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Identifying priority areas for spatial management of mixed fisheries using ensemble of multi‐species distribution models
Abstract
Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e‐SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient‐boosting machine methods in a training and testing protocol. The e‐SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three‐dimensional physical‐biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e‐SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e‐SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.
... Further, they can often operate in areas that larger vessels cannot access, such as shallow water and fragile ecosystems (Liu et al., 2016), decreasing spatial sampling bias (Hughes et al., 2021). Equipped with several different sensors, USVs can simultaneously sample a range of environmental variables such as salinity, temperature, phytoplankton and depth of water column (Swart et al., 2016), important for generating informative spatial predictions of fish over time (Panzeri et al., 2023;Pennino et al., 2020;Rooper and Zimmermann, 2007). While large public databases (e.g. from E.U. ...
... Species distribution models (hereafter SDMs) comes in a wide range of approaches that integrate abundance and oceanographic data (Robinson et al., 2011), with the ability to predict if species are likely to occur in non-sampled locations or time periods (Panzeri et al., 2023;Pennino et al., 2020). SDMs are routinely used in Ecosystem-Based Fisheries Management, to provide valuable information on Essential Fish Habitats and Vulnerable Marine Ecosystems, as well as to inform protection and restoration strategies (Lauria et al., 2017;Panzeri et al., 2023). ...
... Species distribution models (hereafter SDMs) comes in a wide range of approaches that integrate abundance and oceanographic data (Robinson et al., 2011), with the ability to predict if species are likely to occur in non-sampled locations or time periods (Panzeri et al., 2023;Pennino et al., 2020). SDMs are routinely used in Ecosystem-Based Fisheries Management, to provide valuable information on Essential Fish Habitats and Vulnerable Marine Ecosystems, as well as to inform protection and restoration strategies (Lauria et al., 2017;Panzeri et al., 2023). They also help defining stock changes (Orio et al., 2019) and habitat suitability under projected climate change scenarios (Palermino et al., 2024;Panzeri et al., 2024). ...
... The authors conclude that area mortality from fishing vessels noise is unlikely for any fish species, eggs, or larvae located in Jabuka/ Pomo Pit FRA but there is a greater risk of masking and behavioural effects. Considering the high relevance of this area for the restocking of fishing grounds (Panzeri et al., 2023), the above considerations call for a deeper assessment of noise produced by fishing operations. ...
1. Observed data are often dependent on a measure of sampling effort, such as counts measured per unit area. A common tool to account for differences in effort is the ‘offset term’ in a generalized linear model, which allows for a fixed proportional relationship between effort and the response variable. However, there is limited detailed guidance on the application of offsets and transformations or when an estimated effort covariate might be more appropriate. 2. This article explores the parametrisation and implementation of the offset term, plus additional methods to account for sampling effort in regression models. We evaluate the performance of offsets and covariates across various data characteristics through simulation. 3. When uncertainty regarding the effort–response relationship exists, modelling sampling effort as a log-transformed covariate, ideally as a constrained smoother, is ideal because it covers most scenarios: a proportional relationship, a non-linear (e.g. saturated) relationship, and flexibility in multi-species or hurdle models (e.g. allowing effort to influence detection probability in a binomial model). I show that parameter recovery in effort-as-covariate models is generally robust in simple models, so a log-transformed offset is only advantageous when: a proportional relationship is well-supported, model complexity or data availability hinders covariate estimation, or non-linearity at data limits is uncertain. 4. Although our simulation showed reasonable performance of all sampling effort parameterisations, how to model effort remains a key decision, and one that benefits from considered thought before modelling occurs. The nature of the effort–response relationship (i.e. proportional, otherwise linear on the link or original scales, or non-linear), and how multiple effort variables could be included in the same model, will benefit from both statistical and practical contexts and experience.
