ArticleScientific reports2019
Integrating experimental and distribution data to predict future species patterns.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Harnessing macrogenomics to study changing marine ecosystems under global climate change.Proceedings. Biological sciences · 2026Review
- Methods to estimate marine functional connectivity: A primer.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Warm Edge Kelp Populations Show Elevated Volatility to Marine Heatwaves.Ecology letters · 2026Article
- An Evolutionary Mosaic Challenges Traditional Monitoring of a Foundation Species in a Coastal Environment-The Baltic Fucus vesiculosus.Molecular ecology · 2025Article
- Combining observational and experimental data to estimate environmental and species drivers of fungal metacommunity dynamics.Ecology · 2025Article
- Salinity tolerance of Round Goby: Informing invasion potential in North American coastal watersheds.PloS one · 2025Article
- Finnish inventory data of underwater marine biodiversity.Scientific data · 2024Article
- Fitness difference between two synonymous mutations of Phytophthora infestans ATP6 gene.BMC ecology and evolution · 2024Article
- Evaluating the predictive performance of presence-absence models: Why can the same model appear excellent or poor?Ecology and evolution · 2023Article
- Understanding and applying biological resilience, from genes to ecosystems.npj biodiversity · 2023Review
- Ten years of marine evolutionary biology-Challenges and achievements of a multidisciplinary research initiative.Evolutionary applications · 2023Article
- Ecological lags govern the pace and outcome of plant community responses to 21st-century climate change.Ecology letters · 2022Article
- Autumn larval cold tolerance does not predict the northern range limit of a widespread butterfly species.Ecology and evolution · 2021Article
- Spatial genetic structure in a crustacean herbivore highlights the need for local considerations in Baltic Sea biodiversity management.Evolutionary applications · 2020Article
- Forecasting mangrove ecosystem degradation utilizing quantifiable eco-physiological resilience -A study from Indian Sundarbans.Scientific reports · 2020Article
- Article
- Geographic variation in fitness-related traits of the bladderwrackEcology and evolution · 2019Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.
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