Modelling the oceanic habitats of two pelagic species using recreational fisheries data

Abstract

Defining the oceanic habitats of migratory marine species is important for both single species and ecosystem-based fisheries management, particularly when the distribution of these habitats vary temporally. This can be achieved using species distribution models that include physical environmental predictors. In the present study, species distribution models that describe the seasonal habitats of two pelagic fish (dolphinfish, Coryphaena hippurus and yellowtail kingfish, Seriola lalandi), are developed using 19 yr of presence-only data from a recreational angler-based catch-and-release fishing programme. A Poisson point process model within a generalized additive modelling framework was used to determine the species distributions off the east coast of Australia as a function of several oceanographic covariates. This modelling framework uses presence-only data to determine the intensity of fish (fish km À2 ), rather than a probability of fish presence. Sea surface temperature (SST), sea level anomaly, SST frontal index and eddy kinetic energy were significant environmental predictors for both dolphinfish and kingfish distributions. Models for both species indicate a greater fish intensity off the east Australian coast during summer and autumn in response to the regional oceanography, namely shelf incursions by the East Australian Current. This study provides a framework for using presence-only recreational fisheries data to create species distribution models that can contribute to the future dynamic spatial management of pelagic fisheries.

Oceanographic habitat suitability is positively correlated with the body condition of a coastal‐pelagic fish

“…Therefore, incorporating data from the Tasmanian coastal ocean subsequently reduced the maximum time‐before‐capture period that a statistically significant relationship between habitat suitability and kingfish condition could be detected. Although SLA and EKE are also significant predictors of kingfish oceanographic habitat (Brodie et al, ; Champion, Hobday, Zhang, et al, ), the relationship between these variables and temporal trends in the persistence of suitable kingfish habitat is less clear than for SST. Specifically, SLA has a positive linear effect on kingfish habitat suitability (Figure S3b), indicating that kingfish occurrence is likely to be higher in convergence areas, while low EKE values have a positive effect on model parameters that declines at values greater than ~ 0.11 m 2 s −2 (Figure S3c).…”

“…Twenty thousand pseudo‐absences were selected to (a) ensure that environmental variability occurring over the spatiotemporal extent encompassed by the occurrence dataset was adequately captured, (b) comply with Barbet‐Massin, Jiguet, Albert, and Thuiller () who recommend selecting a large number (i.e.> 10,000) of pseudo‐absences when using regression techniques to develop species distribution models, and (c) facilitate comparisons with habitat suitability models for other pelagic fishes from eastern Australia (see Champion, Hobday, Tracey, et al, ) that were also developed using approximately 20,000 pseudo‐absences (e.g. Brodie et al () and Hill et al () who used 20,000 and 23,242 pseudo‐absences, respectively). Explanatory oceanographic variables were matched to the resulting set of occurrence and pseudo‐absence data using the Spatial Dynamics Ocean Data Explorer (Hartog, Hobday, & Jumppanen, ).…”

“…Species distribution or habitat suitability models have become valuable tools for assessing spatiotemporal variation in environmental habitat quality (Brodie et al, ; Hazen et al, ). These models have been successfully applied to predict the seasonal distributions of coastal‐pelagic fishes (Brodie et al, ) and to identify shifts in their core (Dell, Wilcox, Matear, Chamberlain, & Hobday, ; Hill, Tobin, Reside, Pepperell, & Bridge, ) and range edge habitats under global change (Champion, Hobday, Tracey, & Pecl, ; Robinson, Hobday, Possingham, & Richardson, ). While predictions created using habitat suitability models are commonly dependent on the probability of species occurrence (Robinson et al, ), these predictions may also be used to provide novel insights into the physiological status of individuals across broad spatial extents.…”

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

“…This is not surprising given that temperature is a key factor influencing reproduction (Pankhurst, 1997;Byrne et al, 2009), survival (Pepin, 1991;Eggert, 2012), growth (Morrongiello and Thresher, 2015;Singh and Singh, 2015) and behavior (Biro et al, 2010;Allan et al, 2015) of many marine species. Satellite derived sea surface temperature (SST) measurements are widely used as a proxy for water temperature (Block et al, 2011;Brodie et al, 2015), due to readily available data, broad spatial coverage and long-time series (albeit at low spatial resolution) in the absence of in situ recordings (which are often at short time scales and spatially limited). Although such (skin) SST measurements are only representative of the first microns of the sea surface (or ∼10 m depth if foundation SST is used), they are regularly used as a proxy for temperatures at greater water depths (≥10 m) in ecological and biological studies (e.g., animal tracking: Papastamatiou et al, 2013;Lea et al, 2015; fisheries data- Brodie et al, 2015;Montero-Serra et al, 2015).…”

“…Satellite derived sea surface temperature (SST) measurements are widely used as a proxy for water temperature (Block et al, 2011;Brodie et al, 2015), due to readily available data, broad spatial coverage and long-time series (albeit at low spatial resolution) in the absence of in situ recordings (which are often at short time scales and spatially limited). Although such (skin) SST measurements are only representative of the first microns of the sea surface (or ∼10 m depth if foundation SST is used), they are regularly used as a proxy for temperatures at greater water depths (≥10 m) in ecological and biological studies (e.g., animal tracking: Papastamatiou et al, 2013;Lea et al, 2015; fisheries data- Brodie et al, 2015;Montero-Serra et al, 2015). SST, however, may not be available for coastal areas due to contamination in the satellite-derived images [noisier radar returns from land and sea (Brooks et al, 1990) and improper instrument corrections (Shum et al, 1998)].…”