Effect of imperfect detection on the estimation of niche overlap between two forest dormice

doi:10.3832/ifor2738-011

Effect of imperfect detection on the estimation of niche overlap between two forest dormice

Chiara Paniccia⁽¹⁾, Mirko Di Febbraro⁽¹⁾ , Ludovico Frate⁽¹⁾, Lorenzo Sallustio^(2-3), Giovanni Santopuoli⁽²⁾, Tiziana Altea⁽⁴⁾, Mario Posillico^(5-6), Marco Marchetti⁽²⁾, Anna Loy⁽¹⁾

iForest - Biogeosciences and Forestry, Volume 11, Issue 4, Pages 482-490 (2018)
doi: https://doi.org/10.3832/ifor2738-011
Published: Jul 18, 2018 - Copyright © 2018 SISEF

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Quantification of niche overlap represents an important topic in several aspects of ecology and conservation biology, although it could be potentially affected by imperfect detection, i.e., failure to detect a species at occupied sites. We investigate the effect of imperfect detection on niche overlap quantification in two arboreal rodents, the edible dormouse (Glis glis) and the hazel dormouse (Muscardinus avellanarius). For both species, we used Generalized Linear Mixed Models (GLMM) to estimate the occurrence probability and Occupancy Models (OM) to calculate occurrence and detection probabilities. By comparing these predictions through niche equivalency and similarity tests, we first hypothesised that methods correcting for imperfect detection (OM) provide a more reliable estimate of niche overlap than traditional presence/ absence methods (GLMM). Furthermore, we hypothesised that GLMM mainly estimate species detectability rather than actual occurrence, and that a low number of sampling replicates provokes an underestimation of species niche by GLMM. Our results highlighted that GLMM-based niche overlap yielded significant outcomes only for the equivalency test, while OM-based niche overlap reported significant outcomes for both niche equivalency and similarity tests. Moreover, GLMM occurrence probabilities and OM detectabilities were not statistically different. Lastly, GLMM predictions based on single sampling replicates were statistically different from the average occurrence probability predicted by GLMM over all replicates. We emphasized how accounting for imperfect detection can improve the statistical significance and interpretability of niche overlap estimates based on occurrence data. Under a habitat management perspective, an accurate quantification of niche overlap may provide useful information to assess the effects of different management practices on species occurrence.

Keywords

Occupancy Models, Generalized Linear Mixed Models, Forest Management, Niche Overlap

Authors’ address

(1)

Envix-Lab, Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, c.da Fonte Lappone, I-86090 Pesche, IS (Italy)

(2)

Centro di Ricerca per le Aree Interne e gli Appennini (ArIA), Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, c.da Fonte Lappone, I-86090 Pesche, IS (Italy)

(3)

CREA Research Centre for Forestry and Wood, v.le Santa Margherita 80, I-52100 Arezzo (Italy)

(4)

Coordinamento Territoriale Carabinieri per l’Ambiente, Parco Nazionale “Abruzzo-Lazio-Molise”, Pescasseroli, AQ (Italy)

(5)

Reparto Carabinieri Biodiversità Castel di Sangro, Centro Ricerche Ambienti Montani, v. Sangro, 45-67031. Castel di Sangro, AQ (Italy)

(6)

Consiglio Nazionale delle Ricerche, Istituto di Biologia Agroambientale e Forestale, v. Salaria km 29.300, I-00015 Montelibretti, RM (Italy)

Corresponding author

Mirko Di Febbraro
mirkodifebbraro@gmail.com

Citation

Paniccia C, Di Febbraro M, Frate L, Sallustio L, Santopuoli G, Altea T, Posillico M, Marchetti M, Loy A (2018). Effect of imperfect detection on the estimation of niche overlap between two forest dormice. iForest 11: 482-490. - doi: 10.3832/ifor2738-011

Academic Editor

Massimo Faccoli

Paper history

Received: Jan 24, 2018
Accepted: May 01, 2018

First online: Jul 18, 2018
Publication Date: Aug 31, 2018
Publication Time: 2.60 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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