Final Report Summary - ACONITE (ACONITE: Assessment of Carrying capacity cONcept for specIes richness in planT assEmblages)
The primary purpose of ACONITE (Assessment of Carrying capacity cONcept for specIes richness in planT assEmblages) was to deepen our understanding of the mechanisms that drive the organization of plant species communities, in order to produce more realistic biodiversity models for present and future climatic conditions. To achieve this goal, this study involved of four specific objectives: 1) Test the concept of community carrying capacity and disentangle the drivers of biodiversity patterns. 2) Improve the outcome of biodiversity models by taking into account the previous objective. 3) Test the relative importance of different methodological aspects relevant for the modelling process. 4) Generate an improved framework for predicting plant assemblages under changing climates.
In a first step, we tested the importance of the concept of environmental carrying capacity in biodiversity models. Here, we evaluated new findings suggesting that the species richness of many communities is not saturated by inherent environmental limits, and we presented a conceptual and methodological framework to advance biodiversity modelling under the consequent assumption of unsaturated communities. We assessed existing model assumptions on community saturation, their implications, and propose approaches to improve these models to better account for unsaturated communities [1].
In a second step, we investigated species richness patterns of European land plants. Here, we have predicted the species composition by using stacked species distribution models (S-SDMs), and the assemblage properties by using macroecological models (MEMs), and we determined whether species richness increases with decreasing latitude, as predicted by theory. Species richness increases towards the south in spermatophytes, but towards the north in ferns and bryophytes, reflecting different biodiversity mechanisms for the different taxa studied [2].
We have applied previous steps in a comparison of two biodiversity modelling approaches and we predicted three facets of biodiversity (taxonomic, functional, and phylogenetic) using high-resolution data of plant communities. In this study, we tested a novel approach that couples S-SMDs with MEMs to model different biodiversity components. This method produced a moderate but significant improvement over the most common approach of stacking individual species predictions. Our results open a promising avenue to improve our ability to predict the different facets of species diversity in space, and perhaps in time, across broad environmental gradients using functional and phylogenetic information [3].
In the next step, we have reviewed different methodological aspects potentially affecting biodiversity predictions. In a first article, we evaluated the performance of direct interpolation of museum collections data and S-SDMs to produce reliable reconstructions of species richness patterns. Our results demonstrated that S-SDMs offer a useful tool for identifying detailed richness patterns [4]. In a second article, we validated the cross-temporal transferability of model predictions using paleoecological data. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. For cross-temporal projections of species distributions, we recommend models of intermediate complexity [5]. Besides, we presented a package written in R language: the ecospat package is to support of spatial analyses and modelling of species niches and distributions, with a focus on pre-, core and post- modelling analyses of species distribution, niche quantification, and community assembly [6].
To complement previous results, we investigated the phylogeographic structure and population dynamics of a plant species by combining genomic phylogeography and SDMs. Our results illustrate how past climatic changes affected the demographic history of organisms. Our findings highlight the significance of combining genetic approaches with environmental data when evaluating the effects of past climatic changes [7]. In this regard, phylogenetic community ecology (ecophylogenetics) being an emerging field of study that uses phylogenetics to test hypotheses about how ecological communities are assembled and about the potential outcomes of species interactions, it represents a dimension that should be increasingly considered in biodiversity analyses.
Finally, we made predictions of the Macaronesian endemic bryophyte flora in the context of ongoing climate change. The potential distribution of 35 Macaronesian endemic bryophyte species was assessed under present and future climate conditions using SDMs. Projections of the models under different climate change scenarios predicted an average decrease of suitable areas of 62–87% per species, and complete extinctions were foreseen for six of the studied Macaronesian endemic species [8].
The European Union (EU) has played a lead role in delivering the legislative framework for the Convention on Biological Diversity. Biodiversity modeling can be used to overcome the incomplete information on species distribution. The information obtained in this project can be used to help establish conservation strategies or to predict future patterns of biodiversity under climate change. Projecting community composition for future scenarios will support managers to apply conservation strategies efficiently, addressing more effectively the climate change challenge. The methodological approach proposed here will be of great usefulness for the European Research Area (ERA) since it will provide an innovative outline for predictions of species richness.
• CONTACT:
Dr. Rubén G. Mateo (email: rubeng.mateo@gmail.com webpage: https://rubengmateo.wordpress.com)
Prof. Antoine Guisan (email: antoine.guisan@unil.ch webpage: https://www.unil.ch/ecospat/en/home.html)
• REFERENCES:
1. Mateo, R.G. K. Mokany, A. Guisan (Submitted) Biodiversity models: what if unsaturation is the rule? Trends in Ecology & Evolution.
2. Mateo, R.G. O. Broennimann et al. (2016) Scientific Reports 6:25546.
3. D’Amen, M.*, R.G. Mateo* et al. (Under review) Improving spatial predictions of taxonomic, functional and phylogenetic diversity. Journal of Ecology.
4. Hespanhol, H., K. Cezón et al. (2015) Ecology and Evolution 5: 5443–5455.
5. Moreno-Amat, E., R.G. Mateo et al. (2015) Ecological Modelling 312:308-317.
6. Di Cola, V., O. Broennimann, et al. (Under review) Ecospat: an R package for the support of spatial analyses and modelling of species niches and distributions. Ecography.
