Climatic conditions produce contrasting influences on demographic traits in a long‐distance Arctic migrant

Cleasby, Ian R.; Bodey, Thomas W.; Vigfúsdóttir, Freydís; McDonald, Jenni L.; McElwaine, Graham; Mackie, Kerry; Colhoun, Kendrew; Bearhop, Stuart

doi:10.1111/1365-2656.12623

Cited by 30 publications

(34 citation statements)

References 58 publications

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“…For aquatic as well as terrestrial species, temporal variation in average annual and seasonal weather has been linked to changes in population abundance (Pardikes et al 2015, Kanno et al 2017, vital rates (Dybala et al 2013, Abadi et al 2017, and population dynamics (Seegrist and Gard 1972, Fern andez-Chac on et al 2011, Grossman et al 2016, Cleasby et al 2017. However, responses of animal populations may be more closely related to short-duration, high-magnitude extreme events or disturbances which exceed a biological threshold above or below which animals have reduced fitness (i.e., survival and reproductive success; Resh et al 1988, Lake 2003, Roland and Matter 2013, Childress and Letcher 2017.…”

Section: Introductionmentioning

confidence: 99%

Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance

2018

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To manage ecosystems in the context of climate change, we need to understand the relationship between extreme events and population dynamics. Floods and droughts are projected to occur more frequently, but how aquatic species will respond to these extreme events remains uncertain. Based on counts of Brook Trout (Salvelinus fontinalis) collected over 28 yr at 115 sites in Shenandoah National Park, we developed mixed-effects models to (1) assess how well extreme streamflow, as compared to mean flows and total precipitation, can explain young-of-the-year (YOY) abundance, (2) identify potential nonlinear relationships between seasonal environmental covariates and abundance using nonlinear generalized additive mixed models, and (3) explore likely impacts of expected future weather and streamflow conditions. We found that (1) using covariates of streamflow extremes improved prediction of YOY abundance compared to use of mean seasonal flow values or precipitation as a proxy, (2) warmer maximum daily spring temperatures were associated with increased YOY abundance up to about 1.5 standard deviations, above which abundance declined, and (3) a strong negative effect of extreme winter streamflow, unlikely to be offset by possibly positive effects from other seasons, is expected to have a detrimental impact on Brook Trout populations given predicted increases in winter precipitation. Because YOY abundance is a strong determinant of population dynamics for these short-lived species, extreme events will have the potential to exert a strong influence on population persistence of Brook Trout in a changing climate. Management actions that maximize resiliency of populations in response to extreme events, such as restoration of habitat connectivity, should be prioritized to buffer negative impacts.

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Section: Introductionmentioning

confidence: 99%

Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance

2018

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“…, Cleasby et al. ) allowing estimation of the dynamics of each sex separately and its influence on population dynamics.…”

Section: Classical Ipms Structured By Age Sex and Reproductive Statementioning

confidence: 99%

“…However, males and females may have different life-history strategies and thus different demographic rates (Caswell and Weeks 1986, Rankin and Kokko 2007, V eran and Beissinger 2009). To date, only a handful of published IPMs have included a sex structure (Tavecchia et al 2009, Fieberg et al 2010, P eron et al 2012, Cleasby et al 2017) allowing estimation of the dynamics of each sex separately and its influence on population dynamics.…”

Section: Classical Ipms Structured By Age Sex and Reproductive Statementioning

confidence: 99%

“…, Cleasby et al. ). In this classic case, the demographic model of the IPM is an age/stage‐structured projection model (matrix models, Caswell ) typically including a low number of stages that are known to influence demographic rates and hence population dynamics (Coulson et al.…”

Section: Introductionmentioning

confidence: 99%

“…Until now, most IPMs have assumed that demographic rates of all individuals vary with some categorical attributes, often called stages, such as age, sex, or reproductive status (P eron et al 2012, Schaub et al 2013, Weegman et al 2016, Cleasby et al 2017. In this classic case, the demographic model of the IPM is an age/stage-structured projection model (matrix models, Caswell 2001) typically including a low number of stages that are known to influence demographic rates and hence population dynamics (Coulson et al 2001).…”

