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

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^?2), 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.     

Realizing the potential of trait‐based approaches to advance fisheries science

Analysing how fish populations and their ecological communities respond to perturbations such as fishing and environmental variation is crucial to fisheries science. Researchers often predict fish population dynamics using species‐level life‐history parameters that are treated as fixed over time, while ignoring the impact of intraspecific variation on ecosystem dynamics. However, there is increasing recognition of the need to include processes operating at ecosystem levels (changes in drivers of productivity) while also accounting for variation over space, time and among individuals. To address similar challenges, community ecologists studying plants, insects and other taxa increasingly measure phenotypic characteristics of individual animals that affect fitness or ecological function (termed “functional traits”). Here, we review the history of trait‐based methods in fish and other taxa, and argue that fisheries science could see benefits by integrating trait‐based approaches within existing fisheries analyses. We argue that measuring and modelling functional traits can improve estimates of population and community dynamics, and rapidly detect responses to fishing and environmental drivers. We support this claim using three concrete examples: how trait‐based approaches could account for time‐varying parameters in population models; improve fisheries management and harvest control rules; and inform size‐based models of marine communities. We then present a step‐by‐step primer for how trait‐based methods could be adapted to complement existing models and analyses in fisheries science. Finally, we call for the creation and expansion of publicly available trait databases to facilitate adapting trait‐based methods in fisheries science, to complement existing public databases of life‐history parameters for marine organisms.     

Do habitat models accurately predict the depth distribution of pelagic fishes?

Habitat models are used to correct estimates of fish abundance derived from pelagic longline fishing gear. They combine information on hook depth with the species’ preferences for ambient environmental conditions to adjust the gear's catchability. We compare depth distributions of bigeye tuna (Thunnus obesus) catch predicted by a habitat model with distributions derived from data collected by observers on longliners in the tropical Pacific Ocean. Our analyses show that the habitat model does not accurately predict the depth distribution of bigeye tuna; its predictions are worse than those from models that assume no effect of depth on catches. Statistical models provided superior fits to the observed depth distribution. The poor performance of the habitat model is probably due to (1) problems in estimating hook depth, (2) fine‐scale variations in environmental conditions, (3) incomplete knowledge of habitat preferences and (4) differences between the distribution of bigeye tuna and their vulnerability to longline gear.     

European small pelagic fish distribution under global change scenarios

The spectre of increasing impacts on exploited fish stocks in consequence of warmer climate conditions has become a major concern over the last decades. It is now imperative to improve the way we project the effects of future climate warming on fisheries. While estimating future climate‐induced changes in fish distribution is an important contribution to sustainable resource management, the impacts on European small pelagic fish—representing over 50% of the landings in the Mediterranean and Black Sea between 2000 and 2013—are yet largely understudied. Here, we investigated potential changes in the spatial distribution of seven of the most harvested small pelagic fish species in Europe under several climate change scenarios over the 21st century. For each species, we considered eight Species Distribution Models (SDMs), five General Circulation Models (GCMs) and three emission scenarios (the IPCC Representative Concentration Pathways; RCPs). Under all scenarios, our results revealed that the environmental suitability for most of the seven species may strongly decrease in the Mediterranean and western North Sea while increasing in the Black and Baltic Seas. This potential northward range expansion of species is supported by a strong convergence among projections and a low variability between RCPs. Under the most pessimistic scenario (RCP8.5), climate‐related local extinctions were expected in the south‐eastern Mediterranean basin. Our results highlight that a multi‐SDM, multi‐GCM, multi‐RCP approach is needed to produce more robust ecological scenarios of changes in exploited fish stocks in order to better anticipate the economic and social consequences of global climate change.     

Modelling the potential distribution of two tropical freshwater fish species under climate change scenarios

1. Climate change is causing shifts in the distribution patterns of freshwater fish at various spatio-temporal scales. Tropical freshwater fish are vulnerable, especially in areas where a high impact of climate change is predicted; thus, there is an increasing need to predict these shifts to determine conservation and adaptation strategies.
2. Ecological niche models offer a reliable way to predict the effects of climate change on species distribution. Potential shifts in the distribution of tropical fish were tested under two scenarios (4.5 – moderate and 8.5 – extreme) with three general circulation models for years 2050 and 2070 using maximum entropy software using as models two predatory species – the tropical gar Atractosteus tropicus and the giant cichlid Petenia splendida.
3. The potential distribution of both species was associated with warm and humid–sub-humid conditions. Future projections showed a higher availability of suitable areas for both species resulting from the expansion of warmer conditions in the middle and upper basins of the Central American mountain range and centre of the Yucatan Peninsula.
4. Ecological niche models of keystone or umbrella species such as A. tropicus and P. splendida could be useful to support conservation plans of protected areas. The potential distribution of both species covers areas of high suitability including six important biosphere reserves in Mexico, three protected areas in Guatemala and part of the Mesoamerican biological corridor.
5. Despite the potential expansion of the present distribution range suggested by the models, it is important to consider the biological and ecological requirements of the species and the ecological implications of these potential shifts in distribution. Both scenarios could have several implications at genetic, population, and ecosystem levels.

