Contrasted impacts of weather conditions in species sensitive to both survival and fecundity: A montane bird case study

Canonne, Coline; Bernard‐Laurent, A.; Souchay, Guillaume; Perrot, Charlotte; Besnard, Aurélien

doi:10.1002/ecy.3932

Cited by 6 publications

(6 citation statements)

References 55 publications

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“…Studies using LTREs to decompose variance in realized growth rates with IPMs have focused on contributions from variation in vital rates, even when environmental predictors have been included in models (e.g. Canonne et al, 2023;Nater et al, 2022). We show how realized LTREs can be used to also decompose variation into contributions from environmental factors, and to assess the overall contribution of environmental versus demographic stochasticity, two conceptual extensions of the approach of Koons et al (2016).…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Partitioning variance in population growth for models with environmental and demographic stochasticity

Knape¹,

Paquet²,

Arlt³

et al. 2023

Preprint

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1. How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual ‘realized’ growth rates have gained in popularity.2. Realized LTREs have been used particularly to understand how variation in vital rates translate into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking.3. We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.4. We illustrate how to use the approach in an IPM for data from a 26 year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50-120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.5. The case study suggest that variation in first year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Partitioning variance in population growth for models with environmental and demographic stochasticity

Knape¹,

Paquet²,

Arlt³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Transient LTREs have previously been used mainly to decompose variance in realized growth rates into contributions from vital rates and age-structure, even when environmental predictors have been included in models (e.g. Canonne et al, 2023;Nater et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Transient LTREs have previously been used mainly to decompose variance in realized growth rates into contributions from vital rates and age‐structure, even when environmental predictors have been included in models (e.g. Canonne et al, 2023; Nater et al, 2022). We show how transient LTREs can be used to also decompose the variation in vital rates into contributions from environmental factors and density dependence, and to assess the overall contribution of environmental versus demographic stochasticity, two conceptual extensions of the approach of Koons et al (2016).…”

Section: Discussionmentioning

confidence: 99%

“…A large coefficient does, however, not necessarily imply a large impact on the growth rate if the sensitivity of the vital rate is low. Similarly, opposing directions of effects of the same environmental variable on multiple vital rates can lead to unclear resultant effects on the growth rate (Canonne et al, 2023). Investigations into how environmental variation translates into variation in growth have been done with asymptotic LTREs (Caswell, 2001;Davison et al, 2013) The aim of this paper is to explore a decomposition of variation in realized growth rates into contributions from environmental factors and density dependence acting via the vital rates, extending the methods of Koons et al (2016).…”

Section: Introductionmentioning

confidence: 99%

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Partitioning variance in population growth for models with environmental and demographic stochasticity

Knape

Paquet

Arlt

et al. 2023

Journal of Animal Ecology

View full text Add to dashboard Cite

How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual ‘realized’ growth rates using ‘transient’ LTREs have gained in popularity. Transient LTREs have been used particularly to understand how variation in vital rates translate into variation in growth for populations under long‐term study. For these, complete population models may be constructed to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. We extend transient LTREs to further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows one to compare the resultant effect on the growth rate of different environmental factors, as well as density dependence, which may each act via multiple vital rates. We also show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses. We illustrate these extensions with an integrated population model (IPM) for data from a 26 years study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density and unexplained random variation via multiple vital rates, and from demographic stochasticity. The case study suggests that variation in first year survival via the unexplained random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.

show abstract