Detrimental impacts of climate change may be exacerbated by density‐dependent population regulation in blue mussels

Jaatinen, Kim; Westerbom, Mats; Norkko, Alf; Mustonen, Olli; Koons, David N.

doi:10.1111/1365-2656.13377

Cited by 16 publications

(34 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For other species, like the blue mussels Mytilus trossulus described in Jaatinen et al. (2021), it is impossible to track individuals and estimate demographic rates. In these instances, time series of abundance can be used to understand how population density, in addition to abiotic factors, drive changes in population size.…”

Section: Figurementioning

confidence: 99%

“…Jaatinen et al. (2021) monitored population densities of blue mussels at six locations off the southern coast of Finland over a 17‐year period. They used these data to explore how sea surface temperature and salinity, along with density‐dependent processes, affected population dynamics of the keystone species.…”

Section: Figurementioning

confidence: 99%

“…Jaatinen et al. (2021) then projected how mussel populations are likely to respond to future increases in temperature and decreases in salinity, exploring a range of values inspired by regional climate projections from an ensemble of GCMs (Meier et al., 2012). The forecasts accounted for many sources of uncertainty: emission scenarios (in a simplified way, with trends in mean salinity and temperature values), parameter uncertainty (using samples from Bayesian posterior distributions) and process variance.…”

Section: Figurementioning

confidence: 99%

“…Jaatinen et al. 's (2021) approach could be used in analyses of other species to evaluate the extent to which species traits and/or environmental conditions influence the outcome of interactions between density‐dependent processes and climate variables.…”

Section: Figurementioning

confidence: 99%

“…Even when demographic data are not available, population models could account for density‐dependent effects (as Jaatinen et al. 2021 did) or use count data to characterize underlying demographic processes (Dail & Madsen, 2011; Zipkin et al., 2014). Yet, increased model complexity may not always improve population forecasts, and could even decrease the accuracy of forecasts if responses to abiotic factors are sufficiently variable across time or space (Rollinson et al., 2021).…”

Section: Figurementioning

confidence: 99%

See 4 more Smart Citations

Accounting for sources of uncertainty when forecasting population responses to climate change

Zylstra

Zipkin

2021

Journal of Animal Ecology

View full text Add to dashboard Cite

In Focus: Jaatinen, K., Westerbom, M., Norkko, A., Mustonen, O., & Koons, D. N. (2021). Detrimental impacts of climate change may be exacerbated by density‐dependent population regulation in blue mussels. Journal of Animal Ecology, 90, 562–573, https://doi.org/10.1111/1365-2656.13377. Conservation strategies for threatened species are increasingly dependent on forecasts of population responses to climate change. For such forecasts to be accurate, they must account for multiple sources of uncertainty, including those associated with projections of future climate scenarios and those associated with the models used to describe population dynamics. While many population forecasts incorporate parameter uncertainty in abiotic effects and process variance related to unexplained temporal variation, most forecasts overlook the importance of evaluating uncertainty in the structure of the population model itself. By accounting for structural uncertainties in a model of population growth for blue mussels, Jaatinen et al. (2021) demonstrated that density‐dependent processes are likely to exacerbate adverse effects of climate change and reduce population viability of this keystone species. These findings highlight the importance of incorporating structural unknowns in population forecasts and the value of approaches that account for multiple sources of climate and model uncertainties. Forecasts that capture a range of possible population trajectories under climate change will help ensure efficient allocation of limited conservation resources.

show abstract

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 3 more Smart Citations

Accounting for sources of uncertainty when forecasting population responses to climate change

Zylstra

Zipkin

2021

Journal of Animal Ecology

View full text Add to dashboard Cite

show abstract

Fitting individual‐based models of spatial population dynamics to long‐term monitoring data

Malchow,

Fandos,

Kormann

et al. 2024

Ecological Applications

View full text Add to dashboard Cite

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process‐based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long‐term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual‐based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial‐block cross‐validation scheme and assess predictive performance with a rank‐correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

show abstract

Food resource uncertainty shapes the fitness consequences of early spring onset in capital and income breeding migratory birds

Ejsmond

2022

Ecology and Evolution

View full text Add to dashboard Cite

Due to climate change, the timing of spring arrival and nesting onset in many migratory bird species have advanced. Earlier spring onsets prolong the available breeding period but can also deteriorate local conditions, leading to increased temporal variation in resource availability. This interaction between phenological shifts in nesting onset and short-term temporal variation in food gain has unknown consequences for fitness of migratory bird species. We model two contrasting breeding strategies to investigate the fitness consequences of stochastically fluctuating food gain and storing of energetic reserves for reproduction. The model was inspired by the biology of common eiders (Somateria mollissima), which store extensive reserves prior to egg laying and incubation (capital breeding strategy), and king eiders (S. spectabilis), which continue to forage during nesting (income breeding strategy). For capital breeders, foraging prior to breeding increases energy reserves and clutch size, but for both strategies, postponing nesting reduces the chances of recruitment. We found that in scenarios with early spring onset, the average number of recruits produced by capital breeders was higher under conditions of stochastic rather than deterministic food gain. This is because under highly variable daily food gain, individuals successful in obtaining food can produce large clutches early in the season. However, income breeders do not build up reserve buffers; consequently, their fitness is always reduced, when food availability fluctuates. For both modeled strategies, resource uncertainty had only a minor effect on the timing of nesting onset. Our work shows that the fitness consequences of global changes in breeding season onset depend on the level of uncertainty in food intake and the degree to which reserves are used to fuel the reproductive effort. We predict that among migratory bird species producing one clutch per year, capital breeders are more resilient to climate-induced changes in spring phenology than income breeders.

show abstract

Detrimental impacts of climate change may be exacerbated by density‐dependent population regulation in blue mussels

Cited by 16 publications

References 87 publications

Accounting for sources of uncertainty when forecasting population responses to climate change

Accounting for sources of uncertainty when forecasting population responses to climate change

Fitting individual‐based models of spatial population dynamics to long‐term monitoring data

Food resource uncertainty shapes the fitness consequences of early spring onset in capital and income breeding migratory birds

Contact Info

Product

Resources

About