Recent exposure to environmental stochasticity does not determine the demographic resilience of natural populations

Cant, James; Capdevila, Pol; Beger, Maria; Salguero‐Gómez, Roberto

doi:10.1111/ele.14234

Cited by 10 publications

(10 citation statements)

References 102 publications

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“…In P. clavata populations exposed and unexposed to MHWs, the main differences in resistance came from progression and stasis. These results contrast, to some extent, with previous studies suggesting that both reproductive and survival demographic processes are important drivers of demographic resistance (Cant et al, 2023; Capdevila, Stott, et al, 2022). Cant et al (2023) observed that greater investment in survival reduces population resistance (they measured first step attenuation instead of its inverse, as done here), while greater reproductive investment increases resistance potential.…”

Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Demographic resilience is tightly linked to the life history of species (Cant et al, 2023; Capdevila, Stott, et al, 2022; Field et al, 2019). For instance, species with slow life history strategies (high investment in survival, low reproduction, and slow growth) often require longer times to recover (Cant et al, 2023; Capdevila, Stott, et al, 2022). In addition, previous studies have shown that there exists a trade-offs in animal species between being highly resistant and recovering fast (Capdevila, Stott, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Marine heatwaves favour resistant Mediterranean octocoral populations at the expense of their speed of recovery

Capdevila,

Zentner,

Rovira

et al. 2023

Preprint

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Abstract1. The effects of climate change are now more pervasive than ever. Marine ecosystems have been particularly impacted by climate change, with Marine Heat Waves (MHWs) being a strong driver of mass mortality events. Even in the most optimistic greenhouse gas emission scenarios, MHWs will continue to increase in frequency, intensity, and duration. For this reason, understanding the resilience of marine species to the increase of MHWs is crucial to predicting their viability under future climatic conditions.2. In this study, we explored the consequences of Marine Heatwaves (MHWs) on the resilience of a Mediterranean key octocoral species,Paramuricea clavata, to further disturbances to their population structure. To quantifyP. clavata’s capacity to resist and recover from future disturbances, we used demographic information collected from 1999 to 2022, from two different sites in the NW Mediterranean Sea.3. Our results showed that the differences in the dynamics of populations exposed and those not exposed to MHWs were driven mostly by differences in mean survivorship and growth. We also showed that after MHWsP. clavatapopulations had slower rates of recovery but did not experience changes in resistance. Populations exposed to MHWs had lower resistance elasticity to progression but higher stasis compared to unexposed populations. In contrast, the only demographic process showing some differences when comparing the speed of recovery elasticity values between populations exposed and unexposed to MHWs was stasis. Finally, under scenarios of increasing frequency of MHWs, the extinction ofP. clavatapopulations will accelerate and their capacity to recover after further disturbances will be hampered.4. Overall, these findings confirm that future climatic conditions will make octocoral populations even more vulnerable to further disturbances. These results highlight the importance of limiting local impacts on marine ecosystems to dampen the consequences of climate change.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Marine heatwaves favour resistant Mediterranean octocoral populations at the expense of their speed of recovery

Capdevila,

Zentner,

Rovira

et al. 2023

Preprint

Self Cite

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“…Comparative analyses often rely on the collation of published data to obtain sufficient sample sizes. There are numerous recent examples of this (Dalgleish et al 2010, Bullock et al 2012, Burns et al 2013, and large-scale collaborative efforts to collate global demographic and life history and related data are increasingly common (Wright et al 2004, Loh et al 2005, Kattge et al 2011, Salguero-Gómez et al 2015, Capdevila et al 2022, Hernández-Yáñez et al 2022, Cant et al 2023. Such databases provide a rich resource for workers focussing on life-history strategies and demographic performance.…”

Section: Introductionmentioning

confidence: 99%

Plant demographic knowledge is biased towards short‐term studies of temperate‐region herbaceous perennials

