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@article{2335757,
author = {Bazzichetto, Manuele and Lenoir, Jonathan and Daniele, Da Re and Tordoni, Enrico and Rocchini, Duccio and Malavasi, Marco and Barták, Vojtech and Sperandii, Marta Gaia},
article_location = {Hoboken},
article_number = {10},
doi = {http://dx.doi.org/10.1111/geb.13725},
keywords = {bias; ecological niche breadth; environmental space; realised niche; root mean squared error; sampling bias; simulation; virtual species},
language = {eng},
issn = {1466-822X},
journal = {Global Ecology and Biogeography},
title = {Sampling strategy matters to accurately estimate response curves' parameters in species distribution models},
url = {https://doi.org/10.1111/geb.13725},
volume = {32},
year = {2023}
}
TY - JOUR
ID - 2335757
AU - Bazzichetto, Manuele - Lenoir, Jonathan - Daniele, Da Re - Tordoni, Enrico - Rocchini, Duccio - Malavasi, Marco - Barták, Vojtech - Sperandii, Marta Gaia
PY - 2023
TI - Sampling strategy matters to accurately estimate response curves' parameters in species distribution models
JF - Global Ecology and Biogeography
VL - 32
IS - 10
SP - 1717-1729
EP - 1717-1729
PB - Wiley
SN - 1466822X
KW - bias
KW - ecological niche breadth
KW - environmental space
KW - realised niche
KW - root mean squared error
KW - sampling bias
KW - simulation
KW - virtual species
UR - https://doi.org/10.1111/geb.13725
N2 - Aim: Assessing how different sampling strategies affect the accuracy and precision of species response curves estimated by parametric species distribution models.Major Taxa Studied: Virtual plant species.Location: Abruzzo (Italy).Time Period: Timeless (simulated data).Methods: We simulated the occurrence of two virtual species with different ecology (generalist vs specialist) and distribution extent. We sampled their occurrence following different sampling strategies: random, stratified, systematic, topographic, uniform within the environmental space (hereafter, uniform) and close to roads. For each sampling design and species, we ran 500 simulations at increasing sampling efforts (total: 42,000 replicates). For each replicate, we fitted a binomial generalised linear model, extracted model coefficients for precipitation and temperature, and compared them with true coefficients from the known species' equation. We evaluated the quality of the estimated response curves by computing bias, variance and root mean squared error (RMSE). Additionally, we (i) assessed the impact of missing covariates on the performance of the sampling approaches and (ii) evaluated the effect of incompletely sampling the environmental space on the uniform approach.Results: For the generalist species, we found the lowest RMSE when uniformly sampling the environmental space, while sampling occurrence data close to roads provided the worst performance. For the specialist species, all sampling designs showed comparable outcomes. Excluding important predictors similarly affected all sampling strategies. Sampling limited portions of the environmental space reduced the performance of the uniform approach, regardless of the portion surveyed.Main Conclusions: Our results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species' ecology. Overall, uniformly sampling the environmental space seems more efficient for species with wide environmental tolerances. The advantage of seeking the most appropriate sampling strategy vanishes when modelling species with narrow realised niches.
ER -
BAZZICHETTO, Manuele, Jonathan LENOIR, Da Re DANIELE, Enrico TORDONI, Duccio ROCCHINI, Marco MALAVASI, Vojtech BARTÁK a Marta Gaia SPERANDII. Sampling strategy matters to accurately estimate response curves' parameters in species distribution models. \textit{Global Ecology and Biogeography}. Hoboken: Wiley, 2023, roč.~32, č.~10, s.~1717-1729. ISSN~1466-822X. Dostupné z: https://dx.doi.org/10.1111/geb.13725.
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