Combining ecological niche models with experimental seed germination to estimate the effect of climate change on the distribution of endangered plant species in the Brazilian Cerrado

Potential Distribution of Pilocarpus microphyllus in the Amazonia/Cerrado Biomes under Near-Future Climate Change Scenarios

Pilocarpus microphyllus Stapf. ex Wardlew. (Rutaceae) is an endemic and threatened medicinal plant species from tropical Brazil. Popularly known as "jaborandi", it is the unique natural source of pilocarpine, an alkaloid used to medical treat glaucoma and xerostomia. Based on Species Distribution Models (SDMs), we modeled the suitability of P. microphyllus's geographical distribution considering three Global Circulation Models (GCMs) under two future climate change scenarios (SSP2-4.5 and SSP5-8.5). The quantitative analyses carried out using ten different SDM algorithms revealed that precipitation seasonality (Bio15) and precipitation of the driest month (Bio14) were the most important bioclimatic variables. The results evidenced four main key areas of continuous occurrence of the plant spreading diagonally over tropical Brazilian biomes (Amazon, Cerrado and Caatinga). The near-future (2020 to 2040) ensemble projections considering all GCMs and scenarios have indicated negative impacts for the potential loss or significant reduction in suitable habitats for P. microphyllus in the transition region between the Amazon and Cerrado into central and northern Maranhão state, and mainly in the Caatinga biome over the northern Piaui state. On the other hand, positive impacts of the expansion of the plant habitat suitability are projected over forest cover protected areas of the Amazon biome in the southeastern Pará state. Since the jaborandi is of socioeconomic importance for many families in the north/northeast Brazil, it is urgent to implement public policies for conservation and sustainable management, thus mitigating the impacts of global climate change.

Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data

Simple Summary

Climate change is the main threat for conservation in the 21st century. Reliable methodologies and tools for the evaluation of its impact are urgently needed. Correlative ecological niche models (ENMs) are effective tools for predicting the future distribution of species under climate change scenarios. Despite this, many alternative different methods have been proposed, and objective reasons for a proper selection are unclear. Therefore, a comparative study to evaluate the consistency of predictions of the main ENM algorithms was performed. To test the effectiveness of correlative ENM temporal projection, we compared predictions generated using historical data and projected to the modern climate with predictions generated using modern distribution and climate data. In total, 600 case studies were generated, by using 25 Italian endemic plant species, 12 algorithms and 2 alternative sets of environmental variables. As a result, we highlighted the similarity of eight algorithms and the poor performance of four.

Abstract

Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.