Application of species distribution models to explain and predict the distribution, abundance and assemblage structure of nearshore temperate reef fishes

Spatial Change of Dominant Baltic Sea Demersal Fish Across Two Decades

The arange and biomass distribution of marine fish species offer insights into their underlying niches. Quantitative data are rare compared to occurrences and remain underused in species distribution models (SDMs) to explore realized niches-the actual space occupied by a species shaped by abiotic and biotic factors. Local densities drive differences in species contributions to ecological processes and ecosystem function rather than through presence alone. If a species growth rate is strongly controlled by macro-environmental conditions, then predicting geographical abundance or densities should be possible. We collated 20 years (2001-2020) of standardized scientific bottom trawl data to fit several versions of hierarchical generalized additive models using biomass (kg km-2) of four dominant demersal species (Common dab, European flounder, European plaice, Atlantic cod) within yearly and seasonal (winter and autumn) time windows. Covariates were represented with trawl-level geographic information (position, depth) and high-resolution oceanographic features. This work illustrates species-specific spatiotemporal biomass patterns across two decades and demonstrates superior predictive performance with seasonally variable smoothing terms, revealing seasonally different responses to oceanographic predictors. Firstly, we find relative stasis in Common dab biomass which is linked to the macro-environmental salinity gradient in the western Baltic Sea but with different temperature responses across seasons. Secondly, we show both European flounder and plaice have increased in biomass in the western Baltic Sea with different seasonal relationships to bottom temperature, and that flounder switches between salinity conditions based on season during spawning/feeding periods. Lastly, both juvenile and adult Atlantic cod life stages are shown to have declined most significantly in the Bornholm Deeps and the Gdańsk Deeps. For cod, we conclude that biomass was less reliably predicted in comparison to the other major Baltic demersals studied here, warranting dynamic fishing covariates as a formerly major commercial fishing target. These models approach more dynamic species distribution models and are increasingly valuable to constrain uncertainties in biogeographic forecasting which often rely on annually-averaged response curves, occurrence data, and suitability maps which rarely discriminate between areas of high and low biomass areas in space and time.

Nonparametric Prediction for Spatial Dependent Functional Data Under Fixed Sampling Design

In this work, we consider a nonparametric prediction of a spatiofunctional process observed under a non-random sampling design. The proposed predictor is based on functional regression and depends on two kernels, one of which controls the spatial structure and the other measures the proximity between the functional observations. It can be considered, in particular, as a supervised classification method when the variable of interest belongs to a predefined discrete finite set. The mean square error and almost complete (or sure) convergence are obtained when the sample considered is a locally stationary α-mixture sequence. Numerical studies were performed to illustrate the behavior of the proposed predictor. The finite sample properties based on simulated data show that the proposed prediction method outperformsthe classical predictor which not taking into account the spatial structure.

The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%-10% of the world's seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

Quantifying the statistical power of monitoring programs for marine protected areas

Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500-m ROV transects) and temporal replication (every 1-3 yr) to detect expected post-MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

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.

Applications of unmanned aerial vehicles in intertidal reef monitoring

Monitoring of intertidal reefs is traditionally undertaken by on-ground survey methods which have assisted in understanding these complex habitats; however, often only a small spatial footprint of the reef is observed. Recent developments in unmanned aerial vehicles (UAVs) provide new opportunities for monitoring broad scale coastal ecosystems through the ability to capture centimetre resolution imagery and topographic data not possible with conventional approaches. This study compares UAV remote sensing of intertidal reefs to traditional on-ground monitoring surveys, and investigates the role of UAV derived geomorphological variables in explaining observed intertidal algal and invertebrate assemblages. A multirotor UAV was used to capture <1 cm resolution data from intertidal reefs, with on-ground quadrat surveys of intertidal biotic data for comparison. UAV surveys provided reliable estimates of dominant canopy-forming algae, however, understorey species were obscured and often underestimated. UAV derived geomorphic variables showed elevation and distance to seaward reef edge explained 19.7% and 15.9% of the variation in algal and invertebrate assemblage structure respectively. The findings of this study demonstrate benefits of low-cost UAVs for intertidal monitoring through rapid data collection, full coverage census, identification of dominant canopy habitat and generation of geomorphic derivatives for explaining biological variation.