climetrics: An R package to quantify multiple dimensions of climate change

Shirin Taheri, Babak Naimi, Miguel B. Araújo

Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure.

Babak Naimi and Miguel B. Araújo

Sdm is an object-oriented, reproducible and extensible, platform for species distribution modelling. It uses individual species and community-based approaches, enabling ensembles of models to be fitted and evaluated, to project species potential distributions in space and time. It provides a standardized and unified structure for handling species distributions data and modelling techniques, and supports markedly different modelling approaches, including correlative, process based (mechanistic), agent-based, and cellular automata. The object-oriented design of software is such that scientists can modify existing methods, extend the framework by developing new methods or modelling procedures, and share them to be reproduced by other scientists. sdm can handle spatial and temporal data for single or multiple species and uses high performance computing solutions to speed up modelling and simulations. The framework is implemented in R, providing a flexible and easy-to-use GUI interface.

Andrew V. Bradley , Isabel M.D. Rosa, Robert G. Pontius Jr., Sadia E. Ahmed, Miguel B. Araújo, Daniel G. Brown Amintas Brandão Jr., Gilberto Câmara, Tiago G.S. Carnerio, Andrew J. Hartley, Matthew J. Smith, Robert M. Ewers

The multiple uses of land-cover models have led to validation with choice metrics or an ad hoc choice of the validation metrics available. To address this, we have identified the major dimensions of land-cover maps that ought to be evaluated and devised a Similarity Validation (SimiVal) tool. SimiVal uses a linear regression to test a modelled projection against benchmark cases of, perfect, observed and systematic bias, calculated by rescaling the metrics from a random case relative to the observed, perfect case. The most informative regression coefficients, p-value and slope are plot on a ternary graph of ‘similarity space’ whose extremes are the three benchmark cases. SimiVal is tested on projections of two deliberately contrasting land-cover models to show the similarity between intra- and inter-model parameterisations. We find metrics of landscape structure are important in distinguishing between different projections of the same model. Predictive and exploratory models can benefit from the tool.

Hedvig K. Nenzén, Rebecca M. Swab , David A. Keith, Miguel B. Araújo

demoniche is a freely available R-package which simulates stochastic population dynamics in multiple populations of a species. A demographic model projects population sizes utilizing several transition matrices that can represent impacts on species growth. The demoniche model offers options for setting demographic stochasticity, carrying capacity, and dispersal. The demographic projection in each population is linked to spatially-explicit niche values, which affect the species growth. With the demoniche package it is possible to compare the influence of scenarios of environmental changes on future population sizes, extinction probabilities, and range shifts of species.

Wilfried Thuiller, Bruno Lafourcade, Robin Engler and Miguel B. Araújo

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal

functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.


Aquatic Macroecological Experiment

In collaboration with Miguel Matias we have installed a series of mesocosms across the Iberian Peninsula to study community and ecosystem dynamics and examine how they might be affected by climate change (warming and drying). The mesocosms consist of 1000L tanks (or catlle-tanks) and they are installed in groups of 32 across 6 different locations. These locations were chosen to encompass the variety of bioclimatic regions across the peninsula (atlantic x1, mediterranean x1, alpine x2 and semi-arid x2). The mesocosms are equipped with an automated monitoring system that allows controlling the temperature and water-level, thus enabling simulation of climatic change effects.