Species Distribution Models Overfitting Effect on Hidden Animal Diversity Estimation: Implications for Conservation Decision-Making

19 Pages Posted: 12 Jan 2024

See all articles by Camilo Matus-Olivares

Camilo Matus-Olivares

affiliation not provided to SSRN

Jaime Carrasco

University of Chile

José Luis Yela

University of Castilla-La Mancha

Paula Meli

University of Concepción

Andres Weintraub

University of Chile - Industrial Engineering

Fulgencio Lisón Gil

University of Concepción

Abstract

The geographical distribution patterns of animal species communities are essential to design and implement ecological applications and conservation measures successfully. However, to know accurately the species distribution is complex mainly due to three factors: 1) undersampled areas, 2) sampling methodologies not always effective, and 3) uncertainty in the true absence. Therefore, conceptually, there exists a "hidden animal diversity" (HAD) that is possible to measure and quantify, especially through species distribution models (SDMs). Here, we analyzed the overfitting effect of different SDM algorithms over the HAD estimates. Specifically, we a) compare and assess the overfitting levels and predictive performance of different SDM algorithms; b) examine the overfitting effect of different SDM algorithms over HAD estimates and; c) create and assess an evaluation method that allows choosing the most suitable SDM algorithm according to a jointly evaluation of metrics of predictive performance and overfitting. Our results showed that those algorithms with high overfitting levels were worse at predicting hidden animal diversity. We found a significative negative relationship between the expected value of HAD and the level of overfitting of an SDM algorithm. This result is reflected in the distribution maps and it becomes an issue in the decision-making process for the conservation policies. Our findings help improve HAD estimation and have applications in conservation and restoration, providing critical issues on how allocating resources in space, taxonomic groups, or functional guilds. Moreover, we provide a flexible evaluation method that considers the overfitting when evaluating an SDM algorithm.

Keywords: beta diversity, Chiroptera, ensemble models, Lepidoptera, threshold models

Suggested Citation

Matus-Olivares, Camilo and Carrasco, Jaime and Yela, José Luis and Meli, Paula and Weintraub, Andres and Lisón Gil, Fulgencio, Species Distribution Models Overfitting Effect on Hidden Animal Diversity Estimation: Implications for Conservation Decision-Making. Available at SSRN: https://ssrn.com/abstract=4692303 or http://dx.doi.org/10.2139/ssrn.4692303

Camilo Matus-Olivares

affiliation not provided to SSRN ( email )

No Address Available

Jaime Carrasco

University of Chile ( email )

Pío Nono Nº1, Providencia
Santiago, 7520421
Chile

José Luis Yela

University of Castilla-La Mancha ( email )

Plaza Universidad, 1
02071 Albacete, 13071
Spain

Paula Meli

University of Concepción ( email )

Victoria Lamas 471
Concepcion
Chile

Andres Weintraub

University of Chile - Industrial Engineering

República 701, Santiago
Chile

HOME PAGE: aweintra@dii.uchile.cl

Fulgencio Lisón Gil (Contact Author)

University of Concepción ( email )

Victoria Lamas 471
Concepcion
Chile

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