Predicting range shifts of marine species under different CO2 emission scenarios is of paramount importance to understand spatial potential changes in a context of climate change and to ensure appropriate management, in particular in areas where resources are critical to fisheries. Important tools which use environmental variables to infer range limits and species habitat suitability are the species distribution models or SDMs. In this work, we develop an ensemble species distribution model (e-SDM) to assess past, present and future distributions under Representative Concentration Pathway (RCP) 8.5 of nine demersal species and hotspot areas for their two life stages (adult and juvenile) in the Adriatic and Western Ionian Seas in four time windows (1999-2003, 2014-2018, 2031-2035 and 2046-2050). The e-SDM has been developed using three different models (and sub-models), i.e. (i) generalized additive models (GAM), (ii) generalized linear mixed model (GLMM), (iii) gradient boosting machine (GBM), through the combination of density data in terms of numbers of individuals km² and environmental variables. Then, we have determined the changes in the aggregation hotspots and distributions. Finally, we assess gains and losses areas (i.e. occupation area) in the future climate change scenario as new potential range shifts for the nine species and their life stages. The results show that densities of some key commercial species, such as Merluccius merluccius (European hake), Mullus barbatus (red mullet), and Lophius budegassa (anglerfish) will be shifting northwards.
The depletion of the grey mullet population in the Taiwan Strait has generated interest in conservation initiatives and to enhance the effectiveness of restoration initiatives, it is essential to comprehend the specific habitat preferences of the species in question, particularly in relation to environmental changes. In this study, habitat suitability modelling was used to investigate the distribution patterns of grey mullet. Sea surface height (SSH) was the most significant predictor of the grey mullet standardized catch per unit effort (S.CPUE). Furthermore, sea surface temperature (SST) was the second most important oceanographic parameter, followed by mixed-layer depth (MLD). The grey mullet S.CPUE peaked in regions with the summed SST, Chla, salinity, SSH, MLD and current values being 20 °C, 0.9 mg/m3, 34.5 psu, 0.45 m, 18–38 m, and 0.25 m2/s2, respectively. From late October, HSI levels in the northern Taiwan ocean increased, with a consistent upward trend. The distribution expanded in November and December, focusing on the northern and mid-ocean regions west of Taiwan. HSI levels peak in January and February, especially in Taiwan’s coastal region. High HSI distribution shifts away from coastlines and diminishes in April. The findings of this study will contribute to the understanding of this specific species and the approach used in this study may be applicable to other fisheries stocks also.
The projected warming, nutrient decline, changes in net primary production, deoxygenation and acidification of the global ocean will affect marine ecosystems during the 21st century. Here, the climate change-related impacts on the marine ecosystems of the Mediterranean Sea in the middle and at the end of the 21st century are assessed using high-resolution projections of the physical and biogeochemical state of the basin under Representative Concentration Pathways (RCPs) 4.5 and 8.5. In both scenarios, the analysis shows changes in the dissolved nutrient contents of the euphotic and intermediate layers of the basin, net primary production, phytoplankton respiration and carbon stock (including phytoplankton, zooplankton, bacterial biomass and particulate organic matter). The projections also show uniform surface and subsurface reductions in the oxygen concentration driven by the warming of the water column and by the increase in ecosystem respiration as well as an acidification signal in the upper water column linked to the increase in the dissolved inorganic carbon content of the water column due to CO2 absorption from the atmosphere and the increase in respiration. The projected changes are stronger in the RCP8.5 (worst-case) scenario and, in particular, in the eastern Mediterranean due to the limited influence of the exchanges in the Strait of Gibraltar in that part of the basin. On the other hand, analysis of the projections under the RCP4.5 emission scenario shows a tendency to recover the values observed at the beginning of the 21st century for several biogeochemical variables in the second half of the period. This result supports the idea – possibly based on the existence in a system such as the Mediterranean Sea of a certain buffer capacity and renewal rate – that the implementation of policies for reducing CO2 emission could indeed be effective and could contribute to the foundation of ocean sustainability science and policies.
