Simulated poaching affects global connectivity and efficiency in social networks of African savanna elephants-An exemplar of how human disturbance impacts group-living species.

Selective harvest, such as poaching, impacts group-living animals directly through mortality of individuals with desirable traits, and indirectly by altering the structure of their social networks. Understanding the relationship between disturbance-induced, structural network changes and group performance in wild animals remains an outstanding problem. To address this problem, we evaluated the immediate effect of disturbance on group sociality in African savanna elephants-an example, group-living species threatened by poaching. Drawing on static association data from ten free-ranging groups, we constructed one empirically based, population-wide network and 100 virtual networks; performed a series of experiments 'poaching' the oldest, socially central or random individuals; and quantified the immediate change in the theoretical indices of network connectivity and efficiency of social diffusion. Although the social networks never broke down, targeted elimination of the socially central conspecifics, regardless of age, decreased network connectivity and efficiency. These findings hint at the need to further study resilience by modeling network reorganization and interaction-mediated socioecological learning, empirical data permitting. The main contribution of our work is in quantifying connectivity together with global efficiency in multiple social networks that feature the sociodemographic diversity likely found in wild elephant populations. The basic design of our simulation makes it adaptable for hypothesis testing about the consequences of anthropogenic disturbance or lethal management on social interactions in a variety of group-living species with limited, real-world data.

Data-driven discovery of the spatial scales of habitat choice by elephants.

Setting conservation goals and management objectives relies on understanding animal habitat preferences. Models that predict preferences combine location data from tracked animals with environmental information, usually at a spatial resolution determined by the available data. This resolution may be biologically irrelevant for the species in question. Individuals likely integrate environmental characteristics over varying distances when evaluating their surroundings; we call this the scale of selection. Even a single characteristic might be viewed differently at different scales; for example, a preference for sheltering under trees does not necessarily imply a fondness for continuous forest. Multi-scale preference is likely to be particularly evident for animals that occupy coarsely heterogeneous landscapes like savannahs. We designed a method to identify scales at which species respond to resources and used these scales to build preference models. We represented different scales of selection by locally averaging, or smoothing, the environmental data using kernels of increasing radii. First, we examined each environmental variable separately across a spectrum of selection scales and found peaks of fit. These 'candidate' scales then determined the environmental data layers entering a multivariable conditional logistic model. We used model selection via AIC to determine the important predictors out of this set. We demonstrate this method using savannah elephants (Loxodonta africana) inhabiting two parks in southern Africa. The multi-scale models were more parsimonious than models using environmental data at only the source resolution. Maps describing habitat preferences also improved when multiple scales were included, as elephants were more often in places predicted to have high neighborhood quality. We conclude that elephants select habitat based on environmental qualities at multiple scales. For them, and likely many other species, biologists should include multiple scales in models of habitat selection. Species environmental preferences and their geospatial projections will be more accurately represented, improving management decisions and conservation planning.

Quantitative analysis of forest fragmentation in the atlantic forest reveals more threatened bird species than the current red list.

Habitat loss and attendant fragmentation threaten the existence of many species. Conserving these species requires a straightforward and objective method that quantifies how these factors affect their survival. Therefore, we compared a variety of metrics that assess habitat fragmentation in bird ranges, using the geographical ranges of 127 forest endemic passerine birds inhabiting the Atlantic Forest of Brazil. A common, non-biological metric - cumulative area of size-ranked fragments within a species range - was misleading, as the least threatened species had the most habitat fragmentation. Instead, we recommend a modified version of metapopulation capacity. The metric links detailed spatial information on fragment sizes and spatial configuration to the birds' abilities to occupy and disperse across large areas (100,000+ km(2)). In the Atlantic Forest, metapopulation capacities were largely bimodal, in that most species' ranges had either low capacity (high risk of extinction) or high capacity (very small risk of extinction). This pattern persisted within taxonomically and ecologically homogenous groups, indicating that it is driven by fragmentation patterns and not differences in species ecology. Worryingly, we found IUCN considers some 28 of 58 species in the low metapopulation capacity cluster to not be threatened. We propose that assessing the effect of fragmentation will separate species more clearly into distinct risk categories than does a simple assessment of remaining habitat.

Estimating extinction risk with metapopulation models of large-scale fragmentation.

Habitat loss is the principal threat to species. How much habitat remains-and how quickly it is shrinking-are implicitly included in the way the International Union for Conservation of Nature determines a species' risk of extinction. Many endangered species have habitats that are also fragmented to different extents. Thus, ideally, fragmentation should be quantified in a standard way in risk assessments. Although mapping fragmentation from satellite imagery is easy, efficient techniques for relating maps of remaining habitat to extinction risk are few. Purely spatial metrics from landscape ecology are hard to interpret and do not address extinction directly. Spatially explicit metapopulation models link fragmentation to extinction risk, but standard models work only at small scales. Counterintuitively, these models predict that a species in a large, contiguous habitat will fare worse than one in 2 tiny patches. This occurs because although the species in the large, contiguous habitat has a low probability of extinction, recolonization cannot occur if there are no other patches to provide colonists for a rescue effect. For 4 ecologically comparable bird species of the North Central American highland forests, we devised metapopulation models with area-weighted self-colonization terms; this reflected repopulation of a patch from a remnant of individuals that survived an adverse event. Use of this term gives extra weight to a patch in its own rescue effect. Species assigned least risk status were comparable in long-term extinction risk with those ranked as threatened. This finding suggests that fragmentation has had a substantial negative effect on them that is not accounted for in their Red List category.

