Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques

Abstract

Species distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMs) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions.

Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species

“…Given potential differences in explanatory power and predictive skill (Derville, Torres, Iovan, & Garrigue, ; Fiedler et al, ), species distribution models were built using both Generalized Additive Mixed Models (GAMMs; “mgcv” R package) (Wood, ) and Boosted Regression Trees (BRTs; “dismo” R package) (Elith, Leathwick, & Hastie, ). Because seasonality in migratory behaviour has been shown to influence blue whale environmental preferences (Hazen et al, ), for both GAMMs and BRTs we explored year‐round models as well as separate models for each migratory season (summer/fall—July–November; winter/spring—December–June).…”

A treasure from the past: Former sperm whale distribution in Indonesian waters unveiled using distribution models and historical whaling data

“…The similarity of the spatial distributions between both models is also apparent from the high Pearson's correlation coefficients (Fiedler et al., 2018) between predicted grid values for sperm whale habitat suitability. The Pearson's correlations were high in all models, ranging from 0.705 to 0.851 (Figure 3).…”

“…Twelve submerged topographic variables were selected based on the expected ecological relevance to sperm whale habitats (Azzellino et al., 2012; Fiedler et al., 2018; Johnson et al., 2016; Redfern et al., 2017; Schlacher, Rowden, Dower, & Consalvey, 2010) in the study area. Due to unavailability of data during the historical whaling period, and the uncertainty of their stability over time, dynamic oceanographic variables (e.g.…”

“…The use of GAM is common in SDM because it allows the data to identify nonlinearity in species–habitat relationships rather than imposing parametric fits through polynomial terms in a linear regression (Chambers & Hastie, 2017; Hastie & Tibshirani, 1990). GAM is a presence–absence model that has been applied for cetacean studies (Fiedler et al., 2018; Virgili, Racine, Authier, Monestiez, & Ridoux, 2017). The GAM model attempts to differentiate the environmental conditions where the species is observed (presence points) and where it is absent (i.e.…”

Cetacean habitat modelling to inform conservation management, marine spatial planning, and as a basis for anthropogenic threat mitigation in Indonesia

“…topographic variables (bathymetry, slope, distance-to coast, shelf, -200m, -1000m, and -2500m isobaths), and oceanographic variables (sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a concentration (Chl)). These environmental features have been used as proxies to understand cetacean habitat in many studies (Azzellino et al, 2012;Fiedler et al, 2018;Tardin et al, 2019;Viddi et al, 2010). The collinearity among variables was checked in each region and only variables with Pearson's correlation values less than 0.75 were included in ecological modelling.…”