Interrelated impacts of climate and land‐use change on a widespread waterbird

Saunders, Sarah P.; Piper, Walter H.; Farr, Matthew; Bateman, Brooke L.; Michel, Nicole L.; Westerkam, Henrik; Wilsey, Chad B.

doi:10.1111/1365-2656.13444

Cited by 11 publications

(10 citation statements)

References 74 publications

(101 reference statements)

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“…Our results suggest that a lack of environmental effects associated with parameters in IPMs should be simulated before researchers conclude that no relationship exists as there may be insufficient information to identify an ecological relationship. Despite these potential challenges, several recent studies have identified environmental relationships with vital rates using an IPM (e.g., Oppel et al, 2014; Saunders et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Relative effects of sample size, detection probability, and study duration on estimation in integrated population models

Ross

Weegman

2022

Ecological Applications

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Understanding mechanistic causes of population change is critical for managing and conserving species. Integrated population models (IPMs) allow for quantifying population changes while directly relating environmental drivers to vital rates, but power of IPMs to detect trends and environmental effects on vital rates remains understudied. We simulated data for an IPM fewer than 41 scenarios to determine the power to detect trends and environmental effects on vital rates based on study duration, sample size, detection probability, and effect size. Our results indicated that temporal duration of a study and effect size, rather than sample size of each individual data set or detection probability, had the greatest influence on the power to identify trends in adult survival and fecundity. When using only 10 years of data, we were unable to identify a 50% increase in adult survival but were able to identify this increase with 22 years of data. When using only capture-recapture data in a traditional Cormack-Jolly-Seber analysis, we lacked sufficient power to identify trends in survival, and power of the Cormack-Jolly-Seber model was always less than the IPM. The IPM had greater power to identify trends and environmental effects on fecundity (e.g., we detected a 58% change in fecundity using 12 years of data). Models with effects of environmental variables on vital rates had less power than trends, likely to be due to increased annual variation in the vital rate when modeling responses to environmental effects that varied by year. Lack of power in the Cormack-Jolly-Seber analysis could be due to the relatively small variability in adult survival compared with fecundity, given the life history of our simulated species. As interannual variation in environmental conditions will probably increase with climate change, this type of analysis can help to inform the study duration needed, which may be a shifting target given future climate uncertainty and the complex nature of environmental correlations with demography.

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Section: Discussionmentioning

confidence: 99%

Relative effects of sample size, detection probability, and study duration on estimation in integrated population models

Ross

Weegman

2022

Ecological Applications

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“…A feature of many of the reviewed studies was the use of chosen habitat quality proxy variable(s) as inputs into sophisticated ecological analyses, such as habitat suitability models (e.g., Guan et al, 2016;Hsu et al, 2014;Tang et al, 2016;Wen et al, 2016), carrying capacity estimates (e.g., Collazo et al, 2002;Mu et al, 2022;Stillman et al, 2000), population viability analyses (e.g., Mattsson et al, 2012;Saunders et al, 2021;Weegman et al, 2022), or to predict the outcome of management interventions on waterbird vital rates (e.g., Beerens et al, 2015;Toral et al, 2012). Where the predictor variables for these analytical approaches covered large spatial extents or could be sourced from extensive temporal archives, knowledge gained from site-level studies could be extrapolated in individually marked birds, limited spatial scale, and substantial costs (Buderman et al, 2020).…”

Section: Applicationsmentioning

confidence: 99%

Measuring habitat quality for waterbirds: A review

Mott

Prowse

Jackson

et al. 2023

Ecology and Evolution

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Quantifying habitat quality is dependent on measuring a site's relative contribution to population growth rate. This is challenging for studies of waterbirds, whose high mobility can decouple demographic rates from local habitat conditions and make sustained monitoring of individuals near‐impossible. To overcome these challenges, biologists have used many direct and indirect proxies of waterbird habitat quality. However, consensus on what methods are most appropriate for a given scenario is lacking. We undertook a structured literature review of the methods used to quantify waterbird habitat quality, and provide a synthesis of the context‐dependent strengths and limitations of those methods. Our search of the Web of Science and Scopus databases returned a sample of 666 studies, upon which our review was based. The reviewed studies assessed habitat quality by either measuring habitat attributes (e.g., food abundance, water quality, vegetation structure), or measuring attributes of the waterbirds themselves (e.g., demographic parameters, body condition, behavior, distribution). Measuring habitat attributes, although they are only indirectly related to demographic rates, has the advantage of being unaffected by waterbird behavioral stochasticity. Conversely, waterbird‐derived measures (e.g., body condition, peck rates) may be more directly related to demographic rates than habitat variables, but may be subject to greater stochastic variation (e.g., behavioral change due to presence of conspecifics). Therefore, caution is needed to ensure that the measured variable does influence waterbird demographic rates. This assumption was usually based on ecological theory rather than empirical evidence. Our review highlighted that there is no single best, universally applicable method to quantify waterbird habitat quality. Individual project specifics (e.g., time frame, spatial scale, funding) will influence the choice of variables measured. Where possible, practitioners should measure variables most directly related to demographic rates. Generally, measuring multiple variables yields a better chance of accurately capturing the relationship between habitat characteristics and demographic rates.