Abundance and distribution of commercial marine resources are influenced by environmental variables, which together with fishery patterns may also influence their catchability. However, Catch Per Unit Effort (CPUE) can be standardized in order to remove most of the variability not directly attributable to fish abundance. In the present study, Generalized Additive Models (GAMs) were used to investigate the effect of some environmental and fishery covariates on the spatial distribution and abundance of the Norway lobster Nephrops norvegicus within the Pomo/Jabuka Pits (Central Adriatic Sea) and to include those that resulted significant in a standardization process. N. norvegicus is a commercially important demersal crustacean, altering its catchability over the 24-h cycle and seasons according to its burrowing behavior. A historically exploited fishing ground for this species, since 2015 subject to specific fisheries management measures, is represented by the meso-Adriatic depressions, which are also characterized by particular oceanographic conditions. Both the species behaviour and the features of this study area influence the dynamics of the population offering a challenging case study for a standardization modelling approach. Environmental and catch data were obtained during scientific trawl surveys properly designed to catch N. norvegicus, thus improving the quality of the model input data. Standardization of CPUE from 2 surveys from 2012 to 2019 was conducted building two GAMs for both biomass and density indices. Bathymetry, fishing pressure, dissolved oxygen and salinity proved to be significant drivers influencing catch distribution. After cross validations, the tuned models were then used to predict new indices for the study area and the two survey series by means of informed spatial grids, composed by constant surface cells, to each of which are associated average values of environmental parameters and specific levels of fishing pressure, depending on the management measures in place. The predictions can be used to better describe the structure and the spatio-temporal distribution of the population providing valuable information to evaluate the status of such an important marine resource.
International scientific fishery survey programmes systematically collect samples of target stocks’ biomass and abundance and use them as the basis to estimate stock status in the framework of stock assessment models. The research surveys can also inform decision makers about Essential Fish Habitat conservation and help define harvest control rules based on direct observation of biomass at the sea. However, missed survey locations over the survey years are common in long-term programme data. Currently, modelling approaches to filling gaps in spatiotemporal survey data range from quickly applicable solutions to complex modelling. Most models require setting prior statistical assumptions on spatial distributions, assuming short-term temporal dependency between the data, and scarcely considering the environmental aspects that might have influenced stock presence in the missed locations. This paper proposes a statistical and machine learning based model to fill spatiotemporal gaps in survey data and produce robust estimates for stock assessment experts, decision makers, and regional fisheries management organizations. We apply our model to the SoleMon survey data in North-Central Adriatic Sea (Mediterranean Sea) for 4 stocks: Sepia officinalis, Solea solea, Squilla mantis, and Pecten jacobaeus. We reconstruct the biomass-index (i.e., biomass over the swept area) of 10 locations missed in 2020 (out of the 67 planned) because of several factors, including COVID-19 pandemic related restrictions. We evaluate model performance on 2019 data with respect to an alternative index that assumes biomass proportion consistency over time. Our model’s novelty is that it combines three complementary components. A spatial component estimates stock biomass-index in the missed locations in one year, given the surveyed location’s biomass-index distribution in the same year. A temporal component forecasts, for each missed survey location, biomass-index given the data history of that haul. An environmental component estimates a biomass-index weighting factor based on the environmental suitability of the haul area to species presence. Combining these components allows understanding the interplay between environmental-change drivers, stock presence, and fisheries. Our model formulation is general enough to be applied to other survey data with lower spatial homogeneity and more temporal gaps than the SoleMon dataset.
Fisheries and aquaculture are facing many challenges worldwide, especially adaptation to climate change. Investigating future distributional changes of largely harvested species has become an extensive research topic, aiming at providing realistic ecological scenarios on which to build management measures, to help fisheries and aquaculture adapt to future climate-driven changes. Here, we use an ensemble modelling approach to estimate the contemporary and future distributional range of eight demersal fish species of high economic value in the Mediterranean Sea. We identify a cardinal influence of (i) temperature on fish species distributions, all being shaped by yearly mean and seasonality in sea bottom temperature, and (ii) the primary production. By assessing the effects of changes in future climate conditions under three Representative Concentration Pathway (RCP2.6, RCP4.5 and RCP8.5) scenarios over three periods of the twenty-first century, we project a contraction of the distributional range of the eight species in the Mediterranean Sea, with a general biogeographical displacement towards the North European coasts. This will help anticipating changes in future catch potential in a warmer world, which is expected to have substantial economic consequences for Mediterranean fisheries.
Geostatistical data—spatially referenced observations related to some continuous spatial phenomenon— are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian random fields, are often computationally intensive and challenging to implement, interpret, and evaluate.
