scholarly journals Integrating dynamic plant growth models and microclimates for species distribution modelling

2020 ◽  
Author(s):  
Rafael Schouten ◽  
Peter Anton Vesk ◽  
Michael Kearney
Keyword(s):  
Plant Growth ◽  
Species Distribution ◽  
Growth Models ◽  
Species Distribution Modelling ◽  
Plant Distribution ◽  
Distribution Modelling ◽  
Continental Scale ◽  
Correlative Models ◽  
Organism Growth ◽  
Climatic Drivers

Climate is a major factor determining the distribution of plant species. Correlative models are frequently used to model the relationships between species distributions and climatic drivers but, increasingly, their use for prediction in novel scenarios such as climate change is being questioned. Mechanistic models, where processes limiting plant distribution are explicitly included, are regarded as preferable but more challenging.The availability of tools for simulating microclimates with high spatial and temporal definition has also opened new possibilities for simulating the limiting environmental stresses experienced by plant over their ontogeny. However, the field of mechanistic species distribution modelling is relatively new and the tools and theory for constructing these models are underdeveloped.In this paper we explore the potential for using a Dynamic Energy Budget model of organism growth integrated with microclimate and photosynthesis models. We model the interactions of plant growth and microclimatic stressors over the life stages of plant growth, and scale them up to demonstrate predictions of distribution at the continental scale. We develop the model using Julia, a new language for scientific computing, as a set of generic modelling packages. These have a modular, toolkit structure that has the potential to increase the efficiency and transparency of developing mechanistic SDMs.

scholarly journals Species Distribution Modelling Using Deep Learning

2019 ◽  
Vol 3 ◽  
Author(s):  
Rutger Vos ◽  
Mark Rademaker ◽  
Laurens Hogeweg
Keyword(s):  
Deep Learning ◽  
Species Distribution ◽  
Large Scale ◽  
Species Distribution Modelling ◽  
Climate Change Scenarios ◽  
Niche Modelling ◽  
Distribution Modelling ◽  
Biodiversity Science ◽  
Learning Techniques ◽  
Correlative Models

Species distribution modelling, or ecological niche modelling, is a collection of techniques for the construction of correlative models based on the combination of species occurrences and GIS data. Using such models, a variety of research questions in biodiversity science can be investigated, among which are the assessment of habitat suitability around the globe (e.g. in the case of invasive species), the response of species to alternative climatic regimes (e.g. by forecasting climate change scenarios, or by hindcasting into palaeoclimates), and the overlap of species in niche space. The algorithms used for the construction of such models include maximum entropy, neural networks, and random forests. Recent advances both in computing power and in algorithm development raise the possibility that deep learning techniques will provide valuable additions to these existing approaches. Here, we present our recent findings in the development of workflows to apply deep learning to species distribution modelling, and discuss the prospects for the large-scale application of deep learning in web service infrastructures to analyze the growing corpus of species occurrence data in biodiversity information facilities.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

Sign in / Sign up

Export Citation Format

Share Document