretic model selection approach. These models allow us to reject the hypothesis that eastern forests are unsuitable habitat for coyotes as their abundance was positively correlated with forest cover and negatively correlated with measures of rural non-forest landscapes. However, measures of vegetation structure turned out to be better predictors of coyote abundance than generalized "forest vs. open" classification. The best supported models included those measures indicative of disturbed forest, especially more open canopies found in logged forests, and included natural edge habitats along water courses. These forest types are more productive than mature forests and presumably host more prey for coyotes. A second model with only variables that could be mapped across the region highlighted the lower density of coyotes in areas with high human settlement, as well as positive relationships with variables such as snowfall and lakes that may relate to increased numbers and vulnerability of deer. The resulting map predicts coyote density to be highest along the southwestern edge of the Adirondack State Park, including Tug Hill, and lowest in the mature forests and more rural areas of the central and eastern Adirondacks. Together, these results support the need for a nuanced view of how eastern coyotes use forested habitats.     

Species distribution modelling: Does one size fit all? A phytogeographic analysis of Salix in Ontario

Empirical models for predicting the distribution of organisms from environmental data have often focused on principles of ecological niche theory. However, even at large scales, there is little agreement over how to represent the dimensions of a species’ niche. The performance of such models is greatly affected by the nature of species distributional and environmental data. Regional scale distribution models were developed for 30 willow species in Ontario to examine (i) the predictive ability of logistic regression analysis, and (ii) the effects of using different distributional and environmental data sets. Two original measures of model accuracy and over-prediction were employed and evaluated using independent data. Models based on unique combinations of monthly climate data predicted distributions most accurately for all species. Models based on a fixed set of variables, while generating the highest average probabilities of occurrence for certain species with limited ranges, resulted in the greatest under- and over-estimates of willow distributions. Comparisons of models demonstrated climatic patterns among willows of differing habit and habitat. The distribution of dwarf willow species, present only in the Ontario arctic, followed gradients of summer maximum temperatures. The distribution of the tree species in the southerly portions of the province followed gradients of fall and winter minimum temperatures. Regardless of distributional and environmental data input, no algorithm maximized model performance for all species. Individual species models require individual approaches; i.e., the variable selection technique, the set of environmental factors used as predictors, and the nature of species distributional data must be carefully matched to the intended application. An understanding of evolutionary processes enhances the meaningful interpretation of individual species models. Unless sampling bias and species prevalence can be accounted for, models based on collection point data are best used to guide field surveys. While inferred range data may be better suited to determine potential ecological niches, overestimation of species prevalence and environmental tolerance must be recognized. A combination of available distributional data types is recommended to best determine species niches, an important step in developing conservation strategies.     

Environmental Surrogates for Predicting and Conserving Adaptive Genetic Variability in Tree Species

Abstract: Adaptive genetic variability within species is an essential component of biodiversity but has been largely ignored in studies aimed at assessing and predicting biodiversity of the forest environment. We used factorial regression and structuring models to test easily measured surrogates, such as ecological attributes, as predictors of adaptive genetic variation between populations of a tree species ( Eucalyptus delegatensis ). Adaptive variability was defined in terms of variation in average growth performance of 68 populations and of population-by-environment interaction across seven different environments. The best surrogates of genetic variability were measures of solar radiation and temperature range, each predicting more than 50% of the genetic variability within the species. Rock and understory types, when used either alone or in combination with other covariates, also were very efficient in discriminating between populations in groups showing similar adaptation. Significant relationships between particular surrogates and growth patterns of variation were attributed to effects of natural selection that had occurred in the population source locations. We recommend the development of studies focusing on the population level of biodiversity to improve the conservation of forest ecosystems in Australia.     

Importance of dispersal routes that minimize open‐ocean movement to the genetic structure of island populations

Islands present a unique scenario in conservation biology, offering refuge yet imposing limitations on insular populations. The Kimberley region of northwestern Australia has more than 2500 islands that have recently come into focus as substantial conservation resources. It is therefore of great interest for managers to understand the driving forces of genetic structure of species within these island archipelagos. We used the ubiquitous bar‐shouldered skink (Ctenotus inornatus) as a model species to represent the influence of landscape factors on genetic structure across the Kimberley islands. On 41 islands and 4 mainland locations in a remote area of Australia, we genotyped individuals across 18 nuclear (microsatellite) markers. Measures of genetic differentiation and diversity were used in two complementary analyses. We used circuit theory and Mantel tests to examine the influence of the landscape matrix on population connectivity and linear regression and model selection based on Akaike's information criterion to investigate landscape controls on genetic diversity. Genetic differentiation between islands was best predicted with circuit‐theory models that accounted for the large difference in resistance to dispersal between land and ocean. In contrast, straight‐line distances were unrelated to either resistance distances or genetic differentiation. Instead, connectivity was determined by island‐hopping routes that allow organisms to minimize the distance of difficult ocean passages. Island populations of C. inornatus retained varying degrees of genetic diversity (N_A = 1.83 – 7.39), but it was greatest on islands closer to the mainland, in terms of resistance‐distance units. In contrast, genetic diversity was unrelated to island size. Our results highlight the potential for islands to contribute to both theoretical and applied conservation, provide strong evidence of the driving forces of population structure within undisturbed landscapes, and identify the islands most valuable for conservation based on their contributions to gene flow and genetic diversity.     

