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Improving models of species ecological niches: a remote sensing overview


Leitão, Pedro J; Santos, Maria J (2019). Improving models of species ecological niches: a remote sensing overview. Frontiers in Ecology and Evolution, 7:online.

Abstract

Effective conservation capable of mitigating global biodiversity declines require thorough knowledge on species distributions and their drivers. A species ecological niche determines its geographic distribution, and species distribution models (SDMs) can be used to predict them. For various reasons, e.g., the lack of spatial data on relevant environmental factors, SDMs fail to characterize important ecological relationships. We argue that SDMs do not yet include relevant environmental information, which can be measured with remote sensing (RS). RS may benefit SDMs because it provides information on e.g., ecosystem function, health and structure, complete spatial assessment, and reasonable temporal repeat for the processes that determine geographical distributions. However, RS data is still seldom included in such studies with the exception of climate data. Here we provide a guide for researchers aiming to improve their SDM studies, describing how they might include RS data in their specific study. We propose how to improve models of species ecological niches, by including measures of habitat quality (e.g., productivity), nutritional values, and seasonal or life-cycle events. To date, several studies have shown that using ecologically-relevant environmental predictors derived from RS improve model performance and transferability, and better approximate a species ecological niche. These data, however, are not a panacea for SDMs, as there are cases in which RS predictors are not appropriate, too costly, or exhibit low predictive power. The integration of multiple environmental predictors derived from RS in SDMs can thus improve our knowledge on processes driving biodiversity change and improve our capacity for biodiversity conservation.

Abstract

Effective conservation capable of mitigating global biodiversity declines require thorough knowledge on species distributions and their drivers. A species ecological niche determines its geographic distribution, and species distribution models (SDMs) can be used to predict them. For various reasons, e.g., the lack of spatial data on relevant environmental factors, SDMs fail to characterize important ecological relationships. We argue that SDMs do not yet include relevant environmental information, which can be measured with remote sensing (RS). RS may benefit SDMs because it provides information on e.g., ecosystem function, health and structure, complete spatial assessment, and reasonable temporal repeat for the processes that determine geographical distributions. However, RS data is still seldom included in such studies with the exception of climate data. Here we provide a guide for researchers aiming to improve their SDM studies, describing how they might include RS data in their specific study. We propose how to improve models of species ecological niches, by including measures of habitat quality (e.g., productivity), nutritional values, and seasonal or life-cycle events. To date, several studies have shown that using ecologically-relevant environmental predictors derived from RS improve model performance and transferability, and better approximate a species ecological niche. These data, however, are not a panacea for SDMs, as there are cases in which RS predictors are not appropriate, too costly, or exhibit low predictive power. The integration of multiple environmental predictors derived from RS in SDMs can thus improve our knowledge on processes driving biodiversity change and improve our capacity for biodiversity conservation.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:29 January 2019
Deposited On:09 Jan 2020 10:19
Last Modified:09 Jan 2020 10:19
Publisher:Frontiers Research Foundation
ISSN:2296-701X
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3389/fevo.2019.00009

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