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Tonteri, T.

Publications and source records attributed to Tonteri, T..

4 recordsLinked to original sources

Habitat context alters the pace of climate-driven community warming across terrestrial and freshwater ecosystems

As global temperatures rise, ecological communities are increasingly dominated by warm-affiliated species, a process known as community warming or thermophilisation. Yet, why different taxa exhibit different rates of community warming remains unclear. Habitat composition and structure are likely drivers of this variation, as the ecological consequences of warming are filtered by local environmental conditions. Using over 40 years of monitoring data spanning terrestrial (birds, insects, plants) and freshwater (phytoplankton) communities, we show that habitat structure determines how strongly communities track warming. Forest cover systematically slows thermophilisation by reducing communities sensitivity to temperature change, whereas habitat heterogeneity has weak and variable effects that differ among ecosystems. Together, these results demonstrate that uneven thermophilisation arises from habitat-mediated differences in how communities respond to a shared climatic signal. Incorporating these effects is essential for improving predictions of biodiversity change under ongoing climate warming.

ecology↗

Accelerating species loss in southern boreal understories as early warning sign of functional erosion

Biodiversity is changing rapidly worldwide. Although taxonomic and functional diversity are intrinsically linked, biodiversity change can decouple their trajectories, challenging biodiversity assessments that often focus on a taxonomic perspective. As a result, functional consequences of biodiversity change remain elusive. To better understand how taxonomic and functional diversity change are intertwined, we analytically partition functional diversity change. Specifically, using 38 years of boreal forest understorey data across Finland, we explicitly link species turnover to shifts in community functional composition. We show that, while stable in Mid and Northern regions, recent net loss of fast-growing species in Southern Finland has halted a decades-long increase in functional diversity. This slowdown emerges not from the loss of highly functionally unique species, but rather from the cumulative number of species lost, whose low abundances limit their contribution to functional diversity. Our approach uncovers mechanisms underlying the decoupling of taxonomic and functional diversity, illustrating how functional diversity can persist despite species loss, while also indicating a growing risk of functional erosion. Taken together, these findings underscore the importance and utility of integrating taxonomic and functional dimensions to better anticipate ecological consequences of biodiversity change.

ecology↗

Inferring ecological selection from multidimensional community trait distributions along environmental gradients

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. While trait-based frameworks are often used to this end, correlations among traits and the assumption of unimodal trait-abundance distributions confound detection of the underlying processes. To overcome these caveats, here we quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We find that directional, stabilizing, and divergent selection all modify community trait niches in over 3600 boreal forest understory plant communities, and selection on a particular trait may change from one type to another over time. Our results provide novel and rare empirical evidence from a natural system for divergent selection. The results also show that while higher trait diversity (measured as trait niche volume) is generally associated with higher species richness, high resource availability may enable tighter niche packing. Jointly our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.

ecology↗

Identifying and separating the processes underlying boreal forest understory community assembly

Joint species distribution models (JSDMs) are an important conservation tool for predicting ecosystem diversity and function under global change. The growing complexity of modern JSDMs necessitates careful model selection tailored to the challenges of community prediction under novel conditions (i.e., transferable models). Common approaches to evaluate the performance of JSDMs for community-level prediction are based on individual species predictions that do not account for the species correlation structures inherent in JSDMs. Here, we formalize a Bayesian model selection approach that accounts for species correlation structures and apply it to compare the community-level predictive performance of alternative JSDMs across broad environmental gradients emulating transferable applications. We connect the evaluation of JSDM predictions to Bayesian model selection theory under which the log score is the preferred performance measure for probabilistic prediction. We define the joint log score for community-level prediction and distinguish it from more commonly applied JSDM evaluation metrics. We then apply this community log score to evaluate predictions of 1,918 out-of-sample boreal forest understory communities spanning 39 species generated using a novel JSDM framework that supports alternative species correlation structures: independent, compositional dependence, and residual dependence. The best performing JSDM included all observed environmental variables and multinomial species correlations reflecting compositional dependence within modeled community data. The addition of flexible residual species correlations improved model predictions only within JSDMs applying a reduced set of environmental variables highlighting potential confounding between unobserved environmental conditions and residual species dependence. The best performing JSDM was consistent across successional and bio-climatic gradients regardless of whether interest was in species- or community-level prediction. Our study demonstrates the utility of the community log score to quantify differences in the predictive performance of complex JSDMs and highlights the importance of accounting for species dependence when interest is in community composition under novel conditions.

ecology↗