Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Abstract

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence-only dataset with a structured, presence-absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence-only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence-absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.

Species-Habitat Associations: Spatial data, predictive models, and ecological insights

“…imperfect detection). Alternatively, it may be possible to combine haphazardly collected data with data collected in a structured fashion to more fully understand and model sampling biases (Dorazio, 2014;Fithian, Elith, Hastie, & Keith, 2015;Koshkina et al, 2017;Isaac et al, 2020).…”

Data quantity is more important than its spatial bias for predictive species distribution modelling

“…Spatial sampling bias can be introduced into simulated data using a parametric function that describes the bias ( Isaac et al, 2014 ; Stolar & Nielsen, 2015 ; Thibaud et al, 2014 ; Simmonds et al, 2020 ) or by following a simplified ad-hoc rule (e.g., splitting the study region into distinct areas that are sampled with different intensities) ( Phillips et al, 2009 ). However, these approaches may not adequately test the effect of spatial bias if the biases found in real biological records do not follow parametric functions or are more severe than artificial parametric or ad-hoc biases.…”

Integrated spatial models foster complementarity between monitoring programs in producing large-scale bottlenose dolphin indicators

“…limited spatial or temporal survey coverage, Zipkin & Saunders 2018; Isaac et al 2019). However, caution should be taken as integrating data requires additional modelling assumptions (Dupont et al, 2019; Farr et al, 2020; Fletcher et al, 2019; Simmonds et al, 2020). Overall, SIM are flexible tools that can include more than 2 datasets (Zipkin & Saunders 2018), and various type of data that enlarge the scope of usable information (presence-absence (Santika et al 2017), count data (Chandler et al 2018), citizen science data (Sun et al 2019)).…”