Forecasting waterfowl population dynamics under climate change — Does the spatial variation of density dependence and environmental effects matter?

Zhao, Qing; Silverman, Emily; Fleming, Kathy; Boomer, G. Scott

doi:10.1016/j.biocon.2015.12.006

Cited by 29 publications

(31 citation statements)

References 36 publications

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“…We calculated total winter (January to April) precipitation and mean spring (March to April) temperature for each reference area to represent regional climate conditions. We chose winter precipitation and spring temperature because these climate variables were shown to drive breeding season wetland habitat conditions (Zhao et al 2016), and thus are likely to influence waterfowl demography (Nichols et al 1982, Osnas et al 2016.…”

Section: Climate Datamentioning

confidence: 99%

“…Populations of waterbirds, including waterfowl, are considered to be vulnerable to climate change (Van de Pol et al 2010, Lehikoinen et al 2013 due to the fact that their key habitat, wetlands, are sensitive to climate conditions (Larson 1995, Sofaer et al 2016). Furthermore, the vulnerability/ resilience of populations to climate change may vary across space, due to the spatial variation of climatic effects on populations (Zhao et al 2016). Therefore, the long-term monitoring and management program of North American waterfowl populations (Smith 1995, US Fish andWildlife Service 2018) is challenged to accounting for system shifts associated with climate change (Nichols et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the long-term monitoring and management program of North American waterfowl populations (Smith 1995, US Fish andWildlife Service 2018) is challenged to accounting for system shifts associated with climate change (Nichols et al 2011). While the longterm monitoring program provides a myriad of information on the demography and population size of these species, the demographic basis of potential shifts in waterfowl population dynamics under climate change are not fully understood (Zhao et al 2016). A modeling framework that assumes separate demographic stochasticity for each demographic state variable, integrates demography and population information, and accounts for uncertainty from multiple sources is needed to understand and predict the responses of these waterfowl populations and make appropriate conservation management decision in the face of climate change.…”

Section: Introductionmentioning

confidence: 99%

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Integrated modeling predicts shifts in waterbird population dynamics under climate change

Zhao

Boomer

Royle³

2019

Ecography

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Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in‐depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N‐mixture models for large scale population data. We then analyzed four decades (1972–2013) of mallard Anas platyrhynchos breeding population survey, band‐recovery and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.

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Section: Climate Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated modeling predicts shifts in waterbird population dynamics under climate change

Zhao

Boomer

Royle³

2019

Ecography

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“…Improvements to surveys and population models also may be required as science‐based information on environmental factors influencing waterfowl migration chronology, and timing and locales of settling on breeding sites become available (Cowardin and Blohm , Austin et al , Mallory et al , Finger et al ). Evaluations of the WBPHS have become increasingly important because responses (e.g., migration chronology and timing of settling) to climate change are likely to vary among species (Gurney et al , Drever et al , Notaro et al , Osnas et al , Zhao et al ).…”

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confidence: 99%

Evaluating the waterfowl breeding population and habitat survey for scaup

et al. 2018

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Potential bias in breeding population estimates of certain duck species from the Waterfowl Breeding Population and Habitat Survey (WBPHS) has been a concern for decades. The WBPHS does not differentiate between lesser (Aythya affinis) and greater (A. marila) scaup, but lesser scaup comprise 89% of the combined scaup population and their population estimates are suspected to be biased. We marked female lesser scaup (i.e., marked scaup) in the Mississippi and Atlantic Flyways, Canada and United States, with implantable satellite transmitters to track their spring migration through the traditional and eastern survey areas of the WBPHS, 2005–2010. Our goal was to use data independent of the WBPHS to evaluate whether breeding population estimates for scaup were biased and identify variables that might be used in the future to refine population estimates. We found that the WBPHS estimates of breeding scaup are biased because, across years, only 30% of our marked scaup had settled for the breeding period when the strata in which they settled were surveyed, 43% were available to be counted in multiple survey strata as their migration continued during the WBPHS, 32% settled outside the WBPHS area, the number of times a marked scaup was available to be counted by survey crews varied positively with the latitude that a marked scaup settled on breeding areas, the probability of a marked scaup being in a stratum while it was surveyed varied among years, and these probabilities were positively correlated with the traditional and eastern breeding population estimates for scaup. Annual population estimates derived from banding data provide a less biased and preferable method of monitoring scaup population status and trend. Development of models that include metrics such as survey stratum latitude and annual spring environmental conditions might potentially be used to improve scaup breeding population estimates derived from the WBPHS, but independent estimates from banding data would be important to evaluate such models. © 2018 The Wildlife Society.

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“…Often, the directions of differences between model-based predictions and observations provide clues to the sorts of new model components that may be needed. Temporal changes in predictive abilities of models (e.g., becoming less predictive over time) may indicate the need for additional model components that deal with global change (e.g., Nichols et al, 2011;Zhao, Silverman, Fleming, & Boomer, 2016). Periodic (1)…”

Section: Box 1 Model Weight Updating With Bayes' Theoremmentioning

confidence: 99%

Accumulating evidence in ecology: Once is not enough

Nichols

Kendall

Boomer

2019

Ecology and Evolution

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Many published studies in ecological science are viewed as stand‐alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the “reproducibility crisis” in science. The key step in science is the comparison of hypothesis‐based predictions with observations, where the predictions are typically generated by hypothesis‐specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta‐analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are preadapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.

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Forecasting waterfowl population dynamics under climate change — Does the spatial variation of density dependence and environmental effects matter?

Cited by 29 publications

References 36 publications

Integrated modeling predicts shifts in waterbird population dynamics under climate change

Integrated modeling predicts shifts in waterbird population dynamics under climate change

Evaluating the waterfowl breeding population and habitat survey for scaup

Accumulating evidence in ecology: Once is not enough

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