Abstract Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout ( Oncorhynchus clarkii clarkii ) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species.
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Improving inferences about private land conservation by accounting for incomplete reporting
Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade‐offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social–ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.
more » « less- Award ID(s):
- 1757324
- NSF-PAR ID:
- 10449570
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Conservation Biology
- Volume:
- 35
- Issue:
- 4
- ISSN:
- 0888-8892
- Format(s):
- Medium: X Size: p. 1174-1185
- Size(s):
- ["p. 1174-1185"]
- Sponsoring Org:
- National Science Foundation
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Abstract Conserving species' ability to traverse the landscape is vital for maintaining biodiversity in the face of global change. Connectivity conservation requires identifying important pathways for species' movements and aligning societal support for conservation of those pathways. Contemporary connectivity analyses emphasize the impacts of topography, vegetation and human footprint on species' movements; but largely ignore the role that attitudes, economics and institutions play in practitioners' ability to conserve those movements.
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Our analysis of five potential corridor locations based on biophysical costs revealed substantial differences in the social costs associated with implementing each corridor despite relatively minimal differences in the biophysical costs.
Our comparison of hypothetical interventions to reduce implementation resistance illustrates that interventions that reduce conflicts between land use and wolves may substantially reduce overall resistance, those reductions are not as well aligned with connectivity priorities as those resulting from changes in land management agency policy.
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Abstract In recent decades, there has been an increasing emphasis on proactive efforts to conserve species being considered for listing under the U.S. Endangered Species Act (ESA) before they are listed (i.e., preemptive conservation). These efforts, which depend on voluntary actions by public and private land managers across the species’ range, aim to conserve species while avoiding regulatory costs associated with ESA listing. We collected data for a set of social, economic, environmental, and institutional factors that we hypothesized would influence voluntary decisions to promote or inhibit preemptive conservation of species under consideration for ESA listing. We used logistic regression to estimate the association of these factors with preemptive conservation outcomes based on data for a set of species that entered the ESA listing process and were either officially listed (
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Abstract Aim Species distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks.
Innovation Here we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (
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Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstractmore » « less
