Dataset Information

Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

ABSTRACT: Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distance to wells, distance to water, NDVI, land cover, distance to roads, terrain shape and fence presence to parameterize the models. We then used the output of these models as cost surfaces to compare two common connectivity models, least-cost modeling (LCM) and circuit theory. Using pronghorn movement data from spring and fall migrations, we identified potential migration corridors by combining each habitat suitability model with each connectivity model. The best performing model combination was Maxent with LCM corridors across both seasons. Maxent out-performed expert-based habitat suitability models for both spring and fall migrations. However, expert-based corridors can perform relatively well and are a cost-effective alternative if species location data are unavailable. Corridors created using LCM out-performed circuit theory, as measured by the number of pronghorn GPS locations present within the corridors. We suggest the use of a tiered approach using different corridor widths for prioritizing conservation and mitigation actions, such as fence removal or conservation easements.

SUBMITTER: Poor EE

PROVIDER: S-EPMC3500376 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

Poor Erin E EE Loucks Colby C Jakes Andrew A Urban Dean L DL

PloS one 20121116 11

Terrestrial long-distance migrations are declining globally: in North America, nearly 75% have been lost. Yet there has been limited research comparing habitat suitability and connectivity models to identify migration corridors across increasingly fragmented landscapes. Here we use pronghorn (Antilocapra americana) migrations in prairie habitat to compare two types of models that identify habitat suitability: maximum entropy (Maxent) and expert-based (Analytic Hierarchy Process). We used distanc ...[more]

PMID: 23166656

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Project description:We studied the habitat selection of pronghorn (Antilocapra americana) during seasonal migration; an important period in an animal's annual cycle associated with broad-scale movements. We further decompose our understanding of migration habitat itself as the product of both broad- and fine-scale behavioral decisions and take a multi-scale approach to assess pronghorn spring and fall migration across the transboundary Northern Sagebrush Steppe region. We used a hierarchical habitat selection framework to assess a suite of natural and anthropogenic features that have been shown to influence selection patterns of pronghorn at both broad (migratory neighborhood) and fine (migratory pathway) scales. We then combined single-scale predictions into a scale-integrated step selection function (ISSF) map to assess its effectiveness in predicting migration route habitat. During spring, pronghorn selected for native grasslands, areas of high forage productivity (NDVI), and avoided human activity (i.e., roads and oil and natural gas wells). During fall, pronghorn selected for native grasslands, larger streams and rivers, and avoided roads. We detected avoidance of paved roads, unpaved roads, and wells at broad spatial scales, but no response to these features at fine scales. In other words, migratory pronghorn responded more strongly to anthropogenic features when selecting a broad neighborhood through which to migrate than when selecting individual steps along their migratory pathway. Our results demonstrate that scales of migratory route selection are hierarchically nested within each other from broader (second-order) to finer scales (third-order). In addition, we found other variables during particular migratory periods (i.e., native grasslands in spring) were selected for across scales indicating their importance for pronghorn. The mapping of ungulate migration habitat is a topic of high conservation relevance. In some applications, corridors are mapped according to telemetry location data from a sample of animals, with the assumption that the sample adequately represents habitat for the entire population. Our use of multi-scale modelling to predict resource selection during migration shows promise and may offer another relevant alternative for use in future conservation planning and land management decisions where telemetry-based sampling is unavailable or incomplete.

Project description:To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black-backed Woodpeckers (Picoides arcticus; a burned-forest specialist) at 20 recently (?6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D (2) models) and various combinations of four environmental variables describing burn severity, the north-south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled "ensemble predictions," consisting of the number of models (0-8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., "no-analogue") environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black-backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned-forest landscapes where black-backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large-scale disturbance specialists.

Project description:Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's < 0.05). Models trained on task data outperformed models trained on rest data. Pearson's correlation and accordance features generally showed a small numerical advantage over discordance features, while PLS regression models were usually better than linear regression models. Overall, in addition to correlation features combined with linear models (Rosenberg et al., 2016a), it is useful to consider accordance features and PLS regression for CPM.

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Dataset Information

Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

Publications

Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations.

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