Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models

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

doi:10.1016/j.ecolmodel.2017.07.019

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…We included fixed effects ( β lat and β long ) to account for possible clinal effects of standardized banding site ( b ) latitude and longitude. ε s/t/b are residual random effects of year nested within stratum ( s/t ) to account for residual temporal variation (Zhao, Boomer, Silverman, & Fleming, ), and banding site nested within year and stratum ( s/t/b ) to account for fine‐scale habitat selection or site‐specific variation in capturing methods at different sites (i.e. large moulting wetlands that attract a preponderance of adult females, or use of dive‐trapping methods that result in higher capture rates of juveniles).…”

Section: Methodsmentioning

confidence: 99%

Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator–prey dynamics

Specht

Arnold

2018

Journal of Applied Ecology

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Fecundity estimates for demographic modelling are difficult to acquire at the regional spatial scales that correspond to climate shifts, land use impacts or habitat management programmes. Yet they are important for evaluating such effects. While waterfowl managers have historically used harvest‐based age ratios to assess fecundity at continental scales, widely available age ratios from late summer banding (ringing) data present an underutilized opportunity to examine a regional fecundity index with broad temporal replication. We used age ratios from banding data and hierarchical mixed‐effect models to examine how fecundity of five North American dabbling duck species was affected by temporal variation in hydrological cycles, intra‐ and interspecific density dependence and alternate prey availability, and whether those relationships were consistent across a broad geographic area. Model‐estimated fecundity was within the range of traditional harvest‐based fecundity estimates for each species. Ecological covariates explained between 16% and 53% of the temporal variation in fecundity, dependent on species. Increasing wetland inundation and an indicator of vole population irruptions were consistent predictors of increasing fecundity across all species. Species exhibited mixed positive and negative responses to interspecific and intraspecific breeding pair densities hypothesized to affect nest and brood survival respectively, highlighting the importance of integrating brood survival into fecundity metrics for precocial species. Declines in fecundity over time and across space at more northern latitudes may reflect stronger policies for grassland and wetland protection in the U.S. versus Canadian portions of the prairies over the time period of our study. Maintaining the capacity of less permanent basins to rehydrate in wetter periods through easement protection benefits fecundity, particularly for late‐nesting species that acquire a greater proportion of their reproductive energy on the breeding grounds. Synthesis and applications. Age ratios derived from postbreeding banding operations allowed us to attribute variation in waterfowl fecundity to temporal ecological variables. Effects of habitat management for waterfowl may be masked unless analysts account for this temporal variation. Postbreeding‐pulse age ratios at capture could be useful as fecundity metrics in integrated population models and for evaluating population dynamics of extensively banded nongame species, especially if adjusted for capture vulnerability using within‐season recapture data.

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Section: Methodsmentioning

confidence: 99%

Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator–prey dynamics

Specht

Arnold

2018

Journal of Applied Ecology

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“…We reviewed previous studies for the giant panda and the two bamboo species (Hull et al., ; Pan et al. ; Wang et al., ; Zhang et al., , ), and identified abiotic (e.g., elevation and slope), biotic (e.g., bamboo presence for giant panda), and anthropogenic (e.g., road transportation and construction for giant panda) variables that have been shown to affect their occupancy (Supporting Information Table S2). We used a 30‐m resolution digital elevation model (Global ASTER, ) to delineate the slope, aspect, and terrain ruggedness using ArcToolbox in ArcGIS 10.2 (ESRI, ).…”

Section: Methodsmentioning

confidence: 99%

“…While incorporating biotic traits such as slow migration can improve the performance of SDMs in mapping species' realized niche spaces (Botkin et al, 2007), data representing biotic interactions and biotic traits may not always be available, and residual SAC needs to be accounted for. Failure to account for SAC can lead to overstated predictions of species' habitat loss when extrapolated to future conditions (Crase, Liedloff, Vesk, Fukuda, & Wintle, 2014;Zhao, Boomer, Silverman, & Fleming, 2017).…”

Section: Introductionmentioning

confidence: 99%

Incorporating biotic interactions reveals potential climate tolerance of giant pandas

Wang

Zhao

McShea

et al. 2018

CONSERVATION LETTERS

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Many studies have overestimated species' range shifts under climate change because they treat climate as the only determinant while ignoring biotic factors. To assess the response of giant pandas to climate change, we incorporated spatial effects in modeling bamboo distributions, which in turn was incorporated to represent giant pandabamboo biotic interactions in predicting giant panda distribution. Our study revealed potential tolerance of giant pandas to climate change. We found significant residual spatial correlation in the bamboo models. The biotic interactions with bamboo understories and anthropogenic activities had large effects on panda distribution, which lowered the relative importance of climatic variables. Our results are fundamentally different from previous studies that used climate-only and nonspatial approaches, which may have overestimated the effects of climate change on panda and lead to inappropriate conservation recommendations. We strongly advocate that giant panda conservation planning continues to focus on protecting bamboo forest and reducing anthropogenic interferences. K E Y W O R D Sbamboo, biotic

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“…Population and habitat management needs to account for large-scale system shifts associated with climate change, and management decision-making often relies on predictions of population status (Clark et al 2001, Petchey et al 2015. So far the efforts of predicting waterfowl population responses to climate change mainly rely on population growth models that do not incorporate demographic information (Zhao et al 2016, Zhao et al 2017a). Here we presented an integrated modeling approach that provided understanding of relationships among climate, density dependent processes, population demography and dynamics and predict population responses to climate change.…”

Section: Conservation Implicationsmentioning

confidence: 99%

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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Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models

Cited by 5 publications

References 46 publications

Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator–prey dynamics

Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator–prey dynamics

Incorporating biotic interactions reveals potential climate tolerance of giant pandas

Integrated modeling predicts shifts in waterbird population dynamics under climate change

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