Here, we introduce the R package sdmTMB, which implements predictive-process SPDE- (stochastic partial differential equation) based spatial and spatiotemporal models. Estimation is conducted via maximum marginal likelihood with Template Model Builder (TMB) but can be extended to penalized likelihood or Bayesian inference. We describe the statistical model, illustrate the package’s use through two case studies, and compare the functionality, speed, and interface to related software.
We highlight advantages of using sdmTMB for this class of models: (1) sdmTMB provides a flexible interface familiar to users of glm() , lme4, glmmTMB, or mgcv; (2) estimation is often faster than alternatives; (3) sdmTMB provides simple out-of-sample cross validation; (4) non-stationary processes (time-varying and spatially varying coefficients) are easily constructed with a formula interface; and (5) sdmTMB includes features not available as a combination in related packages (e.g., penalized smoothers and break-point effects, anisotropy, abundance index standardization).
We hope that sdmTMB’s user-friendly interface will open this useful class of models to a wider audience within species distribution modelling and beyond.
Developing Species Distribution Models (SDM) for marine exploited species is a major challenge in fisheries ecology. Classical modelling approaches typically rely on fish research survey data. They benefit from a standardized sampling design and a controlled catchability, but they usually occur once or twice a year and they may sample a relatively small number of spatial locations. Spatial monitoring of commercial data (based on logbooks crossed with Vessel Monitoring Systems) can provide an additional extensive data source to inform fish spatial distribution.
We propose a spatial hierarchical framework integrating both data sources while accounting for preferential sampling (PS) of commercial data.
From simulations, we demonstrate that PS should be accounted for in estimation when PS is actually strong. When commercial data far exceed scientific data, the later bring little information to spatial predictions in the areas sampled by commercial data, but bring information in areas with low fishing intensity and provide a validation dataset to assess the integrated model consistency. We applied the framework to three demersal species (hake, sole, and squids) in the Bay of Biscay that emphasize contrasted PS intensity and we demonstrate that the framework can account for several fleets with varying catchabilities and PS behaviours.
Ocean reanalyses integrate models and observations to provide a continuous and consistent reconstruction of the past physical and biogeochemical ocean states and variability. We present a reanalysis of the Mediterranean Sea biogeochemistry at a 1/24 • resolution developed within the Copernicus Marine Environment Monitoring Service (CMEMS) framework. The reanalysis is based on the Biogeochemical Flux Model (BFM) coupled with a variational data assimilation scheme (3DVarBio) and forced by the Nucleus for European Modeling of the Ocean (NEMO)-OceanVar physical reanalysis and European Centre for medium-range weather forecasts (ECMWF) reanalysis ERA5 atmospheric fields. Covering the 1999-2019 period with daily means of 12 published and validated biogeochemical state variables, the reanalysis assimilates surface chlorophyll data and integrates EMODnet data as initial conditions, in addition to considering World Ocean Atlas data at the Atlantic boundary, CO 2 atmospheric observations, and yearly estimates of riverine nutrient inputs. With the use of multiple observation sources (remote, in situ, and BGC-Argo), the quality of the biogeochemical reanalysis is qualitatively and quantitatively assessed at three validation levels including the evaluation of 12 state variables and fluxes and several process-oriented metrics. The results indicate an overall good reanalysis skill in simulating basin-wide values and variability in the biogeochemical variables. The uncertainty in reproducing observations at the mesoscale and weekly temporal scale is satisfactory for chlorophyll, nutrient, oxygen, and carbonate system variables in the epipelagic layers, whereas the uncertainty increases for a few variables (i.e., oxygen and ammonium) in the mesopelagic layers. The vertical dynamics of phytoplankton and nitrate are positively evaluated with specific metrics using BGC-Argo data. As a consequence of the continuous increases in temperature and salinity documented in the Mediterranean Sea over the last 20 years and atmospheric CO 2 invasion, we observe basin-wide biogeochemical signals indicating surface deoxygenation, increases in alkalinity, and dissolved inorganic carbon concentrations, and decreases in pH at the surface. The new, high-resolution reanalysis, open and freely available from the Copernicus Marine Service, allows users from different communities to investigate the spatial and temporal variability in 12 biogeochemical variables and fluxes at different scales (from Frontiers in Marine Science | www.frontiersin.org 1 November 2021 | Volume 8 | Article 741486 Cossarini et al. Reanalysis of Mediterranean Biogeochemistry the mesoscale to the basin-wide scale and from daily to multiyear scales) and the interaction between physical and biogeochemical processes shaping Mediterranean marine ecosystem functioning.
