Testing a Spatially Explicit, Individual-Based Model of Red-Cockaded Woodpecker Population Dynamics

Schiegg, Karin; Walters, Jeffrey R.; Priddy, Jeffery A.

doi:10.1890/04-1473

Cited by 19 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously existing models depicting RCW population dynamics have included no information on the species response to landscape features, portraying dispersal as simple functions of distance (Letcher et al 1998, Schiegg et al 2002, Pasinelli et al 2004, Schiegg et al 2005. Our results show that RCW movement is strongly inhibited by open areas.…”

Section: Contributions To Knowledge Of Dispersal Behavior For Each Spmentioning

confidence: 76%

“…Using radio telemetry, we confirmed that juvenile female RCWs conduct prospecting behavior with extensive forays from their natal territory before settling on a single breeding site . Previous models depicting RCW dispersal behavior have failed to account for this prospecting behavior and for long-distance dispersal events (Letcher et al 1998, Daniels and Walters 2000, Schiegg et al 2002, Pasinelli et al 2004, Schiegg et al 2005 and, thus, may provide unrealistic depiction of RCW dispersal behavior. Despite the extensive RCW monitoring and widespread fragmentation of the longleaf pine ecosystem, because dispersal behavior was poorly known before this project, connectivity has yet to be quantified for RCW populations.…”

Section: Red-cockaded Woodpeckermentioning

confidence: 99%

“…Incorporation of detailed dispersal dynamics into Spatially Explicit Population Models (SEPMs) could allow researchers to investigate the population-dynamic consequences of limited dispersal and evaluate the influence of different habitat management options on population persistence and recovery. This has been done previously for RCWs in the Fort Bragg landscape, but the SPM employed did not include effects of the landscape on movement, but rather portrayed dispersal as a simple function of distance (Walters andCrowder 2002, Scheigg andWalters 2005). The detailed dispersal dynamics we documented for RCWs were incorporated into this SEPM as part of RC-1472.…”

Section: Limitations and Next Stepsmentioning

confidence: 99%

See 2 more Smart Citations

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations

Moody¹,

Haddad²,

Morris³

et al. 2011

View full text Add to dashboard Cite

LIST OF ACRONYMS ABB Appalachian Brown Butterfly (surrogate species for SFS) ACUB Army Compatible Use Buffer Program AIC Akaiki Information Criterion DEM Digital Elevation Model DoD Department of Defense EPA Environmental Protection Agency GAM Generalized Additive Model GAP Gap Analysis Program LiDAR Light Detection and Ranging NCSCP North Carolina Sandhills Conservation Partnership NLCD National Land Cover Dataset NWI National Wetland Inventory RCW Red Cockaded Woodpecker

show abstract

Section: Contributions To Knowledge Of Dispersal Behavior For Each Spmentioning

confidence: 76%

Section: Red-cockaded Woodpeckermentioning

confidence: 99%

Section: Limitations and Next Stepsmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations

Moody¹,

Haddad²,

Morris³

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Applying the models to real landscapes to compare simulated and observed population dynamics may be especially instructive in this regard (e.g. Lindenmayer et al 2003, Schiegg et al 2005). Sensitivity analysis is also useful in directing attention to the parameters that matter most to model performance.…”

