Fragmentation effects on an endangered species across a gradient from the interior to edge of its range

Understanding how habitat fragmentation affects individual species is complicated by challenges associated with quantifying species-specific habitat and spatial variability in fragmentation effects within a species' range. We aggregated a 29-year breeding survey data set for the endangered marbled murrelet (Brachyramphus marmoratus) from >42,000 forest sites throughout the Pacific Northwest (Oregon, Washington, and northern California) of the United States. We built a species distribution model (SDM) in which occupied sites were linked with Landsat imagery to quantify murrelet-specific habitat and then used occupancy models to test the hypotheses that fragmentation negatively affects murrelet breeding distribution and that these effects are amplified with distance from the marine foraging habitat toward the edge of the species' nesting range. Murrelet habitat declined in the Pacific Northwest by 20% since 1988, whereas the proportion of habitat comprising edges increased by 17%, indicating increased fragmentation. Furthermore, fragmentation of murrelet habitat at landscape scales (within 2 km of survey stations) negatively affected occupancy of potential breeding sites, and these effects were amplified near the range edge. On the coast, the odds of occupancy decreased by 37% (95% confidence interval [CI] -54 to 12) for each 10% increase in edge habitat (i.e., fragmentation), but at the range edge (88 km inland) these odds decreased by 99% (95% CI 98 to 99). Conversely, odds of murrelet occupancy increased by 31% (95% CI 14 to 52) for each 10% increase in local edge habitat (within 100 m of survey stations). Avoidance of fragmentation at broad scales but use of locally fragmented habitat with reduced quality may help explain the lack of murrelet population recovery. Further, our results emphasize that fragmentation effects can be nuanced, scale dependent, and geographically variable. Awareness of these nuances is critical for developing landscape-level conservation strategies for species experiencing broad-scale habitat loss and fragmentation.

Effects of climate and human activity on the current distribution of amphibians in China

In China, as elsewhere, amphibians are highly endangered. Anthropogenic environmental change has affected the distribution and population dynamics of species, and species distributions at a broad scale are strongly driven by climate and species' ability to disperse. Yet, current knowledge remains limited on how widespread human activity affects the distribution patterns of amphibians in China and whether this effect extends beyond climate. We compiled a relatively comprehensive database on the distribution of 196 amphibian species in China from the literature, public databases, and field data. We obtained 25,826 records on almost 50% of known species in China. To test how environmental factors and human activities influence the current distribution of amphibians (1960-1990), we used range filling, which is species realized ranges relative to their potential climate distribution. We used all species occurrence records to represent realized range and niche models to predict potential distribution range. To reduce uncertainty, we used 3 regression methods (beta regression, generalized boosted regression models, and random forest) to test the associations of species range filling with human activity, climate, topography, and range size. The results of the 3 approaches were consistent. At the species level, mean annual precipitation (climate) had the most effect on spatial distribution pattern of amphibians in China, followed by range size. Human activity ranked last. At the spatial level, mean annual precipitation remained the most important factor. Regions in southeastern of China that are currently moist supported the highest amphibian diversity, but were predicted to experience a decline in precipitation under climate change scenarios. Consequently, the distributions of amphibians will likely shift to the northwest in the future, which could affect future conservation efforts.

Climate suitability as a predictor of conservation translocation failure

The continuing decline and loss of biodiversity has caused an increase in the use of interventionist conservation tools, such as translocation. However, many translocation attempts fail to establish viable populations, with poor release site selection often flagged as an inhibitor of success. We used species distribution models (SDMs) to predict the climate suitability of 102 release sites for amphibians, reptiles, and terrestrial insects and compared suitability predictions between successful and failed attempts. We then quantified the importance of climate suitability relative to 5 other variables frequently considered in the literature as important determinants of translocation success: number of release years, number of individuals released, life stage released, origin of the source population, and position of the release site relative to the species' range. Probability of translocation success increased as predicted climate suitability increased and this effect was the strongest among the variables we considered, accounting for 48.3% of the variation in translocation outcome. These findings should encourage greater consideration of climate suitability when selecting release sites for conservation translocations and we advocate the use of SDMs as an effective way to do this.

