A new model to assess the probability of occurrence of a species, based on presence-only data

Abstract

This study aims to describe a new nonparametric ecological niche model for the analysis of presence-only data, which we use to map the spatial distribution of Atlantic cod and to project the potential impact of climate change on this species. The new model, called the Non-Parametric Probabilistic Ecological Niche (NPPEN) model, is derived from a test recently applied to compare the ecological niche of 2 different species. The analysis is based on a simplification of the Multiple Response Permutation Procedures (MRPP) using the Generalised Mahalanobis distance. For the first time, we propose to test the generalized Mahalanobis distance by a non-parametric procedure, thus avoiding the arbitrary selection of quantile classes to allow the direct estimation of the probability of occurrence of a species. The model NPPEN was applied to model the ecological niche (sensu Hutchinson) of Atlantic cod and therefore its spatial distribution. The modelled niche exhibited high probabilities of occurrence at bathymetry ranging from 0 to 500 m (mode from 100 to 300 m), at annual sea surface temperature of from -1 to 14°C (mode from 4 to 8°C) and at annual sea surface salinity ranging from 0 to 36 (mode from 25 to 34). This made the species a good indicator of the subarctic province. Current climate change is having a strong effect on North Sea cod and may have also reinforced the negative impact of fishing on stocks located offshore of North America. The model shows a pronounced effect of present-day climate change on the spatial distribution of Atlantic cod. Projections for the coming decades suggest that cod may eventually disappear as a commercial species from regions where a sustained decrease or collapse has already been documented. In contrast, the abundance of cod is likely to increase in the Barents Sea.

Climate change impact on Balearic shearwater through a trophic cascade

“…Probability of fish occurrence was calculated from the non-parametric probabilistic ecological niche model [10,11]. Our modelling is based on actual occurrence data point from the database 'Ocean Biogeographic Information System'.…”

“…It uses information on sea surface temperature, bathymetry and sea surface salinity to reconstruct the ecological niche of a species and project onto a geographical space [11]. Spatial distribution of the average mean of these variables is shown in Beaugrand et al [10]. An average annual value was calculated for all four species in the geographical area (178W-48 E and 48 -608 N) considered in Wynn et al (c) Sequential algorithm for testing regime shift To test for regime shift, we applied Rodionov's sequential algorithm in which a first-order autoregressive model was inserted to consider temporal autocorrelation (method of inverse proportionality with four corrections, subsample size of 5 years) [12].…”

Socio-economic Impacts—Fisheries

“…On the basis of the GAM results the authors suggested that they did not expect or predict substantial further deepening (as previously observed by Dulvy et al 2008), and that the capacity of fish to remain in cooler water by changing their depth distribution had been largely exhausted by the 1980s, that fish with preferences for cooler water are being increasingly exposed to higher temperatures, with expected physiological, life history and negative population consequences. Beaugrand et al (2011) described a model to map the future spatial distribution of Atlantic cod. The model, which they named the non-parametric probabilistic ecological niche model (NPPEN), suggested that cod may eventually disappear as a commercial species from some regions including the North Sea where a sustained decline has already been documented; in contrast, the abundance of cod is likely to increase in the Barents Sea.…”

Predicting the distribution of oceanic-stage Kemp's ridley sea turtles

“…The distribution of a species is commonly predicted by correlating occurrence records with environmental metrics to generate maps of habitat suitability or likelihood of occurrence [1,2]. Alternatively, distribution can be estimated from principles of movement ecology theory: the distribution of a species is mechanistically predicted by simulating the movement process of individuals within a realistic environmental model [3].…”

“…Alternatively, distribution can be estimated from principles of movement ecology theory: the distribution of a species is mechanistically predicted by simulating the movement process of individuals within a realistic environmental model [3]. Compared with more frequently used tools for predicting distribution, for instance ecological niche models [1,2], the movement ecology approach may be particularly useful for species that occupy habitats which preclude sampling or that possess cryptic life-stages, for instance sea turtles [4,5]. In most sea turtle species, the young migrate from beaches into the ocean and are rarely encountered again until they return to coastal waters 2-15 years later [4 -6].…”