Thresholds in Songbird Occurrence in Relation to Landscape Structure

Abstract:  Theory predicts the occurrence of threshold levels of habitat in landscapes, below which ecological processes change abruptly. Simulation models indicate that below critical thresholds, fragmentation of habitat influences patch occupancy by decreasing colonization rates and increasing rates of local extinction. Uncovering such putative relationships is important for understanding the demography of species and in developing sound conservation strategies. Using segmented logistic regression, we tested for thresholds in occurrence of 15 bird species as a function of the amount of suitable habitat at multiple scales (150–2000-m radii). Suitable habitat was defined quantitatively based on previously derived, spatially explicit distribution models for each species. The occurrence of 10 out of 15 species was influenced by the amount of habitat at a landscape scale (≥500-m radius). Of these species all but one were best predicted by threshold models. Six out of nine species exhibited asymptotic thresholds; the effects of habitat loss intensified at low amounts of habitat in a landscape. Landscape thresholds ranged from 8.6% habitat to 28.7% (     = 18.5 ± 2.6%[95% CI]). For two species landscape thresholds coincided with sensitivity to fragmentation; both species were more likely to occur in large patches, but only when the amount of habitat in a landscape was low. This supports the fragmentation threshold hypothesis. Nevertheless, the occurrence of most species appeared to be unaffected by fragmentation, regardless of the amount of habitat present at landscape extents. The thresholds we identified may be useful to managers in establishing conservation targets. Our results indicate that findings of landscape-scale studies conducted in regions with relatively high proportions of habitat and low fragmentation may not be applicable in regions with low habitat proportions and high fragmentation.     

Fire and forest landscapes in the Georgia Piedmont: an assessment of spatial modeling assumptions

Landscape simulation models are widely used to study the behavior of ecological systems. As computing power has increased, these models have become more complex and incorporated more realistic spatial representations of landscape patterns and ecological processes. The goal of this research was to examine the sensitivity of simulated landscape patterns to fundamental spatial modeling assumptions. The LANDIS simulator was parameterized for forests of the Georgia Piedmont and used to model landscape-scale community dynamics at fire return intervals from 20 to 100 years. A base scenario incorporating localized seed dispersal along with landform-related variation in species establishment rates and disturbance regimes was contrasted with three alternative scenarios. The uniform habitat scenario applied the same set of species establishment coefficients across all landforms. The uniform dispersal scenario removed the effects of seed source abundance and pattern on species establishment. The uniform disturbance scenario assumed identical disturbance regimes on all landforms.At the shortest fire return intervals, fire severities were low and the stand age distribution was dominated by older forests. At longer fire return intervals, fire severities were high and the stand age distribution was skewed toward younger forests. Species composition generally followed a gradient from fire-resistant species at short fire return intervals to fire-sensitive species at longer fire return intervals. However, some species exhibited bimodal distributions with high abundances at both short and long fire return intervals. Landscape responses to fire were similar in the uniform habitat scenario and the base scenario. Communities were less sensitive to fire return interval and had more fire-sensitive species in the uniform dispersal scenario than in the base scenario. Species composition in the uniform disturbance scenario was similar to the base scenario for the longest fire-intervals, but was more sensitive to changes in the fire regime at shorter fire return intervals. In models of Piedmont forest landscapes, accurate spatial representations of dispersal and fire regime heterogeneity are essential for predicting landscape-scale species composition under changing fire regimes. In contrast, the precise spatial representation of species–habitat relationships may be considerably less important.     

A model for the evolution of communal foraging in hierarchically structured populations

A kin selection model is described for populations in which groups of interacting individuals (trait groups, sensu Wilson 1975) are spatially situated within larger aggregations. The model predicts the optimal foraging strategy when resources are shared with other trait group members and there is an individual risk in foraging. The ecological mechanism of variation in group fitness, differential resource accumulation, is explicitly incorporated into the model. The optimal foraging rate obtained from this model depends on the product of a benefit-to-cost ratio and a relatedness parameter. The appropriate definition of relatedness for the evolution of communal foraging is determined by the details of the ecological interaction between consumers and resources. When competition is purely intra-specific, the genetic correlation among interactants relative to other members of the local aggregation defines the relatedness parameter applicable to selection on foraging propensity. When competition is primarily inter-specific, the genetic correlation among trait group members relative to the entire population defines relatedness.