Incorporating fragmentation and non-native species into distribution models to inform fluvial fish conservation

Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the range of the fluvial-specialist shoal bass (Micropterus cataractae) continues to decrease, yet how perceived threats have contributed to range loss is largely unknown. We used species distribution models to determine which factors contributed most to shoal bass range loss. We estimated a potential distribution based on natural abiotic factors and a series of currently occupied distributions that incorporated variables characterizing land cover, non-native species, and river fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). We allowed interspecific relationships between non-native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of currently occupied distribution showed that range loss increased as fragmentation intensified. Response curves from models of currently occupied distribution indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non-native congeners, wherein non-natives may be favored at the highest fragmentation intensity. Response curves also suggested that >100 km of interconnected, free-flowing stream fragments were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested that models had favorable predictive and discriminative abilities. Similar approaches that use readily available, diverse, geospatial data sets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats.

Integrated models to support multiobjective ecological restoration decisions

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state-and-transition, species-distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade-offs in management outcomes through integrated modeling and structured decision-support approaches has wide application for situations in which trade-offs exist between competing conservation objectives.

The importance of incorporating functional habitats into conservation planning for highly mobile species in dynamic systems

The distribution of mobile species in dynamic systems can vary greatly over time and space. Estimating their population size and geographic range can be problematic and affect the accuracy of conservation assessments. Scarce data on mobile species and the resources they need can also limit the type of analytical approaches available to derive such estimates. We quantified change in availability and use of key ecological resources required for breeding for a critically endangered nomadic habitat specialist, the Swift Parrot (Lathamus discolor). We compared estimates of occupied habitat derived from dynamic presence-background (i.e., presence-only data) climatic models with estimates derived from dynamic occupancy models that included a direct measure of food availability. We then compared estimates that incorporate fine-resolution spatial data on the availability of key ecological resources (i.e., functional habitats) with more common approaches that focus on broader climatic suitability or vegetation cover (due to the absence of fine-resolution data). The occupancy models produced significantly (P < 0.001) smaller (up to an order of magnitude) and more spatially discrete estimates of the total occupied area than climate-based models. The spatial location and extent of the total area occupied with the occupancy models was highly variable between years (131 and 1498 km² ). Estimates accounting for the area of functional habitats were significantly smaller (2-58% [SD 16]) than estimates based only on the total area occupied. An increase or decrease in the area of one functional habitat (foraging or nesting) did not necessarily correspond to an increase or decrease in the other. Thus, an increase in the extent of occupied area may not equate to improved habitat quality or function. We argue these patterns are typical for mobile resource specialists but often go unnoticed because of limited data over relevant spatial and temporal scales and lack of spatial data on the availability of key resources. Understanding changes in the relative availability of functional habitats is crucial to informing conservation planning and accurately assessing extinction risk for mobile resource specialists.

The value of using feasibility models in systematic conservation planning to predict landholder management uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple-use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders' willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders' decisions to engage in management. To test the utility of our best-supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost-effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost-effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost-efficient and effective manner.

Predicting the spatial distribution of a seabird community to identify priority conservation areas in the Timor Sea

Understanding spatial and temporal variability in the distribution of species is fundamental to the conservation of marine and terrestrial ecosystems. To support strategic decision making aimed at sustainable management of the oceans, such as the establishment of protected areas for marine wildlife, we identified areas predicted to support multispecies seabird aggregations in the Timor Sea. We developed species distribution models for 21 seabird species based on at-sea survey observations from 2000-2013 and oceanographic variables (e.g., bathymetry). We applied 4 statistical modeling techniques and combined the results into an ensemble model with robust performance. The ensemble model predicted the probability of seabird occurrence in areas where few or no surveys had been conducted and demonstrated 3 areas of high seabird richness that varied little between seasons. These were located within 150 km of Adele Island, Ashmore Reef, and the Lacepede Islands, 3 of the largest aggregations of breeding seabirds in Australia. Although these breeding islands were foci for high species richness, model performance was greatest for 3 nonbreeding migratory species that would have been overlooked had regional monitoring been restricted to islands. Our results indicate many seabird hotspots in the Timor Sea occur outside existing reserves (e.g., Ashmore Reef Marine Reserve), where shipping, fisheries, and offshore development likely pose a threat to resident and migratory populations. Our results highlight the need to expand marine spatial planning efforts to ensure biodiversity assets are appropriately represented in marine reserves. Correspondingly, our results support the designation of at least 4 new important bird areas, for example, surrounding Adele Island and Ashmore Reef.

Detecting extinction risk from climate change by IUCN Red List criteria

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.