Biologically informed ecological niche models for an example pelagic, highly mobile species

Abstract

Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds.Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change. INTRODUCTION ABSTRACTBoosted regression trees; digital accessible knowledge; distribution modelling; Maxent; minimum volume ellipsoids; pelagic seabird distribution; Diomedea exulans KEYWORDS

Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina

“…Temporally explicit approaches to correlative niche modelling methods have been at the core of movement ecology analyses for some time (Gschweng et al., 2012), and yet the distributional ecology community has yet to adopt a similar approach. Indeed, despite long‐standing understanding that traditional (time‐averaged) correlative modelling approaches lead to over‐generalization of climatic niches (Barve et al., 2014; Ingenloff, 2017; Peterson et al., 2005), efforts to incorporate time‐specificity into the modelling framework have a fairly punctuated history. Seasonal modelling has been the gold star method for some time (Laube et al., 2015; Skov et al., 2016; Soriano‐Redondo et al .…”

“…The result is a single, static view of predicted suitability for the study species, which has been the topic of discussion in light of species that switch among multiple niches between seasons (Martínez‐Meyer et al., 2004). These approaches can result in over‐generalization of estimates of ecological niches (Barve et al., 2014; Ingenloff, 2017; Peterson et al., 2005), particularly for migratory or behaviourally complex organisms (Ingenloff, 2017; Peterson et al., 2005). Modelling mobile species presents a particularly challenging situation because, to be meaningful, predictive models must capture both a seasonally dynamic landscape and associated species movements, which traditional methods are unable to account for (Elith et al., 2010).…”

Predicting the potential distribution of the endemic seabirdPelecanus thagusin the Humboldt Current Large Marine Ecosystem under different climate change scenarios

“…Finally, six climate variables were selected: annual mean temperature, mean daytime temperature range, isothermality, seasonality in temperature, annual precipitation, seasonality in precipitation. The oceanographic variables used were sea surface temperature (SST) and marine net primary productivity (mg C m −2 day −1 ), as they are considered the main descriptors of the spatial distribution of seabirds (Quillfeldt et al, 2015; Ingenloff, 2017). These variables were obtained from the National Oceanic and Atmospheric Administration (NOAA, http://www.ngdc.noaa.gov/).…”

MERRA-based trend analysis identifies complex, multidecadal patterns of changing climatic suitability for Cassin’s Sparrow (Peucaea cassinii)

“…ENM is often applied within a time-averaged framework in which the values of environmental variables are averaged over time spans that are not in temporal registration with the occurrence records upon which models are calibrated or tested [55,57]. While useful for exploring species distributions at a broad level, modeling within a time-averaged framework can elide complex effects of the environment on an organism, especially highly mobile or behaviorally complex species [55,[58][59][60][61]. Of particular concern to conservation work, studies have shown that temporal mismatches in the time period spanned by occurrence data and the climate baseline can decrease the utility and accuracy of ENM products [62][63][64][65][66][67].…”