Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls

Schumaker, Nathan H.; Brookes, Allen; Dunk, Jeffrey R.; Woodbridge, Brian; Heinrichs, Julie A.; Lawler, Joshua J.; Carroll, Carlos; LaPlante, David W.

doi:10.1007/s10980-014-0004-4

Cited by 48 publications

(43 citation statements)

References 28 publications

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“…Patches were deemed sources if local births exceeded deaths and sinks if deaths exceeded births over a 100-year period. Movement among patches was accounted for in this metric by the individuals' subsequent contribution to the local birth or death tally, and using this construct, births-deaths approximates emigrationimmigration at a steady state (Schumaker et al 2014). To assess the system-wide strength of source-sink dynamics, we also developed a second metric called productivity disparity, calculated as the difference between the average productivity (b-d) values of all sources and the average productivity values of all sinks in each landscape.…”

Section: Source-sink Metrics and Analysismentioning

confidence: 99%

Intrinsic and extrinsic drivers of source–sink dynamics

Heinrichs

Lawler

Schumaker

2016

Ecology and Evolution

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SummaryMany factors affect the presence and exchange of individuals among subpopulations and influence not only the emergence, but the strength of ensuing source–sink dynamics within metapopulations. Yet their relative contributions remain largely unexplored. To help identify the characteristics of empirical systems that are likely to exhibit strong versus weak source–sink dynamics and inform their differential management, we compared the relative roles of influential factors in strengthening source–sink dynamics. In a series of controlled experiments within a spatially explicit individual‐based model framework, we varied patch quality, patch size, the dispersion of high‐ and low‐quality patches, population growth rates, dispersal distances, and environmental stochasticity in a factorial design. We then recorded source–sink dynamics that emerged from the simulated habitat and population factors. Long‐term differences in births and deaths were quantified for sources and sinks in each system and used in a statistical model to rank the influences of key factors. Our results suggest that systems with species capable of rapid growth, occupying habitat patches with more disparate qualities, with interspersed higher‐ and lower‐quality habitats, and that experience relatively stable environments (i.e., fewer negative perturbations) are more likely to exhibit strong source–sink dynamics. Strong source–sink dynamics emerged under diverse combinations of factors, suggesting that simple inferences of process from pattern will likely be inadequate to predict and assess the strength of source–sink dynamics. Our results also suggest that it may be more difficult to detect and accurately measure source–sink dynamics in slow‐growing populations, highly variable environments, and where a subtle gradient of habitat quality exists.

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Section: Source-sink Metrics and Analysismentioning

confidence: 99%

Intrinsic and extrinsic drivers of source–sink dynamics

Heinrichs

Lawler

Schumaker

2016

Ecology and Evolution

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“…We are currently evaluating the future repercussions of continued deforestation in this system using spatially explicit models developed with HexSim (http://www.HexSim.net; Schumaker et al. ) to better understand the genetic and demographic outlook for Pfrimer's Parakeet.…”

Section: Phylogeography and Landscape Geneticsmentioning

confidence: 99%

“…Given continued deforestation in the region, genetic structure patterns are only expected to become stronger in the future. We are currently evaluating the future repercussions of continued deforestation in this system using spatially explicit models developed with HexSim (www.HexSim.net; Schumaker et al 2014) to better understand the genetic and demographic outlook for Pfrimer's Parakeet.…”

Section: Phylogeography and Landscape Geneticsmentioning

confidence: 99%

The conservation genetics juggling act: integrating genetics and ecology, science and policy

Haig

Miller

Bellinger

et al. 2015

Evolutionary Applications

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The field of conservation genetics, when properly implemented, is a constant juggling act integrating molecular genetics, ecology, and demography with applied aspects concerning managing declining species or implementing conservation laws and policies. This young field has grown substantially since the 1980s following the development of polymerase chain reaction and now into the genomics era. Our laboratory has ‘grown up’ with the field, having worked on these issues for over three decades. Our multidisciplinary approach entails understanding the behavior and ecology of species as well as the underlying processes that contribute to genetic viability. Taking this holistic approach provides a comprehensive understanding of factors that influence species persistence and evolutionary potential while considering annual challenges that occur throughout their life cycle. As a federal laboratory, we are often addressing the needs of the U.S. Fish and Wildlife Service in their efforts to list, de‐list, or recover species. Nevertheless, there remains an overall communication gap between research geneticists and biologists who are charged with implementing their results. Therefore, we outline the need for a National Center for Small Population Biology to ameliorate this problem and provide organizations charged with making status decisions firmer ground from which to make their critical decisions.

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“…With Golden Eagles, for example, forecasting population responses to an increasing array (or intensity) of anthropogenic stressors is complicated because individual contributions to population dynamics vary depending on age, social status, and the ability to acquire resources in spatially variable environments. Mechanistic population models provide a means to address this conundrum because: (1) they provide a standardized framework for integrating empirical data with expert knowledge and biological intuition, and (2) they are well suited for developing, testing, and communicating hypotheses and predictions about the relative influence of multiple interacting stressors (Schumaker et al 2014, Stenglein et al 2015, Tuma et al 2016. Additionally, the process of developing a mechanistic population model demands that authors make explicit assumptions about system dynamics, which ultimately enhances transparency within a management context (Grimm 1999, Munns 2006.…”

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confidence: 99%

Spatial Demographic Models to Inform Conservation Planning of Golden Eagles in Renewable Energy Landscapes

Wiens

Schumaker

Inman

et al. 2017

Journal of Raptor Research

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Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land-use and other anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles (). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, USA. We developed a spatially-explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind-turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative impacts. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative risks of proposed management options to Golden Eagles and their habitats.

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Mapping sources, sinks, and connectivity using a simulation model of northern spotted owls

Cited by 48 publications

References 28 publications

Intrinsic and extrinsic drivers of source–sink dynamics

Intrinsic and extrinsic drivers of source–sink dynamics

The conservation genetics juggling act: integrating genetics and ecology, science and policy

Spatial Demographic Models to Inform Conservation Planning of Golden Eagles in Renewable Energy Landscapes

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