Estimation of spatiotemporal trends in bat abundance from mortality data collected at wind turbines

Renewable energy sources, such as wind energy, are essential tools for reducing the causes of climate change, but wind turbines can pose a collision risk for bats. To date, the population-level effects of wind-related mortality have been estimated for only 1 bat species. To estimate temporal trends in bat abundance, we considered wind turbines as opportunistic sampling tools for flying bats (analogous to fishing nets), where catch per unit effort (carcass abundance per monitored turbine) is a proxy for aerial abundance of bats, after accounting for seasonal variation in activity. We used a large, standardized data set of records of bat carcasses from 594 turbines in southern Ontario, Canada, and corrected these data to account for surveyor efficiency and scavenger removal. We used Bayesian hierarchical models to estimate temporal trends in aerial abundance of bats and to explore the effect of spatial factors, including landscape features associated with bat habitat (e.g., wetlands, croplands, and forested lands), on the number of mortalities for each species. The models showed a rapid decline in the abundance of 4 species in our study area; declines in capture of carcasses over 7 years ranged from 65% (big brown bat [Eptesicus fuscus]) to 91% (silver-haired bat [Lasionycteris noctivagans]). Estimated declines were independent of the effects of mitigation (increasing wind speed at which turbines begin to generate electricity from 3.5 to 5.5 m/s), which significantly reduced but did not eliminate bat mortality. Late-summer mortality of hoary (Lasiurus cinereus), eastern red (Lasiurus borealis), and silver-haired bats was predicted by woodlot cover, and mortality of big brown bats decreased with increasing elevation. These landscape predictors of bat mortality can inform the siting of future wind energy operations. Our most important result is the apparent decline in abundance of four common species of bat in the airspace, which requires further investigation.     

Distribution of a Community of Mammals in Relation to Roads and Other Human Disturbances in Gabon,Central Africa

We present the first community‐level study of the associations of both roads and other human disturbances with the distribution of mammals in Gabon (central Africa). Our study site was in an oil concession within a littoral mosaic landscape. We conducted surveys along 199 line transects and installed camera traps on 99 of these transects to document mammal presence and abundance. We used generalized linear mixed‐effect models to document associations between variables related to the ecosystem (land cover, topography, and hydrology), roads (coating, width of rights of way, condition, type of vehicle used on the road, traffic level, affiliation of users, and general type of road), and other human disturbances (urbanization, agriculture, hunting, logging, gathering, and industrial activities) and the abundance or presence of 17 species or groups of mammals including elephant (Loxodonta cyclotis), buffalo (Syncerus caffer), sitatunga (Tragelaphus spekei), red river hog (Potamochoerus porcus), smaller ungulates, gorilla (Gorilla gorilla), chimpanzee (Pan troglodytes), side‐striped jackal (Canis adustus), carnivores, monkeys, and large rodents. Some types of roads and other human disturbances were negatively associated with the abundance or presence of elephants, buffalos, gorillas, sitatungas, some monkeys, and duikers. The pattern of associations of mammals with roads and other human disturbances was diverse and included positive associations with road presence (red river hog, some monkeys, and duikers), agriculture (sitatunga, small carnivores, and large rodents) and industrial activities (sitatunga, red river hog, red duikers, and side‐striped jackal). Our results suggest that the community of mammals we studied was mostly affected by hunting, agriculture, and urbanization, which are facilitated by road presence. We recommend increased regulation of agriculture, hunting, and road building in the area. Distribución de una Comunidad de Mamíferos en Relación a Carreteras y Otras Perturbaciones Humanas en Gabón, Africa Central     

The importance of considering multiple interacting species for conservation of species at risk