     

Identifying potential causes of fish declines through local ecological knowledge of fishers in the Ganga River,eastern Bihar,India

Local ecological knowledge (LEK) can offer insights into fisheries management by describing long‐term changes that are difficult to unravel in data‐poor river‐floodplain fisheries. LEK is derived from complex interactions between fishers’ observations of environmental change and their institutional capacities to manage fisheries. Hence, it is important to understand where and how LEK and formal scientific studies on fish species’ decline could complement each other. In this paper, the causes of decline of 58 fish and two shrimp taxa were identified from LEK data (1999–2019) obtained from river–floodplain fisheries of the Gangetic plains (Bihar, India). Qualitative analyses of LEK were used to generate species‐specific hypotheses and historical insights on their declines. Destructive fishing, overfishing and the Farakka barrage were cited by fishers as the major causes of declines. Potential reasons for these perceptions were explored in relation to fishers’ experiences of conflicts in the region over fishing rights and access.     

Species‐specific distribution model may be not enough: The case study of bottlenose dolphin (Tursiops truncatus) habitat distribution in Pelagos Sanctuary

1. Geospatial models are used to predict the distribution of terrestrial and marine species, according to their ecological and ethological habits. The bottlenose dolphin is a cosmopolitan marine top predator, inhabiting most of the ocean, with the exception of polar and subpolar waters. This wide distribution is associated with a remarkable plasticity in ecological and behavioural habits, which makes it difficult to model and predict its distribution.
2. This study proposes a ‘multi‐type approach’ to predict the presence and distribution of the bottlenose dolphin in the Pelagos Sanctuary, a Specially Protected Area of Mediterranean Importance located in the north‐west Mediterranean Sea. A multi‐type model based on random forest regression was developed, analysing the distribution habits of two geographical units living in the Pelagos area.
3. When compared with a classical single‐type model, the multi‐type model performed much better in a prediction test (true skill statistics, TSS = 85% vs. 52%), confirming the value of this experimental approach. This work suggests that wild species should not be considered as one single‐type entity, as local specialization may change and shape their distribution habits.

     

Larval fish assemblages and mesoscale oceanographic structure along the Mesoamerican Barrier Reef System

The Mesoamerican Barrier Reef System (MBRS) is of high ecological and economic importance to the western Caribbean region, and contains spawning sites for a number of reef fish species. Despite this, little is known of the distribution and transport of pelagic fish larvae in the area, and basic in situ information on larval fish assemblages is lacking. Here we describe the results of two biological oceanography research cruises conducted in winter‐spring of 2006 and 2007, focusing on larval fish assemblages. We use multivariate assemblage analyses to examine vertical and horizontal distribution characteristics of larval fish assemblages, to highlight key distinguishing taxa, and to relate these to the observed oceanographic structure. Our results showed a general separation between the Gulf of Honduras region, which was characterized by weaker currents and high abundances of inshore and estuarine taxa (Eleotridae, Priacanthidae), and the northern MBRS, which was subject to strong northward flow and contained a mixture of mesopelagic and reef‐associated taxa (Myctophidae, Sparidae). Although distinct patterns of vertical distribution were observed among taxa, both shallow and deep living larvae were broadly distributed throughout the study area. Analysis of historical drifter tracks highlighted the strong northward flow and low retention conditions typically present along the northern MBRS, as well as potential connectivity between the western Caribbean Sea, the Gulf of Mexico and the Atlantic Ocean.     

Freshwater ecosystem vulnerability: Is native climatic niche good enough to predict invasion events?

