Integrating experimental and distribution data to predict future species patterns

Jonne Kotta, Jarno Petteri Vanhatalo, Holger Jänes, Helen Orav-Kotta, Luca Rugiu, Veijo Jormalainen, Ivo Bobsien, Markku Viitasalo, Elina Virtanen, Antonia Nyström Sandman, Martin Isaeus, Sonja Leidenberger, Per R. Jonsson, Kerstin Johannesson

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Article number1821
JournalScientific Reports
Volume9
Number of pages14
ISSN2045-2322
DOIs
Publication statusPublished - 12 Feb 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 1181 Ecology, evolutionary biology
  • CLIMATE-CHANGE IMPACTS
  • NO-ANALOG COMMUNITIES
  • BALTIC SEA
  • DISTRIBUTION MODELS
  • PHENOTYPIC PLASTICITY
  • SPATIAL-DISTRIBUTION
  • FUCUS-VESICULOSUS
  • LOCAL ADAPTATION
  • IDOTEA-BALTICA
  • SHIFTS

Cite this

Kotta, J., Vanhatalo, J. P., Jänes, H., Orav-Kotta, H., Rugiu, L., Jormalainen, V., ... Johannesson, K. (2019). Integrating experimental and distribution data to predict future species patterns. Scientific Reports, 9, [1821]. https://doi.org/10.1038/s41598-018-38416-3
Kotta, Jonne ; Vanhatalo, Jarno Petteri ; Jänes, Holger ; Orav-Kotta, Helen ; Rugiu, Luca ; Jormalainen, Veijo ; Bobsien, Ivo ; Viitasalo, Markku ; Virtanen, Elina ; Nyström Sandman, Antonia ; Isaeus, Martin ; Leidenberger, Sonja ; Jonsson, Per R. ; Johannesson, Kerstin. / Integrating experimental and distribution data to predict future species patterns. In: Scientific Reports. 2019 ; Vol. 9.
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title = "Integrating experimental and distribution data to predict future species patterns",
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.",
keywords = "1181 Ecology, evolutionary biology, CLIMATE-CHANGE IMPACTS, NO-ANALOG COMMUNITIES, BALTIC SEA, DISTRIBUTION MODELS, PHENOTYPIC PLASTICITY, SPATIAL-DISTRIBUTION, FUCUS-VESICULOSUS, LOCAL ADAPTATION, IDOTEA-BALTICA, SHIFTS",
author = "Jonne Kotta and Vanhatalo, {Jarno Petteri} and Holger J{\"a}nes and Helen Orav-Kotta and Luca Rugiu and Veijo Jormalainen and Ivo Bobsien and Markku Viitasalo and Elina Virtanen and {Nystr{\"o}m Sandman}, Antonia and Martin Isaeus and Sonja Leidenberger and Jonsson, {Per R.} and Kerstin Johannesson",
year = "2019",
month = "2",
day = "12",
doi = "10.1038/s41598-018-38416-3",
language = "English",
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journal = "Scientific Reports",
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Kotta, J, Vanhatalo, JP, Jänes, H, Orav-Kotta, H, Rugiu, L, Jormalainen, V, Bobsien, I, Viitasalo, M, Virtanen, E, Nyström Sandman, A, Isaeus, M, Leidenberger, S, Jonsson, PR & Johannesson, K 2019, 'Integrating experimental and distribution data to predict future species patterns' Scientific Reports, vol. 9, 1821. https://doi.org/10.1038/s41598-018-38416-3

Integrating experimental and distribution data to predict future species patterns. / Kotta, Jonne; Vanhatalo, Jarno Petteri; Jänes, Holger; Orav-Kotta, Helen; Rugiu, Luca; Jormalainen, Veijo; Bobsien, Ivo; Viitasalo, Markku; Virtanen, Elina; Nyström Sandman, Antonia; Isaeus, Martin; Leidenberger, Sonja; Jonsson, Per R.; Johannesson, Kerstin.

In: Scientific Reports, Vol. 9, 1821, 12.02.2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Integrating experimental and distribution data to predict future species patterns

AU - Kotta, Jonne

AU - Vanhatalo, Jarno Petteri

AU - Jänes, Holger

AU - Orav-Kotta, Helen

AU - Rugiu, Luca

AU - Jormalainen, Veijo

AU - Bobsien, Ivo

AU - Viitasalo, Markku

AU - Virtanen, Elina

AU - Nyström Sandman, Antonia

AU - Isaeus, Martin

AU - Leidenberger, Sonja

AU - Jonsson, Per R.

AU - Johannesson, Kerstin

PY - 2019/2/12

Y1 - 2019/2/12

N2 - 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.

AB - 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.

KW - 1181 Ecology, evolutionary biology

KW - CLIMATE-CHANGE IMPACTS

KW - NO-ANALOG COMMUNITIES

KW - BALTIC SEA

KW - DISTRIBUTION MODELS

KW - PHENOTYPIC PLASTICITY

KW - SPATIAL-DISTRIBUTION

KW - FUCUS-VESICULOSUS

KW - LOCAL ADAPTATION

KW - IDOTEA-BALTICA

KW - SHIFTS

U2 - 10.1038/s41598-018-38416-3

DO - 10.1038/s41598-018-38416-3

M3 - Article

VL - 9

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 1821

ER -