Conservation of species at risk of extinction is complex and multifaceted. However, mitigation strategies are typically narrow in scope, an artifact of conservation research that is often limited to a single species or stressor. Knowledge of an entire community of strongly interacting species would greatly enhance the comprehensiveness and effectiveness of conservation decisions. We investigated how camera trapping and spatial count models, an extension of spatial-recapture models for unmarked populations, can accomplish this through a case study of threatened boreal woodland caribou (Rangifer tarandus caribou). Population declines in caribou are precipitous and well documented, but recovery strategies focus heavily on control of wolves (Canis lupus) and pay less attention to other known predators and apparent competitors. Obtaining necessary data on multispecies densities has been difficult. We used spatial count models to concurrently estimate densities of caribou, their predators (wolf, black bear [Ursus americanus], and coyote [Canis latrans]), and alternative prey (moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) from a camera-trap array in a highly disturbed landscape within northern Alberta's Oil Sands Region. Median densities were 0.22 caribous (95% Bayesian credible interval [BCI] = 0.08–0.65), 0.77 wolves (95% BCI = 0.26–2.67), 2.39 moose (95% BCI = 0.56–7.00), 2.64 coyotes (95% BCI = 0.45–6.68), and 3.63 black bears (95% BCI = 1.25–8.52) per 100 km². (The white-tailed deer model did not converge.) Although wolf densities were higher than densities recommended for caribou conservation, we suggest the markedly higher black bear and coyote densities may be of greater concern, especially if government wolf control further releases these species. Caribou conservation with a singular focus on wolf control may leave caribou vulnerable to other predators. We recommend a broader focus on the interacting species within a community when conserving species.     

Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R² = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.     

An integrated approach for predicting fates of reintroductions with demographic data from multiple populations

We devised a novel approach to model reintroduced populations whereby demographic data collected from multiple sites are integrated into a Bayesian hierarchical model. Integrating data from multiple reintroductions allows more precise population-growth projections to be made, especially for populations for which data are sparse, and allows projections that account for random site-to-site variation to be made before new reintroductions are attempted. We used data from reintroductions of the North Island Robin (Petroica longipes), an endemic New Zealand passerine, to 10 sites where non-native mammalian predators are controlled. A comparison of candidate models that we based on deviance information criterion showed that rat-tracking rate (an index of rat density) was a useful predictor of robin fecundity and adult female survival, that landscape connectivity and a binary measure of whether sites were on a peninsula were useful predictors of apparent juvenile survival (probably due to differential dispersal away from reintroduction sites), and that there was unexplained random variation among sites in all demographic rates. We used the two best supported models to estimate the finite rate of increase (λ) for populations at each of the 10 sites, and for a proposed reintroduction site, under different levels of rat control. Only three of the reintroduction sites had λ distributions completely >1 for either model. At two sites, λ was expected to be >1 if rat-tracking rates were <5%. At the other five reintroduction sites, λ was predicted to be close to 1, and it was unclear whether growth was expected. Predictions of λ for the proposed reintroduction site were less precise than for other sites because distributions incorporated the full range of site-to-site random variation in vital rates. Our methods can be applied to any species for which postrelease data on demographic rates are available and potentially can be extended to model multiple species simultaneously.     

Predicting species distributions from samples collected along roadsides

Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.     

Effects of tour boats on dolphin activity examined with sensitivity analysis of Markov chains

In Patagonia, Argentina, watching dolphins, especially dusky dolphins (Lagenorhynchus obscurus), is a new tourist activity. Feeding time decreases and time to return to feeding after feeding is abandoned and time it takes a group of dolphins to feed increase in the presence of boats. Such effects on feeding behavior may exert energetic costs on dolphins and thus reduce an individual's survival and reproductive capacity or maybe associated with shifts in distribution. We sought to predict which behavioral changes modify the activity pattern of dolphins the most. We modeled behavioral sequences of dusky dolphins with Markov chains. We calculated transition probabilities from one activity to another and arranged them in a stochastic matrix model. The proportion of time dolphins dedicated to a given activity (activity budget) and the time it took a dolphin to resume that activity after it had been abandoned (recurrence time) were calculated. We used a sensitivity analysis of Markov chains to calculate the sensitivity of the time budget and the activity-resumption time to changes in behavioral transition probabilities. Feeding-time budget was most sensitive to changes in the probability of dolphins switching from traveling to feeding behavior and of maintaining feeding behavior. Thus, an increase in these probabilities would be associated with the largest reduction in the time dedicated to feeding. A reduction in the probability of changing from traveling to feeding would also be associated with the largest increases in the time it takes dolphins to resume feeding. To approach dolphins when they are traveling would not affect behavior less because presence of the boat may keep dolphins from returning to feeding. Our results may help operators of dolphin-watching vessels minimize negative effects on dolphins.     

