Capturing Spatiotemporal Patterns in Presence-Absence Data to Inform Monitoring and Sampling Designs for the Threatened Dakota Skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States

Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence-absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.

Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China

Climate change significantly influences changes in ecological phenomena and processes, such as species distribution and phenology, thus accelerating the rate of species extinction or prosperity. Climate change is considered to be one of the most important threats to global biodiversity in the 21st century and will pose significant challenges to biodiversity conservation in the future. The use of niche modelling to predict changes in the suitable distribution of species under climate change scenarios is becoming a hot topic of biological conservation. In this study, we use data from China’s National Forest Continuous Inventory as well as specimen collection data of Cunninghamia lanceolata (Lamb.) Hook to run optimized Maxent models to predict potential suitable distribution of the species in the present day, 2050s, and 2070s under different climate change scenarios in China. In the modeling process, the most important uncorrelated variables were chosen, and the sample-size-adjusted Akaike information criterion (AICc) was used to select the optimal combination of feature type and regularization multiplier. Variable selection reduced the number of variables used and the complexity of the model, and the use of the AICc reduced overfitting. Variables relating to precipitation were more important than temperature variables in predicting C. lanceolata distribution in the optimal model. The predicted suitable distribution areas of C. lanceolata were different for the different periods under different climate change scenarios, with the centroids showing a degree of northward movement. The suitable distribution area is predicted to become more fragmented in the future. Our results reveal the climate conditions required for the suitable distribution of C. lanceolata in China and the likely changes to its distribution pattern in the future, providing a scientific basis for the sustainable management, protection, and restoration of the suitable habitat of this economically important tree species in the context of climate change.

Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage-grouse management

Predictive species distributional models are a cornerstone of wildlife conservation planning. Constructing such models requires robust underpinning science that integrates formerly disparate data types to achieve effective species management.

Greater sage‐grouse Centrocercus urophasianus, hereafter ‘sage‐grouse’ populations are declining throughout sagebrush‐steppe ecosystems in North America, particularly within the Great Basin, which heightens the need for novel management tools that maximize the use of available information.

Herein, we improve upon existing species distribution models by combining information about sage‐grouse habitat quality, distribution and abundance from multiple data sources. To measure habitat, we created spatially explicit maps depicting habitat selection indices (HSI) informed by >35 500 independent telemetry locations from >1600 sage‐grouse collected over 15 years across much of the Great Basin. These indices were derived from models that accounted for selection at different spatial scales and seasons. A region‐wide HSI was calculated using the HSI surfaces modelled for 12 independent subregions and then demarcated into distinct habitat quality classes.

We also employed a novel index to describe landscape patterns of sage‐grouse abundance and space use (AUI). The AUI is a probabilistic composite of the following: (i) breeding density patterns based on the spatial configuration of breeding leks and associated trends in male attendance; and (ii) year‐round patterns of space use indexed by the decreasing probability of use with increasing distance to leks. The continuous AUI surface was then reclassified into two classes representing high and low/no use and abundance.

Synthesis and applications. Using the example of sage‐grouse, we demonstrate how the joint application of indices of habitat selection, abundance and space use derived from multiple data sources yields a composite map that can guide effective allocation of management intensity across multiple spatial scales. As applied to sage‐grouse, the composite map identifies spatially explicit management categories within sagebrush steppe that are most critical to sustaining sage‐grouse populations as well as those areas where changes in land use would likely have minimal impact. Importantly, collaborative efforts among stakeholders guide which intersections of habitat selection indices and abundance and space use classes are used to define management categories. Because sage‐grouse are an umbrella species, our joint‐index modelling approach can help target effective conservation for other sagebrush obligate species and can be readily applied to species in other ecosystems with similar life histories, such as central‐placed breeding.

Using the example of sage‐grouse, we demonstrate how the joint application of indices of habitat selection, abundance and space use derived from multiple data sources yields a composite map that can guide effective allocation of management intensity across multiple spatial scales. As applied to sage‐grouse, the composite map identifies spatially explicit management categories within sagebrush steppe that are most critical to sustaining sage‐grouse populations as well as those areas where changes in land use would likely have minimal impact. Importantly, collaborative efforts among stakeholders guide which intersections of habitat selection indices and abundance and space use classes are used to define management categories. Because sage‐grouse are an umbrella species, our joint‐index modelling approach can help target effective conservation for other sagebrush obligate species and can be readily applied to species in other ecosystems with similar life histories, such as central‐placed breeding.