Sampling designs matching species biology produce accurate and affordable abundance indices.

Wildlife biologists often use grid-based designs to sample animals and generate abundance estimates. Although sampling in grids is theoretically sound, in application, the method can be logistically difficult and expensive when sampling elusive species inhabiting extensive areas. These factors make it challenging to sample animals and meet the statistical assumption of all individuals having an equal probability of capture. Violating this assumption biases results. Does an alternative exist? Perhaps by sampling only where resources attract animals (i.e., targeted sampling), it would provide accurate abundance estimates more efficiently and affordably. However, biases from this approach would also arise if individuals have an unequal probability of capture, especially if some failed to visit the sampling area. Since most biological programs are resource limited, and acquiring abundance data drives many conservation and management applications, it becomes imperative to identify economical and informative sampling designs. Therefore, we evaluated abundance estimates generated from grid and targeted sampling designs using simulations based on geographic positioning system (GPS) data from 42 Alaskan brown bears (Ursus arctos). Migratory salmon drew brown bears from the wider landscape, concentrating them at anadromous streams. This provided a scenario for testing the targeted approach. Grid and targeted sampling varied by trap amount, location (traps placed randomly, systematically or by expert opinion), and traps stationary or moved between capture sessions. We began by identifying when to sample, and if bears had equal probability of capture. We compared abundance estimates against seven criteria: bias, precision, accuracy, effort, plus encounter rates, and probabilities of capture and recapture. One grid (49 km(2) cells) and one targeted configuration provided the most accurate results. Both placed traps by expert opinion and moved traps between capture sessions, which raised capture probabilities. The grid design was least biased (-10.5%), but imprecise (CV 21.2%), and used most effort (16,100 trap-nights). The targeted configuration was more biased (-17.3%), but most precise (CV 12.3%), with least effort (7,000 trap-nights). Targeted sampling generated encounter rates four times higher, and capture and recapture probabilities 11% and 60% higher than grid sampling, in a sampling frame 88% smaller. Bears had unequal probability of capture with both sampling designs, partly because some bears never had traps available to sample them. Hence, grid and targeted sampling generated abundance indices, not estimates. Overall, targeted sampling provided the most accurate and affordable design to index abundance. Targeted sampling may offer an alternative method to index the abundance of other species inhabiting expansive and inaccessible landscapes elsewhere, provided their attraction to resource concentrations.

Breeding birds on small islands: island biogeography or optimal foraging?

1. We test MacArthur and Wilson's theory about the biogeography of communities on isolated habitat patches using bird breeding records from 16 small islands off the coasts of Britain and Ireland. 2. A traditional examination of patterns of species richness on these islands suggests that area and habitat diversity are important predictors, but that isolation and latitude have a negligible impact in this system. 3. Unlike traditional studies, we directly examine the fundamental processes of colonization and local extinction (cessation of breeding), rather than higher-order phenomena such as species richness. 4. We find that many of MacArthur and Wilson's predictions hold: colonization probability is lower on more isolated islands, and extinction probability is lower on larger islands and those with a greater diversity of habitats. 5. We also find an unexpected pattern: extinction probability is much lower on more isolated islands. This is the strongest relationship in these data, and isolation is the best single predictor of colonization and extinction. 6. Our results show that examination of species richness alone is misleading. Isolation has a strong effect on both of the dynamic processes that underlie richness, and in this system, the reductions in both colonization and extinction probability seen on more distant islands have opposing influences on species richness, and largely cancel each other out. 7. We suggest that an appropriate model for this system might be optimal foraging theory, which predicts that organisms will stay longer in a resource patch if the distance to a neighbouring patch is large. If nest sites and food are the resources in this system, then optimal foraging theory predicts the pattern we observe. 8. We advance the hypothesis that there is a class of spatial systems, defined by their scale and by the taxon under consideration, at which decision-making processes are a key driver of the spatiotemporal dynamics. The appropriate theory for such systems will be a hybrid of concepts from biogeography/metapopulation theory and behavioural ecology.

Rates of species loss from Amazonian forest fragments.

In the face of worldwide habitat fragmentation, managers need to devise a time frame for action. We ask how fast do understory bird species disappear from experimentally isolated plots in the Biological Dynamics of Forest Fragments Project, central Amazon, Brazil. Our data consist of mist-net records obtained over a period of 13 years in 11 sites of 1, 10, and 100 hectares. The numbers of captures per species per unit time, analyzed under different simplifying assumptions, reveal a set of species-loss curves. From those declining numbers, we derive a scaling rule for the time it takes to lose half the species in a fragment as a function of its area. A 10-fold decrease in the rate of species loss requires a 1,000-fold increase in area. Fragments of 100 hectares lose one half of their species in <15 years, too short a time for implementing conservation measures.