Identifying climate change refugia for South American biodiversity

Refugia-based conservation offers long-term effectiveness and minimize uncertainty on strategies for climate change adaptation. We used distribution modelling to identify climate change refugia for 617 terrestrial mammals and to quantify the role of protected areas (PAs) in providing refugia across South America. To do so, we compared species potential distribution across different scenarios of climate change, highlighting those regions likely to retain suitable climatic conditions by year 2090, and explored the proportion of refugia inside PAs. Moist tropical forests in high-elevation areas with complex topography concentrated the highest local diversity of species refugia, although regionally important refugia centers occurred elsewhere. Andean–Amazon forests contained climate change refugia for more than half of the continental species’ pool and for up to 87 species locally (17 × 17 km² grid cell). The highlands of the southern Atlantic Forest also included megadiverse refugia for up to 76 species per cell. Almost half of the species that may find refugia in the Atlantic Forest will do so in a single region—the Serra do Mar and Serra do Espinhaço. Most of the refugia we identified, however, were not in PAs, which may contain <6% of the total area of climate change refugia, leaving 129–237 species with no refugia inside the territorial limits of PAs of any kind. Our results reveal a dismal scenario for the level of refugia protection in some of the most biodiverse regions of the world. Nonetheless, because refugia tend to be in high-elevation, topographically complex, and remote areas, with lower anthropogenic pressure, formally protecting them may require a comparatively modest investment.     

Proposed Conservation Landscape for Giant Pandas in the Minshan Mountains,China

Abstract: The giant panda (Ailuropoda melanoleuca), is one of the world's most endangered species. Habitat loss and fragmentation have reduced its numbers, shrunk its distribution, and separated the population into isolated subpopulations. Such isolated, small populations are in danger of extinction due to random demographic factors and inbreeding. We used least‐cost modeling as a systematic approach to incorporate satellite imagery and data on ecological and behavioral parameters of the giant panda collected during more than 10 years of field research to design a conservation landscape for giant pandas in the Minshan Mountains. We identified 8 core habitats and 4 potential linkages that would link core habitats CH3, CH4, and CH5 with core habitats CH6, CH7, and CH8. Establishing and integrating the identified habitats with existing reserves would create an efficient reserve network for giant panda conservation. The core habitats had an average density of 4.9 pandas/100 km² and contained approximately 76.6% of the giant panda population. About 45% of the core habitat (3245.4 km²) existed outside the current nature reserves network. Total estimated core habitat decreased between 30.4 and 44.5% with the addition of residential areas and road networks factored into the model. A conservation area for giant panda in the Minshan Mountains should aim to ensure habitat retention and connectivity, improve dispersal potential of corridors, and maintain the evolutionary potential of giant pandas in the face of future environmental changes.     

Decision-support tools for dynamic management

Spatial management is a valuable strategy to advance regional goals for nature conservation, economic development, and human health. One challenge of spatial management is navigating the prioritization of multiple features. This challenge becomes more pronounced in dynamic management scenarios, in which boundaries are flexible in space and time in response to changing biological, environmental, or socioeconomic conditions. To implement dynamic management, decision-support tools are needed to guide spatial prioritization as feature distributions shift under changing conditions. Marxan is a widely applied decision-support tool designed for static management scenarios, but its utility in dynamic management has not been evaluated. EcoCast is a new decision-support tool developed explicitly for the dynamic management of multiple features, but it lacks some of Marxan's functionality. We used a hindcast analysis to compare the capacity of these 2 tools to prioritize 4 marine species in a dynamic management scenario for fisheries sustainability. We successfully configured Marxan to operate dynamically on a daily time scale to resemble EcoCast. The relationship between EcoCast solutions and the underlying species distributions was more linear and less noisy, whereas Marxan solutions had more contrast between waters that were good and poor to fish. Neither decision-support tool clearly outperformed the other; the appropriateness of each depends on management purpose, resource-manager preference, and technological capacity of tool developers. Article impact statement: Marxan can function as a decision-support tool for dynamic management scenarios in which boundaries are flexible in space and time.