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Noble, Ciar D. 
ORCID: https://orcid.org/0000-0002-3003-2789, Peres, Carlos A. ORCID: https://orcid.org/0000-0002-1588-8765 and Gilroy, James J. ORCID: https://orcid.org/0000-0002-7597-5780
(2024)
Accounting for imperfect detection when estimating species-area relationships and beta-diversity.
Ecology and Evolution, 14 (7).
ISSN 2045-7758
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Abstract
Ecologists have historically quantified fundamental biodiversity patterns, including species-area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species-area and β-diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β-diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non-parametric diversity estimator (iNEXT.3D) and Multi-Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β-diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z-values, and overestimated SAR c-values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z-values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β-diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β-diversity model coefficients resulted from a systematic, area-related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β-diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: This research was entirely based on simulated data. All code required to replicate the simulated data and associated analyses is available via Zenodo (https://zenodo.org/records/10055523) and a dedicated GitHub repository (https://github.com/ciardorje/Imperfect_Detection_Simulation). Acknowledgements: C.D.N.'s PhD at the University of East Anglia (UEA) is funded by a UEA Science Studentship. The authors thank the staff of the UEA High Performance Computing cluster (ADA), without which this research would not have been feasible. |
| Uncontrolled Keywords: | chao estimator,community simulation,habitat fragmentation,inext.3d,multi-species occupancy model,species richness,ecology, evolution, behavior and systematics,ecology,nature and landscape conservation ,/dk/atira/pure/subjectarea/asjc/1100/1105 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Groups > Environmental Biology Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Organisms and the Environment |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 28 Nov 2024 01:36 |
| Last Modified: | 17 Dec 2024 01:41 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/97790 |
| DOI: | 10.1002/ece3.70017 |
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