Sampling strategy matters to accurately estimate response curves’ parameters in species distribution models

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 7 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint

Supplementary Files

Manuele Bazzichetto, Jonathan Lenoir , Daniele Da Re, Enrico Tordoni, Duccio Rocchini, Marco Malavasi, Vojtech Barták, Marta Gaia Sperandii


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. 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 root mean squared error 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.



Biodiversity, Ecology and Evolutionary Biology, Life Sciences, Other Life Sciences, Plant Sciences


Bias, ecological niche breadth, environmental space, mean squared error, sampling bias, Simulation, virtual species


Published: 2022-08-28 03:52

Last Updated: 2023-07-16 07:43

Older Versions

CC-By Attribution-ShareAlike 4.0 International