PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection
Version 1
: Received: 14 April 2021 / Approved: 15 April 2021 / Online: 15 April 2021 (08:09:25 CEST)
How to cite:
MUGUMAARHAHAMA, Y.; FANDOHAN, A.B.; MUSHAGALUSA, A.C.; SODE, I.A.; GLELE KAKAÏ, R. Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection. Preprints2021, 2021040400. https://doi.org/10.20944/preprints202104.0400.v1
MUGUMAARHAHAMA, Y.; FANDOHAN, A.B.; MUSHAGALUSA, A.C.; SODE, I.A.; GLELE KAKAÏ, R. Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection. Preprints 2021, 2021040400. https://doi.org/10.20944/preprints202104.0400.v1
MUGUMAARHAHAMA, Y.; FANDOHAN, A.B.; MUSHAGALUSA, A.C.; SODE, I.A.; GLELE KAKAÏ, R. Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection. Preprints2021, 2021040400. https://doi.org/10.20944/preprints202104.0400.v1
APA Style
MUGUMAARHAHAMA, Y., FANDOHAN, A.B., MUSHAGALUSA, A.C., SODE, I.A., & GLELE KAKAÏ, R. (2021). Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection. Preprints. https://doi.org/10.20944/preprints202104.0400.v1
Chicago/Turabian Style
MUGUMAARHAHAMA, Y., Idelphonse Akoeugnigan SODE and Romain GLELE KAKAÏ. 2021 "Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection" Preprints. https://doi.org/10.20944/preprints202104.0400.v1
Abstract
Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coecients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to in ate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.
Keywords
Integrated Species distribution models; Maximum likelihood; Point-counts; Presence-only data; Site-Occupancy; species abundance
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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