Animal habitat selection is an important and expansive area of research in ecology. In particular, the study of habitat selection is critical in habitat prioritization efforts for species of conservation concern. Landscape planning for species is happening at ever‐increasing extents because of the appreciation for the role of landscape‐scale patterns in species persistence coupled to improved datasets for species and habitats, and the expanding and intensifying footprint of human land uses on the landscape. We present a large‐scale collaborative effort to develop habitat selection models across large landscapes and multiple seasons for prioritizing habitat for a species of conservation concern. Greater sage‐grouse (Centrocercus urophasianus, hereafter sage‐grouse) occur in western semi‐arid landscapes in North America. Range‐wide population declines of this species have been documented, and it is currently considered as “warranted but precluded” from listing under the United States Endangered Species Act. Wyoming is predicted to remain a stronghold for sage‐grouse populations and contains approximately 37% of remaining birds. We compiled location data from 14 unique radiotelemetry studies (data collected 1994–2010) and habitat data from high‐quality, biologically relevant, geographic information system (GIS) layers across Wyoming. We developed habitat selection models for greater sage‐grouse across Wyoming for 3 distinct life stages: 1) nesting, 2) summer, and 3) winter. We developed patch and landscape models across 4 extents, producing statewide and regional (southwest, central, northeast) models for Wyoming. Habitat selection varied among regions and seasons, yet preferred habitat attributes generally matched the extensive literature on sage‐grouse seasonal habitat requirements. Across seasons and regions, birds preferred areas with greater percentage sagebrush cover and avoided paved roads, agriculture, and forested areas. Birds consistently preferred areas with higher precipitation in the summer and avoided rugged terrain in the winter. Selection for sagebrush cover varied regionally with stronger selection in the Northeast region, likely because of limited availability, whereas avoidance of paved roads was fairly consistent across regions. We chose resource selection function (RSF) thresholds for each model set (seasonal × regional combination) that delineated important seasonal habitats for sage‐grouse. Each model set showed good validation and discriminatory capabilities within study‐site boundaries. We applied the nesting‐season models to a novel area not included in model development. The percentage of independent nest locations that fell directly within identified important habitat was not overly impressive in the novel area (49%); however, including a 500‐m buffer around important habitat captured 98% of independent nest locations within the novel area. We also used leks and associated peak male counts as a proxy for nesting habitat outside of the study sites used to develop the models. A 1.5...
Citation: Doherty, K. E., J. S. Evans, P. S. Coates, L. M. Juliusson, and B. C. Fedy. 2016. Importance of regional variation in conservation planning: a rangewide example of the Greater Sage-Grouse. Ecosphere 7(10):e01462. 10.1002/ecs2.1462Abstract. We developed rangewide population and habitat models for Greater Sage-Grouse (Centro cercus urophasianus) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4-88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1-85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25-34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.
The influence of study design on the ability to detect the effects of landscape pattern on gene flow is one of the most pressing methodological gaps in landscape genetic research. To investigate the effect of study design on landscape genetics inference, we used a spatially‐explicit, individual‐based program to simulate gene flow in a spatially continuous population inhabiting a landscape with gradual spatial changes in resistance to movement. We simulated a wide range of combinations of number of loci, number of alleles per locus and number of individuals sampled from the population. We assessed how these three aspects of study design influenced the statistical power to successfully identify the generating process among competing hypotheses of isolation‐by‐distance, isolation‐by‐barrier, and isolation‐by‐landscape resistance using a causal modelling approach with partial Mantel tests. We modelled the statistical power to identify the generating process as a response surface for equilibrium and non‐equilibrium conditions after introduction of isolation‐by‐landscape resistance. All three variables (loci, alleles and sampled individuals) affect the power of causal modelling, but to different degrees. Stronger partial Mantel r correlations between landscape distances and genetic distances were found when more loci were used and when loci were more variable, which makes comparisons of effect size between studies difficult. Number of individuals did not affect the accuracy through mean equilibrium partial Mantel r, but larger samples decreased the uncertainty (increasing the precision) of equilibrium partial Mantel r estimates. We conclude that amplifying more (and more variable) loci is likely to increase the power of landscape genetic inferences more than increasing number of individuals.
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