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Common trends in recruitment dynamics of north‐east Atlantic fish stocks and their links to environment,ecology and management

Recruitment dynamics are challenging to assess or predict because of the many underlying drivers that vary in their relevance over time and space. Stock size, demographic and trait composition, condition and distribution of spawning fish and the spatio‐temporal dynamics of trophic and environmental interactions all influence recruitment processes. Exploring common patterns among stocks and linking them to potential drivers may therefore provide insights into key mechanisms of recruitment dynamics. Here, we analysed stock‐recruitment data of 64 stocks from the north‐east Atlantic Ocean for common trends in variation and synchrony among stocks using correlation, cluster and dynamic factor analyses. We tested common trends in recruitment success for relationships with large‐scale environmental processes as well as stock state indicators, and we explored links between recruitment success and demographic, environmental and ecological variables for a subset of individual stocks. The results revealed few statistically significant correlations between stocks but showed that underlying common trends in recruitment success are linked to environmental indices and management indicators. Statistical analyses confirmed previously suggested relationships of environmental–ecological factors such as the subpolar gyre and Norwegian coastal current with specific stocks, and indicated a large relevance of spawning stock biomass and demographics, as well as predation, whereas other suggested relationships were not supported by the data. Our study shows that despite persistent challenges in determining drivers of recruitment due to poor data quality and unclear mechanisms, combining different data analysis techniques can improve our understanding of recruitment dynamics in fish stocks.     

Non‐native marine species in north‐west Europe: Developing an approach to assess future spread using regional downscaled climate projections

1. Climate change can affect the survival, colonization and establishment of non‐native species. Many non‐native species common in Europe are spreading northwards as seawater temperatures increase. The similarity of climatic conditions between source and recipient areas is assumed to influence the establishment of such species, however, in a changing climate those conditions are difficult to predict. 2. A risk assessment methodology has been applied to identify non‐native species with proven invasive qualities that have not yet arrived in north‐west Europe, but which could become problematic in the future. Those species with the highest potential to become established or be problematic have been taken forward, as well as some that may be economically beneficial, for species distribution modelling to determine future potential habitat distributions under projected climate change. 3. In the past, species distribution models have usually made use of low resolution global environmental datasets. Here, to increase the local resolution of the distribution models, downscaled shelf seas climate change model outputs for north‐west Europe were nested within global outputs. In this way the distribution model could be trained using the global species presence data including the species' native locations, and then projected using more comprehensive shelf seas data to understand habitat suitability in a potential recipient area. 4. Distribution modelling found that habitat suitability will generally increase further north for those species with the highest potential to become established or problematic. Most of these are known to be species with potentially serious consequences for conservation. With caution, a small number of species may present an opportunity for the fishing industry or aquaculture. The ability to provide potential future distributions could be valuable in prioritizing species for monitoring or eradication programmes, increasing the chances of identifying problem species early. This is particularly important for vulnerable infrastructure or protected or threatened ecosystems.     

Do spatial models of growth rate potential reflect fish growth in a heterogeneous environment? A comparison of model results

Abstract – Spatial models of fish growth rate potential have been used to characterize a variety of environments including estuaries, the North American Great Lakes, small lakes and rivers. Growth rate potential models capture a snapshot of the environment but do not include the effects of habitat selection or competition for food in their measures of environment quality. Here, we test the ability of spatial models of fish growth rate potential to describe the quality of an environment for a fish population in which individual fish may select habitats and local competition may affect per capita intake. We compare growth rate potential measurements to simulated fish growth and distributions of model fish from a spatially explicit individual-based model of fish foraging in the same model environment. We base the model environment on data from Lake Ontario and base the model fish population on alewife in the lake. The results from a simulation experiment show that changes in the model environment that caused changes in the average growth rate potential correlated extremely highly ( r ²≥0.97) with changes in simulated fish growth. Unfortunately, growth rate potential was not a reliable quantitative predictor of simulated fish growth nor of the fish spatial distribution. The inability of the growth rate potential model to quantitatively predict simulated fish growth and fish distributions results from the fact that growth rate potential does not consider the effects of habitat selection or of competition on fish growth or distribution, processes that operate in our individual-based model and presumably also operate in nature. The results, however, do support the use of growth rate potential models to describe the relative quality of habitats and environments for fish populations.     

Efficient spatial models for predicting the occurrence of subarctic estuarine‐associated fishes: implications for management

In many of the nearshore areas where development is most likely to occur, essential fish habitat data are incomplete and there is little information on species occurrence that can be used to inform management decisions. This research investigated the use of multivariate remotely sensed geomorphic and landscape data to develop accurate predictive models of subarctic, estuarine‐associated fishes. The random forest algorithm was used to predict the occurrence of 26 fish species captured in 49 estuaries in Southeast Alaska. Model prediction accuracy ranged from 100 to 42% for species presence and 87 to 15% for species absence. Model goodness of fit and accuracy were assessed by comparing the number of species occurrences predicted by the model against the observed presences and absences of species in an independent data set. Sixty percent of the models were able to predict species presence with an accuracy of 70% or better. The models were used to predict species occurrence for 521 unsampled Southeast Alaskan estuaries to provide a regional map of predicted species distributions.     