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“…Twenty‐three bird species showed a significant association with NAO − (Table 2), in studies from Canada, the USA, the UK, Norway, Sweden, Denmark, Romania, and Spain (Jonzen et al ., 2002; Almaraz & Amat, 2004; Olsen & Schmidt 2004; Grosbois & Thompson, 2005; Votier et al ., 2005; Weatherhead, 2005; Anders & Post, 2006; Zydelis et al ., 2006; Figuerola, 2007; Balbontin et al ., 2009; Goodenough, Elliot & Hart, 2009; Lewis et al ., 2009; Pearce‐Higgins et al ., 2009; Gaston & Robertson, 2010; Bustnes et al ., 2013; Jensen et al ., 2014; Veit & Manne, 2015; Guery et al ., 2017; Schimpf et al ., 2020; Baltag, Kovacs & Sfica, 2021; Saunders et al ., 2021; Table S1). They spanned diverse taxa from loons, cormorants, and eiders to New World blackbirds and Old World finches.…”

Section: Impact Of Naomentioning

confidence: 99%

“…Movements of organisms such as seasonal bird migrations create teleconnection (strong links) among distant regions, and these processes at the broad scale can interact with processes at local scales, resulting in non‐linear dynamics (Heffernan et al ., 2014). Migratory birds are highly susceptible to shifts of broad‐scale climate; there is evidence for changes to breeding [fecundity, clutch size, breeding probability, provision of food for offspring, fledging success, and nesting success (Ancona et al ., 2011; Tompkins & Anderson 2021)], food sources or prey abundance (Pardo et al ., 2013; Saunders et al ., 2021; Tompkins & Anderson 2021), survival [differential survival according to age, changes in migratory tactics and foraging costs (Dugger et al ., 2016; Guery et al ., 2017; Tjornlov et al ., 2020)], and phenology [hatching date and laying dates of birds, or plant and insect phenology (Lorentsen et al ., 2015; Gonzalez‐Braojos et al ., 2017; Morganti et al ., 2019)]. Thus, it is important to investigate how bottom‐up chain effects link broad‐scale and local‐scale climate effects, and to disentangle the independent effects of broad‐ and local‐scale climate.…”

Section: Perspectivesmentioning

confidence: 99%

Broad‐scale climate variation drives the dynamics of animal populations: a global multi‐taxa analysis

Wan

Holyoak²,

Yan

et al. 2022

Biological Reviews

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Climate is a major extrinsic factor affecting the population dynamics of many organisms. The Broad-Scale Climate Hypothesis (BSCH) was proposed by Elton to explain the large-scale synchronous population cycles of animals, but the extent of support and whether it differs among taxa and geographical regions is unclear. We reviewed publications examining the relationship between the population dynamics of multiple taxa worldwide and the two most commonly used broad-scale climate indices, El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Our review and synthesis (based on 561 species from 221 papers) reveals that population changes of mammals, birds and insects are strongly affected by major oceanic shifts or irregular oceanic changes, particularly in ENSO-and NAO-influenced regions (Pacific and Atlantic, respectively), providing clear evidence supporting Elton's BSCH. Mammal and insect populations tended to increase during positive ENSO phases. Bird populations tended to increase in positive NAO phases. Some species showed dual associations with both positive and negative phases of the same climate index (ENSO or NAO). These findings indicate that some taxa or regions are more or less vulnerable to climate fluctuations and that some geographical areas show multiple weather effects related to ENSO or NAO phases. Beyond confirming that animal populations are influenced by broad-scale climate variation, we document extensive patterns of variation among taxa and observe that the direct biotic and abiotic mechanisms for these broad-scale climate factors affecting animal populations are very poorly understood. A practical implication of our research is that changes in ENSO or NAO can be used as early signals for pest management and wildlife conservation. We advocate integrative studies at both broad and local scales to unravel the omnipresent effects of climate on animal populations to help address the challenge of conserving biodiversity in this era of accelerated climate change.

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Interrelated impacts of climate and land‐use change on a widespread waterbird

Cited by 11 publications

References 74 publications

Relative effects of sample size, detection probability, and study duration on estimation in integrated population models

Relative effects of sample size, detection probability, and study duration on estimation in integrated population models

Measuring habitat quality for waterbirds: A review

Broad‐scale climate variation drives the dynamics of animal populations: a global multi‐taxa analysis

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