Solving sampling bias problems in presence–absence or presence‐only species data using zero‐inflated models

Abstract

Aim: Large databases of species records such as those generated through citizen science projects, archives or museum collections are being used with increasing frequency in species distribution modelling (SDM) for conservation and land management. Despite the broad spatial and temporal coverage of the data, its application is often limited by the issue of sampling bias and consequently, zero inflation; there are more zeros (which are potentially 'false absences') in the data than expected. Here, we demonstrate how pooling species presence data into a 'pseudo-abundance' count can allow identification and removal of sampling bias through the use of zero-inflated (ZI) models, and thus solve a common SDM problem. Location: All locationsTaxon: All taxa Methods: We present the results of a series of simulations based on hypothetical ecological scenarios of data collection using random and non-random sampling strategies. Our simulations assume that the locations of occurrence records are known at a high spatial resolution, but that the absence of occurrence records may reflect undersampling. To simulate pooling of presence-absence or presence-only data, we count occurrence records at intermediate and coarse spatial resolutions, and use ZI models to predict the counts (species abundance per grid cell) from environmental layers.Results: ZI models can successfully identify predictors of bias in species data and produce abundance prediction maps that are free from that bias. This phenomenon holds across multiple spatial scales, thereby presenting an advantage over presence-only SDM methods such as binomial GLMs or MaxEnt, where information about species density is lost, and model performance declines at coarser scales.Main Conclusions: Our results highlight the value of converting presence-absence or presence-only species data to 'pseudo-abundance' and using ZI models to address the problem of sampling bias. This method has huge potential for ecological researchers when using large species datasets for research and conservation.

Distribution models calibrated with independent field data predict two million ancient and veteran trees in England

“…The fourth bias correction method is a novel approach we recently developed (Nolan, Gilbert, & Reader, 2021 ), whereby the 93,404 presence‐only ATI records were aggregated into a count of occurrences per 1‐km grid cell (“abundance”) (Figure 1 ). In some cases it is likely that this abundance measure is more likely pseudo‐abundance, as in many species databases single occurrences represent the presence of multiple individuals at a single location.…”

“…Predictive species distribution maps based on abundance are much less common than those based on presence or presence–absence, because most large species data sets record only species occurrence (Lyashevska et al, 2016 ). If the spatial predictors in SDM are only available at a greater resolution than the occurrence data, occurrences must be aggregated to presence‐only or presence–absence at the same resolution, which results in a loss of vital information about species density across the study area (Johnston et al, 2015 ; Nolan, Gilbert, & Reader, 2021 ). An alternative to aggregating occurrences to presence–absence data is to aggregate them into counts of occurrences (i.e., abundance or pseudo‐abundance) at the resolution of the spatial predictors, an approach that retains information about species density and can produce better fitting, more accurate predictive maps (Howard et al, 2014 ; Johnston et al, 2015 ; Nolan, Gilbert, & Reader, 2021 ).…”

“…If the spatial predictors in SDM are only available at a greater resolution than the occurrence data, occurrences must be aggregated to presence‐only or presence–absence at the same resolution, which results in a loss of vital information about species density across the study area (Johnston et al, 2015 ; Nolan, Gilbert, & Reader, 2021 ). An alternative to aggregating occurrences to presence–absence data is to aggregate them into counts of occurrences (i.e., abundance or pseudo‐abundance) at the resolution of the spatial predictors, an approach that retains information about species density and can produce better fitting, more accurate predictive maps (Howard et al, 2014 ; Johnston et al, 2015 ; Nolan, Gilbert, & Reader, 2021 ). One problem with this method is that the new aggregated abundance data are highly likely to be zero‐inflated (ZI) compared with the standard distributions that they are typically expected to follow (Bird et al, 2014 ; Martin et al, 2005 ), but this can be overcome with the use of ZI models (Lambert, 1992 ).…”

“…One problem with this method is that the new aggregated abundance data are highly likely to be zero‐inflated (ZI) compared with the standard distributions that they are typically expected to follow (Bird et al, 2014 ; Martin et al, 2005 ), but this can be overcome with the use of ZI models (Lambert, 1992 ). ZI models, which have received relatively little attention in the field of SDM, are able to cope with such data and have been shown to be able to both identify causes of sampling bias and facilitate its removal in simulated species data (Nolan, Gilbert, & Reader, 2021 ). Here we use our ATI case study to test our recently proposed method of sampling bias correction using ZI models (Nolan, Gilbert, & Reader, 2021 ).…”

“…ZI models, which have received relatively little attention in the field of SDM, are able to cope with such data and have been shown to be able to both identify causes of sampling bias and facilitate its removal in simulated species data (Nolan, Gilbert, & Reader, 2021 ). Here we use our ATI case study to test our recently proposed method of sampling bias correction using ZI models (Nolan, Gilbert, & Reader, 2021 ).…”