In order to be able to forecast the weather and estimate future climate changes in the ocean, it is crucial to understand the past and the mechanisms responsible for the ocean variability. This is particularly true in a complex area such as the Mediterranean Sea with diverse dynamics like deep convection and overturning circulation. To this end, effective tools are ocean reanalyses or reconstructions of the past ocean state. Here we present a new physical reanalysis of the Mediterranean Sea at high resolution, developed in the Copernicus Marine Environment Monitoring Service (CMEMS) framework. The hydrodynamic model is based on the Nucleus for European Modelling of the Ocean (NEMO) combined with a variational data assimilation scheme (OceanVar). The model has a horizontal resolution of 1/24° and 141 unevenly distributed vertical z* levels. It provides daily and monthly temperature, salinity, current, sea level and mixed layer depth as well as hourly fields for surface velocities and sea level. ECMWF ERA-5 atmospheric fields force the model and daily boundary conditions in the Atlantic are taken from a global reanalysis. The reanalysis covers the 33 years from 1987 to 2019. Initialized from SeaDataNet climatology in January 1985, it reaches a nominal state after a 2-years spin-up. In-situ data from CTD, ARGO floats and XBT are assimilated into the model in combination with satellite altimetry observations. This reanalysis has been validated and assessed through comparison to in-situ and satellite observations as well as literature climatologies. The results show an overall improvement of the comparison with observations and a better representation of the main dynamics of the region compared to a previous, lower resolution (1/16°), reanalysis. Temperature and salinity RMSD are decreased by respectively 14 and 18%. The salinity biases at depth of the previous version are corrected. Climate signals show continuous increase of the temperature and salinity, confirming estimates from observations and other reanalysis. The new reanalysis will allow the study of physical processes at multi-scales, from the large scale to the transient small mesoscale structures and the selection of climate indicators for the basin.
The presence of invasive species in the Mediterranean Sea is much higher than in other European seas, and understanding the reasons behind the range expansion of this invasive species is important for minimising any possible impacts to the already highly pressurised Mediterranean marine ecosystem. In this work, a brief description of sightings of the invasive lionfish Pterois miles (Bennett, 1828) in the Ionian Sea is provided, and a broad analysis of temperature patterns throughout the region is given to strengthen the understanding of lionfish range expansion in the Ionian Sea. As of 2018, lionfish expansion into the Ionian Sea was relatively abated, however a substantial increase in the temperature of the upper layers of the Eastern Mediterranean occurred during the late spring of 2018 and likely contributed towards the northward spread of this thermophilic species in the Ionian during 2019. Combining in-situ observations, CMEMS products, and citizen science data could assist in the monitoring of the range of this invasive species; a legal requirement for European countries under the Marine Strategy Framework Directive (Commission Decision (EU) 2017/848).
Biological invasions represent one of the main threats to marine biodiversity. From a conservation perspective, especially in the context of increasing sea warming, it is critical to examine the suitability potential of geographical areas for the arrival of Range Expanding Introduced and Native Species (REINS), and hence anticipate the risk of such species to become invasive in their new distribution areas. Here, we developed an empirical index, based on functional and bio‐ecological traits, that estimates the Invasive Potential (IP; i.e. the potential success in transport, introduction and population establishment) for a set of 13 fishes that are expanding their distributional range into the Mediterranean Sea, the most invaded sea in the world. The IP index showed significant correlation with the observed spreading of REINS. For the six species characterised by the highest IP, we calculated contemporary and future projections of their Environmental Suitability Index (ESI). By using an ensemble modelling approach, we estimated the geographical areas that are likely to be the most impacted by REINS spreading under climate change. Our results demonstrated the importance of functional traits related to reproduction for determining high invasion potential. For most species, we found high contemporary ESI values in the South‐eastern Mediterranean Sea and low to intermediate contemporary ESI values in the Adriatic Sea and North‐western Mediterranean sector. Moreover, we highlighted a major potential future expansion of high ESI values, and thus REINS invasive potential, towards the northern Mediterranean, especially in the northern Adriatic Sea. This potential future northward expansion highlights the risk associated with climate‐induced impacts on ecosystem conservation and fish stock management throughout the entire Mediterranean Sea.
