Biology subjects

Vanhatalo, J.

Publications and source records attributed to Vanhatalo, J..

5 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2026

Inferences about phenological shifts in an Arctic community vary with time-windows

Long-term monitoring data have enabled detection of phenological change, yet it remains poorly understood how its temporal dimensions-- duration and choice of start and end years-- influence the inferences drawn. To examine which phenological signals emerge at different temporal scales, we analyzed the longest continuous dataset on high-arctic plant and arthropod phenology, collected from 1996 to 2024 in the Zackenberg valley, northeast Greenland. These data have been used to suggest both rapid advancement of spring in the High Arctic (2007) and little directional change but decadal regime shifts (2023). To reconcile these differing conclusions, we quantified how trend estimates varied across moving time-windows and determined the minimum time-series length required to achieve a high probability of agreement with long-term trends. We find that while trend directionality shifts with temporal windows, confidence in trend estimates increases with time-series length. Using the full time-series, we show dampened signals of warming trends, with annual increases in spring and summer air temperatures by 0.04 [-0.05, 0.13] and 0.05 [-0.01, 0.11] {degrees}C per year, respectively, alongside a 0.82% [-1.85, 0.19] decline in spring snow cover. We also see modest advancement in the seasonal activity of most arthropod taxa (by [~]0.1 days/year), whereas flowering phenology shows no consistent directional change. Shorter time-series revealed cyclical patterns in abiotic drivers yet variable biotic responses, indicating that a single pattern of "climate change" will translate into varied responses within communities. Finally, almost two decades of data were needed to reliably capture long-term trends. Ecologically, these suggest that 1) phenological shifts in the High Arctic are more moderate than early assessments implied and 2) reflect a dynamic balance between species life histories and ongoing climate variability. These may alter interaction potentials within communities, with consequences for ecosystem functioning.

ecology↗

bioRxiv2023

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↗

bioRxiv2022

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↗

bioRxiv2021

Doors and corners of variance partitioning in statistical ecology

Variance partitioning is a common tool for statistical analysis and interpretation in both observational and experimental studies in ecology. Its popularity has led to a proliferation of methods with sometimes confusing or contradicting interpretations. Here, we present variance partitioning as a general tool in a model based Bayesian framework for summarizing and interpreting regression-like models. To demonstrate our approach we present a case study comprising of a simple occupancy model for a metapopulation of the Glanville fritillary butterfly. We pay special attention to the thorny issue of correlated covariates and random effects, and highlight uncertainty in variance partitioning. We recommend several alternative measures of variance, which jointly can be used to better interpret variance partitions. Additionally, we extend the general approach to encompass partitioning of variance within and between groups of observations, an approach very similar to analysis of variance. While noting that many troublesome issues relating to variance partitioning, such as uncertainty quantification, have been neglected in the literature, we likewise feel that the rather general applicability of the methods as an extension of statistical model-based analyses has not been fully utilized by the ecological research community either.

ecology↗