Section: How Field Biologists Can Interact With Modelers To Improve Cmentioning

confidence: 99%

Modeling Approaches in Avian Conservation and the Role of Field Biologists

Beissinger¹,

Walters²,

Catanzaro³

et al. 2006

Ornithological Monographs

Self Cite

View full text Add to dashboard Cite

This article is a U.S. government work, and is not subject to copyright in the United States. From the EditorLike many members of the American Ornithologists' Union, I developed a passion for birds early in life and have been trying to make a living from this passion ever since. As you get to know fellow AOU members, particularly over drinks, you hear stories about that moment in their lives when they discovered this incredible feeling about birds that could not be ignored. Some of us started at age six or seven, others in high school or college.Some of us, lacking the artistic skills of David Sibley or the humor of Pete Dunne, have had to fall back on doing research. Our field work allows us to earn a paycheck in pursuit of our passion. We get to see and catch and count birds in what are sometimes exciting, and hopefully always interesting, places. We are paid to write reports, scientific papers, and books that present our results from the field. In many cases, our work has direct or indirect implications for bird conservation.Unfortunately, we cannot simply write papers about anything that catches our fancy. Rather, we need to justify our work on the basis of prior studies and present our results using up-to-date methodologies. For many of us, this is where the ugly concepts of hypotheses, models, and statistics come into play and turn our lives of passion into actual work and sometimes drudgery. During my career, the profession has become more and more based on the testing of models, usually with associated quantitative measures. I took a single statistics course during eight years of college; my graduate students take at least three or four, and often more. One even has a Master's degree in statistics to go along with her MS in ecology. More and more, our field work is only as good as the model being tested, and the test only as good as the calculations provided.Of course, doing field work requires a lot of skill, though usually of the "natural history" category that involves details about birds, plants, and so forth. Doing modeling and developing statistical methods obviously requires a high comfort level with conceptual thinking and mathematics. Few ornithologists are experts in both areas; field people are always trying to figure out what is currently the best way to measure their area of specialty, while modelers are trying to provide methods for the analytical questions posed by the field people. The goal of both groups is to develop the best science possible, with the appropriate scientific models tested with the best quantitative techniques.All these areas within ornithological research change over time, so it is difficult for field people to stay current with the latest models and for modelers to ensure that field people are providing the best data possible and analyzing it properly. Ornithological Monograph No. 59 addresses this problem by serving as a bridge between field biologists and modelers. It provides a state-of-the-art review by a set of experts of the models they consider most relevant to...

show abstract

“…There are two broad categories of IBMs. In demographic-based IBMs, empirically determined demographic parameters are ascribed to individual animals whose aggregate demographic responses are used to predict how the population as a whole will be affected by a change in its environment (e.g., Stephens et al 2002, Schiegg et al 2005 of such models are rare but are, in any case, essentially interpolations because they use empirically determined demographic relationships to predict population behavior in more or less the same environments from which the empirical relationships had been established in the first place (e.g., Schiegg et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Test of a Behavior-Based Individual-Based Model: Response of Shorebird Mortality to Habitat Loss

Goss‐Custard

Burton

Clark

et al. 2006

Ecological Applications

View full text Add to dashboard Cite

In behavior-based individual-based models (IBMs), demographic functions are emergent properties of the model and are not built into the model structure itself, as is the case with the more widely used demography-based IBMs. Our behavior-based IBM represents the physiology and behavioral decision making of individual animals and, from that, predicts how many survive the winter nonbreeding season, an important component of fitness. This paper provides the first test of such a model by predicting the change in winter mortality of a charadriid shorebird following removal of intertidal feeding habitat, the main effect of which was to increase bird density. After adjusting one calibration parameter to the level required to replicate the observed mortality rate before habitat loss, the model predicted that mortality would increase by 3.65%, which compares well with the observed increase of 3.17%. The implication that mortality was density-dependent was confirmed by predicting mortality over a range of bird densities. Further simulations showed that the density dependence was due to an increase in both interference and depletion competition as bird density increased. Other simulations suggested that an additional area of mudflat, equivalent to only 10% of the area that had been lost, would be needed by way of mitigation to return mortality to its original level. Being situated at a high shore level with the flow of water in and out impeded by inlet pipes, the mitigating mudflat would be accessible to birds when all mudflats in the estuary were covered at high tide, thus providing the birds with extra feeding time and not just a small replacement mudflat. Apart from providing the first, and confidence-raising, test of a behavior-based IBM, the results suggest (1) that the chosen calibration procedure was effective; (2) that where no new fieldwork is required, and despite being parameter rich, a behavior-based IBM can be parameterized quickly (few weeks), and thus cheaply, because so many of the parameter values can be obtained from the literature and are embedded in the model; and (3) that behavior-based IBMs can be used to explore system behavior (e.g., the role of depletion competition and interference competition in density-dependent mortality).

show abstract

Testing a Spatially Explicit, Individual-Based Model of Red-Cockaded Woodpecker Population Dynamics

Cited by 19 publications

References 31 publications

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations

Mapping Habitat Connectivity for Multiple Rare, Threatened, and Endangered Species on and Around Military Installations

Modeling Approaches in Avian Conservation and the Role of Field Biologists

Test of a Behavior-Based Individual-Based Model: Response of Shorebird Mortality to Habitat Loss

Contact Info

Product

Resources

About