Dynamic‐landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Bonnot, Thomas W.; Thompson, Frank R.; Millspaugh, Joshua J.

doi:10.1002/ecs2.1890

Cited by 16 publications

(17 citation statements)

References 81 publications

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“…3). of these interactions, responses of regional wildlife populations to landscape processes such as climate change and urbanization and the conservation strategies that address them are actually quite complex and sometimes counterintuitive (Bonnot et al 2013, Bonnot et al 2017.…”

Section: Landis Pro Outputsmentioning

confidence: 99%

“…DLMPs have been used to predict how bird populations might respond to forest management and restoration actions (Bonnot et al 2013, Wintle et al 2005. Recently, DLMPs have incorporated the effects of climate change on future forests and landscapes to model how regional wildlife populations might respond to changes in their habitat (Bonnot et al 2017, Wang et al 2015Fig. 4).…”

Section: Dynamic-landscape Metapopulation Models (Dlmps)mentioning

confidence: 99%

“…To develop a scenario that incorporated projections of the future landscape, we prioritized catchments based on projected landcover data for 2100. Future landcover was derived by combining data from recent climate, landscape, and urbanization models, which we describe in more detail below (Bonnot et al 2017, Terando et al 2014, Wang et al 2015. The future prioritization also incorporated projected impacts of climate change on forest conditions that complemented restoration efforts.…”

Section: Alternativesmentioning

confidence: 99%

“…We expanded on the methods by Bonnot et al (2017) to derive habitat variables from LANDIS PRO output through geoprocessing in ArcGIS 10.4 (ESRI 2013). We characterized NLCD land cover classes by comparing the relative importance values estimated by LANDIS PRO for deciduous versus coniferous species and incorporating probabilities of urbanization (Table 2).…”

Section: Alternativesmentioning

confidence: 99%

“…Finally, we applied dispersal models to estimate cell-based movements of dispersing individuals to the surrounding landscape based on species-specific functions of distance between cells, weighted by K of the destination cell (Bonnot et al 2017). Thus, future changes in dispersal patterns reflected shifts in the distribution of focal species in the region.…”

Section: Consequencesmentioning

confidence: 99%

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Developing a decision-support process for landscape conservation design

Bonnot¹,

Jones-Farrand²,

Thompson

et al. 2019

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Planning for sustainable landscapes is hampered by uncertainty in how species will respond to conservation actions amidst impacts from landscape and climate change. Planning decisions, including tradeoffs among competing species objectives, are complex. We developed a decision-support framework that integrates dynamic-landscape metapopulation models (DLMPs) and structured decision making (SDM) to help guide landscape conservation design. With this framework, we demonstrated that planning for viable populations across broad scales can be achieved under global change. Furthermore, the integration of DLMPs with SDM enabled decisions to be more objective and transparent, and thus, more defensible. Cover Art Illustration of integrating dynamic-landscape metapopulation models with structured decision making. Background photo of Ozark forest at the Baskett Wildlife Research and Education Center by Kyle Spradley, MU College of Agriculture, Food & Natural Resources, Flickr Creative Commons. Quality Assurance This publication conforms to the Northern Research Station's Quality Assurance Implementation Plan which requires technical and policy review for all scientific publications produced or funded by the Station. The process included a blind technical review by at least two reviewers, who were selected by the Assistant Director for Research and unknown to the author. This review policy promotes the Forest Service guiding principles of using the best scientific knowledge, striving for quality and excellence, maintaining high ethical and professional standards, and being responsible and accountable for what we do.

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Section: Landis Pro Outputsmentioning

confidence: 99%

Section: Dynamic-landscape Metapopulation Models (Dlmps)mentioning

confidence: 99%

Section: Alternativesmentioning

confidence: 99%

Section: Alternativesmentioning

confidence: 99%

Section: Consequencesmentioning

confidence: 99%

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Developing a decision-support process for landscape conservation design

Bonnot¹,

Jones-Farrand²,

Thompson

et al. 2019

Self Cite

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A spatially explicit, multi‐scale occupancy model for large‐scale population monitoring

Crosby

Porter

2018

J Wildl Manag

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One of the continuing challenges in wildlife ecology and management is the ability to obtain reliable estimates of species’ distributions at large spatial extents. Multi‐scale occupancy models using a cluster sampling design offer the opportunity to increase the resolution of estimates and model processes occurring at multiple spatial scales, increasing the efficiency of large‐scale monitoring and mitigating the tradeoff between extent and grain. However, accounting for spatial correlation among subsamples in a way that allows for the addition of covariates remains an issue. Using tracking transect surveys for carnivores as an example, we describe and evaluate a hierarchical, multi‐scale occupancy model that integrates existing approaches to estimate occupancy at multiple spatial scales simultaneously, and uses a conditional autoregressive (CAR) process to account for spatial correlation in use between subsamples. We evaluated 3 versions of the model under a single‐survey and a multi‐survey sampling design: a non‐spatial model, a model that accounted for spatial correlation in use between transect segments, and a model that also accounted for spatial correlation in the detection process. Simulations showed that accounting for spatial correlation gave better estimates of transect‐level occupancy under both sampling designs, whereas accurate estimates of segment‐level use required a multi‐survey design. When applied to historical snow track data, the differences in estimates among models followed the same pattern found in the simulations. The multi‐survey design was able to detect equivalent declines in segment use with much less survey effort than the single‐survey design. The modeling framework presented here offers researchers and managers a powerful tool for monitoring populations at large spatial extents while being able to detect ecologically important dynamics at finer spatial scales. © 2018 The Wildlife Society.

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Optimal Strategies for Managing Wildlife Harvest Under System Change

Tucker

Runge

2021

J Wildl Manag

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Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non‐stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non‐stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate ( r ) or carrying capacity ( K ) and derive time‐dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which either the optimal policy or a stationary policy is applied. When K declines over time, the stationary policy leads to an underharvest of the population, resulting in less harvest over the short term but leaving the population in a higher‐value state. When r declines over time, the stationary policy leads to overharvest, resulting in greater harvest returns in the short term but leaving the population in a lower and potentially more vulnerable state. This work demonstrates the basic properties of time‐dependent harvest management and provides a framework for evaluating the many outstanding questions about optimal management strategies under climate change. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.

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Dynamic‐landscape metapopulation models predict complex response of wildlife populations to climate and landscape change

Cited by 16 publications

References 81 publications

Developing a decision-support process for landscape conservation design

Developing a decision-support process for landscape conservation design

A spatially explicit, multi‐scale occupancy model for large‐scale population monitoring

Optimal Strategies for Managing Wildlife Harvest Under System Change

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