Comparison of spatial distribution models to predict subtidal burying habitat of the forage fish Ammodytes personatus in the Strait of Georgia, British Columbia, Canada

Abstract

The Pacific sand lance (Ammodytes personatus) is a key forage species for many commercially important fish (e.g. salmon and groundfish), marine birds, and whales found in nearshore coastal waters of British Columbia, Canada. Sand lance lack a swim bladder and have a requirement for low‐silt, medium‐coarse sandy sea‐bed habitat for burying. Little information is available describing the distribution of burying habitat, partly because there are no commercial fisheries for A. personatus in British Columbia. This information is required by habitat and wildlife managers to identify and protect uncommon patches of burying habitats from detrimental activities, including dredging, infilling, and oil spills. In this study, habitat distribution results from five suitability modelling algorithms were evaluated: maximum entropy, generalized linear model, generalized additive model, random forest, and an ensemble model of the latter three. The maximum entropy model had the highest performance score (area under the receiver operator characteristic curve was 0.78) and was selected as the model that most accurately identified the presence of suitable A. personatus burying habitat. Model results indicate that suitable burying habitat is primarily influenced by derived sea‐bed substrate, distance to estuary, distance to sand‐gravel beaches, and bottom sea temperature. Overall, the spatial modelling identified only 105 km2 of highly suitable sand lance burying habitat, or 2.6% of the study area (0–150 m), primarily in Haro Strait, along the east coast of Vancouver Island, and in northern regions of the strait near Cortes, Savary, and Harwood islands. Identification of this uncommon and patchy burying habitat will contribute to the ongoing conservation of an important coastal prey species.

Climate Change and Its Impact on Brown Bear Distribution in Iran

“…AUC (a threshold-independent measure) and TSS statistics were used for estimate accuracy in the models performance. The AUC curve obtained from the Receiving Operator Characteristic (ROC) curve which is an effective indicator of threshold and prevalence for model performance evaluation [5,14,36,40]. In a model that lacks the ability to detect and predict, the AUC is 0.5 and a very high predictive and detectable model will have an AUC equal or close to 1 [5,41] .…”

“…It is noticeable that plants can shift their geographic ranges as a response to climatic change [56] . So new patterns of plant occupancy/ abundance can affect on animals which rely on plant availability for both food and shelter [31,36,[57][58][59] . However it cannot be ignored that it is possible for brown bears to cope with this food challenge due to climate change as they have a wide food niche [60] .…”

“…SDM includes solving a wide range of ambiguities, imperfections and shortcomings in other methods [21] . Because under different conditions, a method can be chosen as the superior model which is not universally applicable to other species, therefore, one of the possible solutions is the use of Ensemble modeling [21,36] which is suitable for dealing with mistakes and uncertainties between models [37] . In the present study sdm package in R were applied using several algorithms including MAXENT, GLM, MARS, BRT, RF, GAM for Ensemble distribution modeling under different climate scenarios as described by Naimi and Araujo, (2016) [21] and finally, the performance of the models were cross validated.…”

Intertidal beach habitat suitability model for Pacific sand lance (Ammodytes personatus) in the Salish Sea, Canada

“…It is likely that intertidal spawning habitat is located near subtidal burying habitat (Haynes and Robinson 2011;Laugier et al 2015). A layer representing the proximity to predicted suitable subtidal habitat (from Robinson et al 2021)) was created by calculating the distance of each raster cell from the closest neighboring cell where predicted subtidal habitat suitability from Robinson et al (2021) was 0.54 or greater.…”

“…Spatially explicit predictions of suitable habitat for sand lance could identify habitat for protection as well as aid managers in assessing the cumulative impacts of multiple pressures including shoreline armouring, dock installation, ship anchoring, and marina development. Recent modelling work has shown that suitable subtidal benthic habitats for sand lance in the Salish Sea are limited and patchy (Robinson et al 2021;Greene et al 2021;4 1995Tomlin et al 2021). No environmental cues that trigger spawning, such as sea surface temperature, tidal and lunar cycles have yet been identified.…”