Forecast skill for predicting distribution shifts: A retrospective experiment for marine fishes in the Eastern Bering Sea

Forecasting distribution shifts under novel environmental conditions is a major task for ecologists and conservationists. Researchers forecast distribution shifts using several tools including: predicting from an empirical relationship between a summary of distribution (population centroid) and annual time series (“annual regression,” AR); or fitting a habitat‐envelope model to historical distribution and forecasting given predictions of future environmental conditions (“habitat envelope,” HE). However, surprisingly little research has estimated forecast skill by fitting to historical data, forecasting distribution shifts and comparing forecasts with subsequent observations of distribution shifts. I demonstrate the important role of retrospective skill testing by forecasting poleward movement over 1‐, 2‐ or 3‐year periods for 20 fish and crab species in the Eastern Bering Sea and comparing forecasts with observed shifts. I specifically introduce an alternative vector‐autoregressive spatio‐temporal (VAST) forecasting model, which can include species temperature responses, and compare skill for AR, HE and VAST forecasts. Results show that the HE forecast has 30%–43% greater variance than predicting that future distribution is identical to the estimated distribution in the final year (a “persistence” forecast). Meanwhile, the AR explains 2%–6% and VAST explains 8%–25% of variance in poleward movement, and both have better performance than a persistence forecast. HE and AR both generate forecast intervals that are too narrow, while VAST models with or without temperature have appropriate width for forecast intervals. Retrospective skill testing for more regions and taxa should be used as a test bed to guide future improvements in methods for forecasting distribution shifts.     

Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates

1. Species distribution models for marine organisms are increasingly used for a range of applications, including spatial planning, conservation, and fisheries management. These models have been constructed using a variety of mathematical forms and drawing on both physical and biological independent variables; however, what might be called first-generation models have mainly followed the form of linear models, or smoothing splines, informed by data collected in the context of fish surveys.
2. The performance of different classes of variables were tested in a series of species occurrence models built with machine learning methods, specifically evaluating the potential contribution of lower trophic level data. Random forest models were fitted based on the classification of the absence/presence for fish and macroinvertebrates surveyed on the US Northeast Continental Shelf.
3. The potential variables included physical, primary production, secondary production, and terrain variables. For accepted model fits, six variable importance measures were computed, which collectively showed that physical and secondary production variables make the greatest contribution across all models. In contrast, terrain variables made the least contribution to these models.
4. Multivariable analyses that account for all performance measures reinforce the role of water depth and temperature in defining species presence and absence; however, chlorophyll concentration and some specific zooplankton taxa, such as Metridia lucens and Paracalanus parvus, also make important contributions with strong seasonal variations.
5. Our results suggest that lower trophic level variables, if available, are valuable in the creation of species distribution models for marine organisms.

     

Effects of mountaintop mining on fish distributions in central Appalachia

Mountaintop mining with valley fills (MTM/VF) is the main source of landscape change in central Appalachia. While our knowledge of the local‐scale effects of MTM/VF on stream chemistry and biotic assemblages has recently improved, the effects at the landscape scale are less well known. In this study, we explore the effects of MTM/VF on the distributions of six fish species with contrasting ecologies in the upper Kentucky River basin, an area heavily affected by MTM/VF. Using a museum‐based data set of 239 occurrence records, land use/land cover data and boosted regression tree modelling, we were able to create robust predictive models for the focal species (AUCs = 0.82–0.93). Models explained from 41.2 to 71.9% of the variation in species distributions. We detected a marked negative influence of MTM/VF in four of the six species distribution models – with relative influences ranging from 5.9–12.7%. Species typically inhabiting faster‐flowing riffle and run mesohabitats appeared to respond more strongly to MTM/VF. Interestingly, the mean patch size of MTM/VF was more influential than the overall proportion of the watershed affected by MTM/VF in our models. Thus, our data suggest the spatial pattern of mining disturbance is very important in determining the cumulative impact of MTM/VF. Considering the central Appalachian region is a continental hot spot for freshwater biodiversity, establishing a firm understanding of the effects of MTM/VF at the landscape scale is essential if we wish to protect these natural resources.