The seascape of demersal fish nursery areas in the North Mediterranean Sea, a first step towards the implementation of spatial planning for trawl fisheries

The identification of nursery grounds and other essential fish habitats of exploited stocks is a key requirement for the development of spatial conservation planning aimed at reducing the adverse impact of fishing on the exploited populations and ecosystems. The reduction in juvenile mortality is particularly relevant in the Mediterranean and is considered as one of the main prerequisites for the future sustainability of trawl fisheries. The distribution of nursery areas of 11 important commercial species of demersal fish and shellfish was analysed in the European Union Mediterranean waters using time series of bottom trawl survey data with the aim of identifying the most persistent recruitment areas. A high interspecific spatial overlap between nursery areas was mainly found along the shelf break of many different sectors of the Northern Mediterranean indicating a high potential for the implementation of conservation measures. Overlap of the nursery grounds with existing spatial fisheries management measures and trawl fisheries restricted areas was also investigated. Spatial analyses revealed considerable variation depending on species and associated habitat/depth preferences with increased protection seen in coastal nurseries and minimal protection seen for deeper nurseries (e.g. Parapenaeus longirostris 6%). This is partly attributed to existing environmental policy instruments (e.g. Habitats Directive and Mediterranean Regulation EC 1967/2006) aiming at minimising impacts on coastal priority habitats such as seagrass, coralligenous and maerl beds. The new knowledge on the distribution and persistence of demersal nurseries provided in this study can support the application of spatial conservation measures, such as the designation of no-take Marine Protected Areas in EU Mediterranean waters and their inclusion in a conservation network. The establishment of no-take zones will be consistent with the objectives of the Common Fisheries Policy applying the ecosystem approach to fisheries management and with the requirements of the Marine Strategy Framework Directive to maintain or achieve seafloor integrity and good environmental status.

Predicting potential European bison habitat across its former range

Habitat loss threatens large mammals worldwide, and their survival will depend on habitat in human-dominated landscapes. Conservation planners thus face the challenge to identify areas of least conflict with land use, yet broadscale species distribution models rarely incorporate real landscape patterns nor do they identify potential conservation conflicts. An excellent example of such conservation challenges is provided by European bison (Bison bonasus). Almost extinct by the early 20th century, bison can only survive in the wild if large metapopulations are established, but it is unclear where new herds can be reintroduced. Using European bison as an example we conducted a continental-scale habitat assessment based on real landscape patterns. Our specific aims here were to (1) map European bison habitat throughout the species' former range, (2) examine whether broadscale habitat suitability factors differ from previously reported fine-scale factors, and (3) assess where suitable habitat occurs in areas with low potential for conflict with land use. We assessed habitat suitability using herd range maps for all 36 free-ranging European bison herds as habitat use data. Habitat suitability maps were compared with maps of land cover, livestock density, agricultural constraints, and protected areas to assess potential conservation conflicts. Our models had high goodness of fit (AUC = 0.941), and we found abundant potential bison habitat. European bison prefer mosaic-type landscapes, with a preference for broad-leaved and mixed forests. European bison metapopulations do not appear to be limited by habitat availability. However, most potential habitat occurred outside protected areas and has substantial potential for conservation conflicts. The most promising areas for establishing large bison metapopulations all occur in Eastern Europe (i.e., the Carpathians, the Belarus-Ukraine borderlands, and several regions in European Russia). The future of European bison and that of other large mammals in the wild thus clearly lies in Eastern Europe, because habitat there is most abundant and least fragmented, and because the potential for conflict with land use is lower. More generally we suggest that broadscale habitat assessments that incorporate land use can be powerful tools for conservation planning and will be key if large herbivore and carnivore conservation is to succeed in a human-dominated world.

Is the climate right for pleistocene rewilding? Using species distribution models to extrapolate climatic suitability for mammals across continents

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial "Pleistocene rewilding" proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.