Römer,

Dahlgren,

Salguero‐Gómez

et al. 2023

Oikos

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Plant demography has a long history resulting in a large knowledge base. Comparative analysis of this information allows exploration of the drivers of demographic patterns globally and the study of life‐history evolution. Studies aiming to generalise demographic patterns rely on data being derived from a representative sample. However, the data are likely to be taxonomically, geographically and methodologically biased. Matrix population models (MPMs) are widely‐used in plant demography, so an assessment of publications using MPMs is a convenient way to assess the distribution of plant demographic knowledge using this modelling approach. We assessed bias in this knowledge using data from the COMPADRE Plant Matrix Database, containing MPMs for > 700 species. We show that tree species and tropical areas are under‐represented, while herbaceous perennials and temperate areas are over‐represented. There is a positive association between the number of studies per country and per capita GDP. Most studies have low spatiotemporal replication with 43% of studies conducted over < four years, and only 17% replicated across > three sites. This limited spatiotemporal coverage means existing data may not represent the environmental conditions the species experience. These biases and knowledge gaps inhibit theory development and limit current utility for identifying useful generalities for management decisions, such as typical responses to climate change. It is likely that similar biases extend to other demographic modelling tools such as integral projection models. We urge researchers to address these biases and close these knowledge gaps.

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“…matrix population models, population modelling, propagation, simulation, uncertainty such as COMPADRE (Salguero-Gómez et al, 2015) and COMADRE (Salguero-Gómez et al, 2016), have greatly contributed to the field of predictive ecological modelling by providing access to thousands of published MPMs and metadata for comparative analyses (Cant et al, 2023;Enríquez et al, 2022;Hernández-Yáñez et al, 2022;Lanuza et al, 2023). However, these repositories do not provide any uncertainty metrics for the underlying parameters of the matrices they hold or their derived quantities, causing uncertainty omission.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty propagation in matrix population models: Gaps, importance and guidelines

Simmonds,

Jones

2023

Methods Ecol Evol

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Matrix population models (MPMs), which describe the demographic behaviour of a population based on age or stage through discrete time, are popular in ecology, evolutionary biology and conservation biology. MPMs provide a tool for guiding management decisions and can give insight into life history trade‐offs, patterns of senescence, transient dynamics and population trajectories. These models are parameterised with estimates of demographic rates (e.g. survival and reproduction) and can have multiple layers of underlying statistical analyses, all of which introduce uncertainty. For accurate and transparent results, this uncertainty should be propagated through to quantities derived from the MPMs, such as population growth rates (λ). However, full propagation is not always achieved, leading to omitted uncertainty and negative consequences for the reliability of inferences drawn. We summarised the contemporary standards regarding demographic rate uncertainty reporting and propagation, by reviewing papers using MPMs from 2010 to 2019. We then used reported uncertainties as the basis for a simulation study to explore the impact of uncertainty omission on inferences drawn from the analysis of MPMs. We simulated four scenarios of demographic rate propagation and evaluated their impact on population growth rate estimates. Although around 78% of MPM papers report some kind of uncertainty in their findings, only half of those report uncertainty in all aspects. Additionally, only 31% of papers fully propagate uncertainty through to derived quantities. Our simulations demonstrate that, even with moderate levels of uncertainty, incomplete propagation introduces bias. Omitting uncertainty may substantially alter conclusions, particularly for results showing small changes in population size. Biased conclusions were most common when uncertainty in the most influential demographic rates for population growth were omitted. We suggest comprehensive guidelines for reporting and propagating uncertainty in MPMs. Standardising methods and reporting will increase the reliability of MPMs and enhance the comparability of different models. These guidelines will improve the accuracy, transparency and reliability of population projections, increasing our confidence in results that can inform conservation efforts, ultimately contributing to biodiversity preservation.

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Recent exposure to environmental stochasticity does not determine the demographic resilience of natural populations

Cited by 10 publications

References 102 publications

Marine heatwaves favour resistant Mediterranean octocoral populations at the expense of their speed of recovery

Marine heatwaves favour resistant Mediterranean octocoral populations at the expense of their speed of recovery

Plant demographic knowledge is biased towards short‐term studies of temperate‐region herbaceous perennials

Uncertainty propagation in matrix population models: Gaps, importance and guidelines

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