7. Ren, G., R.G. Mateo et al. (2016). New Phytologist. DOI: 10.1111/nph.14221
8. Patiño, J.*, R.G. Mateo* et al. (2016) Scientific Reports 6:29156.
In a first step, we tested the importance of the concept of environmental carrying capacity in biodiversity models. Here, we evaluated new findings suggesting that the species richness of many communities is not saturated by inherent environmental limits, and we presented a conceptual and methodological framework to advance biodiversity modelling under the consequent assumption of unsaturated communities. We assessed existing model assumptions on community saturation, their implications, and propose approaches to improve these models to better account for unsaturated communities [1].
In a second step, we investigated species richness patterns of European land plants. Here, we have predicted the species composition by using stacked species distribution models (S-SDMs), and the assemblage properties by using macroecological models (MEMs), and we determined whether species richness increases with decreasing latitude, as predicted by theory. Species richness increases towards the south in spermatophytes, but towards the north in ferns and bryophytes, reflecting different biodiversity mechanisms for the different taxa studied [2].
We have applied previous steps in a comparison of two biodiversity modelling approaches and we predicted three facets of biodiversity (taxonomic, functional, and phylogenetic) using high-resolution data of plant communities. In this study, we tested a novel approach that couples S-SMDs with MEMs to model different biodiversity components. This method produced a moderate but significant improvement over the most common approach of stacking individual species predictions. Our results open a promising avenue to improve our ability to predict the different facets of species diversity in space, and perhaps in time, across broad environmental gradients using functional and phylogenetic information [3].
In the next step, we have reviewed different methodological aspects potentially affecting biodiversity predictions. In a first article, we evaluated the performance of direct interpolation of museum collections data and S-SDMs to produce reliable reconstructions of species richness patterns. Our results demonstrated that S-SDMs offer a useful tool for identifying detailed richness patterns [4]. In a second article, we validated the cross-temporal transferability of model predictions using paleoecological data. Reliable temporal model transferability is especially relevant for forecasting species distributions under future climate change. For cross-temporal projections of species distributions, we recommend models of intermediate complexity [5]. Besides, we presented a package written in R language: the ecospat package is to support of spatial analyses and modelling of species niches and distributions, with a focus on pre-, core and post- modelling analyses of species distribution, niche quantification, and community assembly [6].
To complement previous results, we investigated the phylogeographic structure and population dynamics of a plant species by combining genomic phylogeography and SDMs. Our results illustrate how past climatic changes affected the demographic history of organisms. Our findings highlight the significance of combining genetic approaches with environmental data when evaluating the effects of past climatic changes [7]. In this regard, phylogenetic community ecology (ecophylogenetics) being an emerging field of study that uses phylogenetics to test hypotheses about how ecological communities are assembled and about the potential outcomes of species interactions, it represents a dimension that should be increasingly considered in biodiversity analyses.
Finally, we made predictions of the Macaronesian endemic bryophyte flora in the context of ongoing climate change. The potential distribution of 35 Macaronesian endemic bryophyte species was assessed under present and future climate conditions using SDMs. Projections of the models under different climate change scenarios predicted an average decrease of suitable areas of 62–87% per species, and complete extinctions were foreseen for six of the studied Macaronesian endemic species [8].
The European Union (EU) has played a lead role in delivering the legislative framework for the Convention on Biological Diversity. Biodiversity modeling can be used to overcome the incomplete information on species distribution. The information obtained in this project can be used to help establish conservation strategies or to predict future patterns of biodiversity under climate change. Projecting community composition for future scenarios will support managers to apply conservation strategies efficiently, addressing more effectively the climate change challenge. The methodological approach proposed here will be of great usefulness for the European Research Area (ERA) since it will provide an innovative outline for predictions of species richness.
• CONTACT:
Dr. Rubén G. Mateo (email: rubeng.mateo@gmail.com webpage: https://rubengmateo.wordpress.com)
Prof. Antoine Guisan (email: antoine.guisan@unil.ch webpage: https://www.unil.ch/ecospat/en/home.html)
• REFERENCES:
1. Mateo, R.G. K. Mokany, A. Guisan (Submitted) Biodiversity models: what if unsaturation is the rule? Trends in Ecology & Evolution.
2. Mateo, R.G. O. Broennimann et al. (2016) Scientific Reports 6:25546.
3. D’Amen, M.*, R.G. Mateo* et al. (Under review) Improving spatial predictions of taxonomic, functional and phylogenetic diversity. Journal of Ecology.
4. Hespanhol, H., K. Cezón et al. (2015) Ecology and Evolution 5: 5443–5455.
5. Moreno-Amat, E., R.G. Mateo et al. (2015) Ecological Modelling 312:308-317.
6. Di Cola, V., O. Broennimann, et al. (Under review) Ecospat: an R package for the support of spatial analyses and modelling of species niches and distributions. Ecography.
7. Ren, G., R.G. Mateo et al. (2016). New Phytologist. DOI: 10.1111/nph.14221
8. Patiño, J.*, R.G. Mateo* et al. (2016) Scientific Reports 6:29156.