Section: Introductionmentioning

confidence: 99%

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Integrated population models: powerful methods to embed individual processes in population dynamics models

Plard

Fay

Kéry

et al. 2019

Ecology

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Population dynamics models have long assumed that populations are composed of a restricted number of groups, where individuals in each group have identical demographic rates and where all groups are similarly affected by density‐dependent and ‐independent effects. However, individuals usually vary tremendously in performance and in their sensitivity to environmental conditions or resource limitation, such that individual contributions to population growth will be highly variable. Recent efforts to integrate individual processes in population models open up new opportunities for the study of eco‐evolutionary processes, such as the density‐dependent influence of environmental conditions on the evolution of morphological, behavioral, and life‐history traits. We review recent advances that demonstrate how including individual mechanisms in models of population dynamics contributes to a better understanding of the drivers of population dynamics within the framework of integrated population models (IPMs). IPMs allow for the integration in a single inferential framework of different data types as well as variable population structure including sex, social group, or territory, all of which can be formulated to include individual‐level processes. Through a series of examples, we first show how IPMs can be beneficial for getting more accurate estimates of demographic traits than classic matrix population models by including basic population structure and their influence on population dynamics. Second, the integration of individual‐ and population‐level data allows estimating density‐dependent effects along with their inherent uncertainty by directly using the population structure and size to feedback on demography. Third, we show how IPMs can be used to study the influence of the dynamics of continuous individual traits and individual quality on population dynamics. We conclude by discussing the benefits and limitations of IPMs for integrating data at different spatial, temporal, and organismal levels to build more mechanistic models of population dynamics.

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Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models

Weegman

Arnold

Clark³

et al. 2021

Ecological Applications

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Integrated population models (IPMs) are widely used to combine disparate data sets in joint analysis to better understand population dynamics and provide guidance for conservation activities. An often-cited assumption of IPMs is independence among component data sets within the combined likelihood. Dependency among data sets should lead to underestimation of variance and bias because individuals contribute data to more than one data set. In practice, studied individuals often occur in multiple data sets in IPMs (i.e., overlap), which is one way for the independence assumption to be violated. Such cases have the potential to dissuade practitioners and limit application of IPMs to solve emerging ecological problems. We assessed precision and bias of demographic rates estimated from IPMs using a complete gradient (0-100%) of overlap among data sets, wide ranges in demographic rates (e.g., survival 0.1-0.8) and sample sizes (100-1,200 individuals) and variable data sources. We compared results from our simulations with those from IPMs constructed using empirical data on tree swallows (Tachycineta bicolor) where data sets either had complete overlap or included different individuals. Contrary to previous investigators, we found no substantive bias or uncertainty in any demographic rate from IPMs derived from data sets with complete overlap. While variability in demographic rates was greater at low sample sizes (i.e., low capture, recapture, and survey probabilities), there were negligible differences in the posterior mean or root mean square error of demographic rates among IPMs with strong dependence vs. complete independence among data sets. Our simulations suggest IPMs can be designed using only capturerecapture data or harvest and capture-recovery data where population estimates are obtained from the same data as survival and productivity data. While we encourage researchers to carefully consider the modeling approach best suited for their data sets, our results suggest that dependence among data sets does not generally compromise IPM estimates. Thus, violation of the independence assumption should not dissuade researchers from the application of IPMs in ecological research.

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Climatic conditions produce contrasting influences on demographic traits in a long‐distance Arctic migrant

Cited by 30 publications

References 58 publications

Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance

Seasonal streamflow extremes are key drivers of Brook Trout young‐of‐the‐year abundance

Integrated population models: powerful methods to embed individual processes in population dynamics models

Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models

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