To conserve global biodiversity, countries must forge equitable alliances that support sustainability in traditional pastoral lands, fisheries-management areas, Indigenous territories and more. To conserve global biodiversity, countries must forge equitable alliances that support sustainability in traditional pastoral lands, fisheries-management areas, Indigenous territories and more.
Benthic habitats of the deep Mediterranean Sea and the biodiversity they host are increasingly jeopardized by increasing human pressures, both direct and indirect, which encompass fisheries, chemical and acoustic pollution, littering, oil and gas exploration and production and marine infrastructures (i.e., cable and pipeline laying), and bioprospecting. To this, is added the pervasive and growing effects of human-induced perturbations of the climate system. International frameworks provide foundations for the protection of deep-sea ecosystems, but the lack of standardized criteria for the identification of areas deserving protection, insufficient legislative instruments and poor implementation hinder an efficient set up in practical terms. Here, we discuss the international legal frameworks and management measures in relation to the status of habitats and key species in the deep Mediterranean Basin. By comparing the results of a multi-criteria decision analysis (MCDA) and of expert evaluation (EE), we identify priority deep-sea areas for conservation and select five criteria for the designation of future protected areas in the deep Mediterranean Sea. Our results indicate that areas (1) with high ecological relevance (e.g., hosting endemic and locally endangered species and rare habitats),(2) ensuring shelf-slope connectivity (e.g., submarine canyons), and (3) subject to current and foreseeable intense anthropogenic impacts, should be prioritized for conservation. The results presented here provide an ecosystem-based conservation strategy for designating priority areas for protection in the deep Mediterranean Sea.
Machine learning algorithms have become very popular for spatial mapping of the environment due to their ability to fit nonlinear and complex relationships. However, this ability comes with the disadvantage that they can only be applied to new data if these are similar to the training data. Since spatial mapping requires predictions to new geographic space which in many cases goes along with new predictor properties, a method to assess the area to which a prediction model can be reliably applied is required.
Here, we suggest a methodology that delineates the ‘area of applicability’ (AOA) that we define as the area where we enabled the model to learn about relationships based on the training data, and where the estimated cross‐validation performance holds. We first propose a ‘dissimilarity index’ (DI) that is based on the minimum distance to the training data in the multidimensional predictor space, with predictors being weighted by their respective importance in the model. The AOA is then derived by applying a threshold which is the (outlier‐removed) maximum DI of the training data derived via cross‐validation. We further use the relationship between the DI and the cross‐validation performance to map the estimated performance of predictions. We illustrate the approach in a simulated case study chosen to mimic ecological realities and test the credibility by using a large set of simulated data.
The simulation studies showed that the prediction error within the AOA is comparable to the cross‐validation error of the trained model, while the cross‐validation error does not apply outside the AOA. This applies to models being trained with randomly distributed training data, as well as when training data are clustered in space and where spatial cross‐validation is applied. Using the relationship between DI and cross‐validation performance showed potential to limit predictions to the area where a user‐defined performance applies.
We suggest to add the AOA computation to the modeller's standard toolkit and to present predictions for the AOA only. We further suggest to report a map of DI‐dependent performance estimates alongside prediction maps and complementary to (cross‐)validation performance measures and the common uncertainty estimates.
