Utilizing individual fish biomass and relative abundance models to map environmental niche associations of adult and juvenile targeted fishes

Many fishes undergo ontogenetic habitat shifts to meet their energy and resource needs as they grow. Habitat resource partitioning and patterns of habitat connectivity between conspecific fishes at different life-history stages is a significant knowledge gap. Species distribution models were used to examine patterns in the relative abundance, individual biomass estimates and environmental niche associations of different life stages of three iconic West Australian fishes. Continuous predictive maps describing the spatial distribution of abundance and individual biomass of the study species were created as well predictive hotspot maps that identify possible areas for aggregation of individuals of similar life stages of multiple species (i.e. spawning grounds, fisheries refugia or nursery areas). The models and maps indicate that processes driving the abundance patterns could be different from the body size associated demographic processes throughout an individual's life cycle. Incorporating life-history in the spatially explicit management plans can ensure that critical habitat of the vulnerable stages (e.g. juvenile fish, spawning stock) is included within proposed protected areas and can enhance connectivity between various functional areas (e.g. nursery areas and adult populations) which, in turn, can improve the abundance of targeted species as well as other fish species relying on healthy ecosystem functioning

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value

Multi-Scale approach for predicting fish species distributions across coral reef seascapes

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5–300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided ‘outstanding’ model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided ‘outstanding’ model predictions for two of five species, with the remaining three models considered ‘excellent’ (AUC = 0.8–0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management

Timing of migratory baleen whales at the Azores in relation to the North Atlantic spring bloom

Each year, a phytoplankton spring bloom starts just north of the North Atlantic Subtropical Gyre, and then expands northwards across the entire North Atlantic. Here, we investigate whether the timing of the spring migration of baleen whales is related to the timing of the phytoplankton spring bloom, using 4 yr of dedicated whale observations at the Azores in combination with satellite data on ocean chlorophyll concentration. Peak abundances of blue whale Balaenoptera musculus, fin whale B. physalus, humpback whale Megaptera novaeangliae and sei whale B. borealis were recorded in April-May. The timing of their presence tracked the onset of the spring bloom with mean time lags of 13, 15, 15 and 16 wk, respectively, and was more strongly related to the onset of the spring bloom than to the actual time of year. Baleen whales were actively feeding on northern krill Meganyctiphanes norvegica in the area, and some photo-identified individuals stayed in Azorean waters for at least 17 d. Baleen whales were not observed in this area in autumn, during their southward migration, consistent with low chlorophyll concentrations during summer and autumn. Our results support the hypothesis that baleen whales track the secondary production generated by the North Atlantic spring bloom, utilizing mid-latitude areas such as the Azores as foraging areas en route towards their summer feeding grounds

A review of cephalopod-environment interactions in European Seas

Cephalopods are highly sensitive to environmental conditions and changes at a range of spatial and temporal scales. Relationships documented between cephalopod stock dynamics and environmental conditions are of two main types: those concerning the geographic distribution of abundance, for which the mechanism is often unknown, and those relating to biological processes such as egg survival, growth, recruitment and migration, where mechanisms are sometimes known and in a very few cases demonstrated by experimental evidence. Cephalopods seem to respond to environmental variation both ‘actively’ (e.g. migrating to areas with more favoured environmental conditions for feeding or spawning) and ‘passively’ (growth and survival vary according to conditions experienced, passive migration with prevailing currents). Environmental effects on early life stages can affect life history characteristics (growth and maturation rates) as well as distribution and abundance. Both large-scale atmospheric and oceanic processes and local environmental variation appear to play important roles in species–environment interactions. While oceanographic conditions are of particular significance for mobile pelagic species such as the ommastrephid squids, the less widely ranging demersal and benthic species may be more dependent on other physical habitat characteristics (e.g. substrate and bathymetry). Coastal species may be impacted by variations in water quality and salinity (related to rainfall and river flow). Gaps in current knowledge and future research priorities are discussed. Key research goals include linking distribution and abundance to environmental effects on biological processes, and using such knowledge to provide environmental indicators and to underpin fishery management

Dealing with temporal variation and different life stages of whitemouth croaker Micropogonias furnieri (Actinopterygii, Sciaenidae) in species distribution modeling to improve essential estuarine fish habitat identification

Understanding the habitat usage of a species is essential to assessing the impacts of human activities, conservation efforts, and management issues on that species. Whitemouth croaker, an important species in the artisanal fishery market, inhabits different habitats of the Patos Lagoon estuary year-round based on the stage of its life cycle. Our aim was to test the hypothesis that habitat preferences and changes in abundance during the life cycle influence the model outcomes in the study of species distribution. Additionally, we evaluated whether incorporating additional life stages within the model affected the outcome of what comprised the essential fish habitat. Our results showed that the model’s outcome was affected when temporal variability and additional life stages were considered. We suggest that variability in abundance and habitat preferences at different developmental stages must be considered when identifying essential fish habitat of species with complex life cycles, such as whitemouth croaker. Understanding these changes could improve conservation and management outcomes

Modeling habitat preferences of Caspian kutum, Rutilus frisii kutum (Kamensky, 1901) (Actinopterygii, Cypriniformes) in the Caspian Sea

Predicting and modeling of habitat preferences of fish is a very important issue for aquatic management. Classification trees (CTs) were used to predict the habitat preferences of the Caspian kutum (Rutilus frisii kutum, hereafter kutum) in the southern Caspian Sea. The applied model was optimized with genetic algorithm (GA) and greedy stepwise (GS) to select the most explanatory variables for predicting the presence/absence of kutum. The suitability index was considered to determine the quality and suitability of fish habitat in the sea. The results of Paired Student's t tests showed that there was a significant difference between predictive performances of models before and after variable selection methods. Both optimizers improved the predictive power of CTs and resulted in a better understanding of CTs by making a selection of the sea characteristics that were used as inputs to the models. The results show that the effect of different seasons, sea depth, and photosyntheticaly active radiation were the main predictors affecting the habitat preferences of kutum in the Caspian Sea. Constructed trees in combination with GA and GS showed high capability when applied to predict the habitat preferences of this valuable commercial fish species. Determining the habitat needs of the target fish will enhance local fisheries performances and the long-term conservation planning of the fish to implement the ecosystem-based management in the Caspian Sea

Comparing two remote video survey methods for spatial predictions of the distribution and environmental niche suitability of demersal fishes

Information on habitat associations from survey data, combined with spatial modelling, allow the development of more refined species distribution modelling which may identify areas of high conservation/fisheries value and consequentially improve conservation efforts. Generalised additive models were used to model the probability of occurrence of six focal species after surveys that utilised two remote underwater video sampling methods (i.e. baited and towed video). Models developed for the towed video method had consistently better predictive performance for all but one study species although only three models had a good to fair fit, and the rest were poor fits, highlighting the challenges associated with modelling habitat associations of marine species in highly homogenous, low relief environments. Models based on baited video dataset regularly included large-scale measures of structural complexity, suggesting fish attraction to a single focus point by bait. Conversely, models based on the towed video data often incorporated small-scale measures of habitat complexity and were more likely to reflect true species-habitat relationships. The cost associated with use of the towed video systems for surveying low-relief seascapes was also relatively low providing additional support for considering this method for marine spatial ecological modelling