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

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

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.

Seasonal variation in the balance and strength of cooperative and competitive behavior in patches of blue mussels

Aggregation into groups may affect performance of individuals through the balance and strength of facilitative versus competitive interactions. We studied in situ how seasonal variation in abiotic environment affects this balance for blue mussels, a semi-sessile species. We hypothesize that seasonal variation in stresses and resources affects the strength of the interaction. We expected that, in benign conditions (here: high food availability, medium temperatures, low hydrodynamic stress), performance is dominated by growth and is better at low densities, while at adverse conditions (here: low food availability, low or high temperatures, high hydrodynamic stress), performance is dominated by survival and higher at high densities. Mussels were kept in shallow subtidal exclosures at 10 different densities for a one-month period. This exact procedure was repeated seven times at the same location within a one-year period. We measured development in mussel patch shape, performance, and environmental parameters. Environmental conditions for mussels were most benign in summer and most adverse in winter. Patches developed into less complex shapes at lower densities, but also after stronger hydrodynamic disturbances. Towards summer, mussels became more active, aggregation behavior increased, and interactions became more pronounced. Towards winter, mussels became less active: aggregation behavior and growth rates declined and at the lowest temperatures survival started to decrease with mussel density. Survival and growth (by proxy of mussel condition) were both density-dependent; however, contrary to our expectations we found positive interactions between density and survival at the most benign conditions in summer and negative interactions at the most adverse conditions in winter. In between the two seasons, the strength of the interactions increased towards summer and decreased towards winter following a bell-shaped pattern. This pattern might be explained by the environmental mediated aggregation behavior of the mussels. The obvious seasonal pattern in balance and strength of density-dependent interactions demonstrates that strength and direction of intra-specific interactions are both strongly affected by environmental context.

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

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.

Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis

Climate change has become increasingly severe, threatening ecosystem stability and, in particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is inevitably affected by climate change, and therefore has a great potential to provide valuable information for addressing such ecosystem problems. However, the impacts of climate change on vegetation growth, especially the spatial and temporal distribution of vegetation, are still lacking of comprehensive exposition. To this end, this review systematically reveals the influences of climate change on vegetation dynamics in both time and space by dynamical modeling the interactions of meteorological elements and vegetation growth. Moreover, we characterize the long-term evolution trend of vegetation growth under climate change in some typical regions based on data analysis. This work is expected to lay a necessary foundation for systematically revealing the coupling effect of climate change on the ecosystem.

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change-rather than accurately predict specific outcomes-it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.

Regulation of open populations of a stream insect through larval density-dependence

In organisms with complex life cycles, the various stages occupy different habitats creating demographically open populations. The dynamics of these populations will depend on the occurrence and timing of stochastic influences relative to demographic density dependence, but understanding of these fundamentals, especially in the face of climate warming, has been hampered by the difficulty of empirical studies.

Using a logically feasible organism, we conducted a replicated density‐perturbation experiment to manipulate late‐instar larvae of nine populations of a stream caddisfly, Zelandopsyche ingens, and measured the resulting abundance over 2 years covering the complete life cycle of one cohort to evaluate influences on dynamics.

Negative density feedback occurred in the larval stage, and was sufficiently strong to counteract variation in abundance due to manipulation of larval density, adult caddis dispersal in the terrestrial environment as well as downstream drift of newly hatched and older larvae in the current. This supports theory indicating regulation of open populations must involve density dependence in local populations sufficient to offset variability associated with dispersal, especially during recruitment, and pinpoints the occurrence to late in the larval life cycle and driven by food resource abundance.

There were large variations in adult, egg mass and early instar abundance that were not related to abundance in the previous stage, or the manipulation, pointing to large stochastic influences. Thus, the results also highlight the complementary nature of stochastic and deterministic influences on open populations. Such density dependence will enhance population persistence in situations where variable dispersal and transitioning between life stages frequently creates mismatches between abundance and the local availability of resources, such as might become more common with climate warming.

Heat sensitivity of first host and cercariae may restrict parasite transmission in a warming sea

To predict global warming impacts on parasitism, we should describe the thermal tolerance of all players in host-parasite systems. Complex life-cycle parasites such as trematodes are of particular interest since they can drive complex ecological changes. This study evaluates the net response to temperature of the infective larval stage of Himasthla elongata, a parasite inhabiting the southwestern Baltic Sea. The thermal sensitivity of (i) the infected and uninfected first intermediate host (Littorina littorea) and (ii) the cercarial emergence, survival, self-propelling, encystment, and infection capacity to the second intermediate host (Mytilus edulis sensu lato) were examined. We found that infection by the trematode rendered the gastropod more susceptible to elevated temperatures representing warm summer events in the region. At 22 °C, cercarial emergence and infectivity were at their optimum while cercarial survival was shortened, narrowing the time window for successful mussel infection. Faster out-of-host encystment occurred at increasing temperatures. After correcting the cercarial emergence and infectivity for the temperature-specific gastropod survival, we found that warming induces net adverse effects on the trematode transmission to the bivalve host. The findings suggest that gastropod and cercariae mortality, as a tradeoff for the emergence and infectivity, will hamper the possibility for trematodes to flourish in a warming ocean.

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

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.