Fish habitats sustain essential functions for fish to complete their life cycle, such as feeding, growing and spawning. Conservation is crucial to maintain fish populations and their exploitation. Since 2013, the spawning stock biomass of the Northern stock of European seabass (Dicentrarchus labrax) has been in a worrying state. A series of low recruitments with a persistently high level of fishing has been blamed, raising concerns about the processes involved in seabass reproduction and settlement in nurseries. Here, we characterise seabass spawning areas along the French Atlantic coast using Vessel Monitoring System (VMS) data. A non-linear geostatistical approach was applied, from 2008 to 2014, to detect locations where seabass aggregate for spawning. Occurrence maps of spawning distribution were combined into probability maps to quantify the seasonal and inter-annual variability and to highlight recurrent, occasional and unfavourable spawning areas. We identified three main spawning areas: the Rochebonne Plateau in the Bay of Biscay, the Western English Channel and the North of the Cotentin peninsula in the Eastern English Channel. The correlative link between this geographical distribution and environmental factors was investigated using a Bayesian spatio-temporal model. The spatio-temporal structure accounted for the vast majority of the model predictive skills, whereas environmental covariates had a negligible effect. Our model revealed the persistence of the spatial distribution of spawning areas with intra- and inter-annual variability. Offshore areas appear to be essential spawning areas for seabass, and should be considered in spatial management strategies.
Current fishing practices often do not allow adequate selection of species or sizes of fish, resulting in unwanted catches, subsequently discarded, with the consequent negative effects on both marine communities and fisheries profitability. The cross-analysis of density patches of potential unwanted catches and distribution of fishing effort can support the identification of spatial-temporal hot-spots in which the fishing pressure should be reduced to limit the amount of discards. The MinouwApp represents a technological and methodological framework to bring different, and structurally complex, sources of georeferenced data together into a simple visual interface aiming to interactively explore temporal ranges and areas of interest. The objective is to improve the understanding of fisheries dynamics, including discards, thus contributing to the implementation of discard management plans in a context of participative, ecosystem-based fisheries management strategies.
Long-term data series (1971–2015) of physical and biogeochemical parameters were analyzed in order to assess trends and variability of oceanographic conditions in the northern Adriatic Sea (NAS), a mid-latitude shallow continental shelf strongly impacted by river discharges, human activities and climate changes. Interpolation maps and statistical models were applied to investigate seasonal and spatial variability, as well as decadal trends of temperature, salinity, chlorophyll-a and nutrients. This analysis shows that sea surface temperature increased by +0.36% year−1 over four decades. Annual mean flow of the Po River markedly changed due to the occurrence of periods of persistent drought, whereas the frequency of flow rates higher than 3000 m3 s−1 decreased between 2006 and 2015. Moreover, we observed a long-term decrease in surface phosphate concentrations in Po River water (−1.34% year−1) and in seawater (in summer −2.56% year−1) coupled, however, to a significant increase in nitrate concentration in seawater (+3.80% year−1) in almost all seasons. These changes indicate that the nutrient concentrations in the NAS have been largely modulated, in the last forty years, by the evolution of environmental management practices and of the runoff. This implies that further alteration of the marine environment must be expected as a consequence of the climate changes.
Spatial distributions of marine fauna are determined by complex interactions between environmental conditions and animal behaviors. As climate change leads to warmer, more acidic, and less oxygenated oceans, species are shifting away from their historical distribution ranges, and these trends are expected to continue into the future. Correlative Species Distribution Models (SDMs) can be used to project future habitat extent for marine species, with many different statistical methods available. However, it is vital to assess how different statistical methods behave under novel environmental conditions before using these models for management advice, and to consider whether future projections based on these techniques are biologically reasonable. In this study, we built SDMs for adults and larvae of two ecologically important pelagic fishes in the California Current System (CCS): Pacific sardine (Sardinops sagax) and northern anchovy (Engraulis mordax). We used five different SDM methods, ranging from simple [thermal niche model (TNM)] to complex (artificial neural networks). Our results show that some SDMs trained on data collected between 2003 and 2013 lost substantial predictive skill when applied to observations from more recent years, when ocean temperatures associated with a marine heatwave were outside the range of historical measurements. This decrease in skill was particularly apparent for adult sardine, which showed non-stationary relationships between catch locations and sea surface temperature (SST) through time. While sardine adults and larvae shifted their distributions markedly during the marine heatwave, anchovy largely maintained their historical spatiotemporal distributions. Our results suggest that correlative relationships between species and their environment can become unreliable during anomalous conditions. Understanding the underlying physiology of marine species is therefore essential for the construction of SDMs that are robust to rapidly changing environments. Developing distribution models that offer skillful predictions into the future for species such as sardine and anchovy, which are migratory and include separate sub-stocks, may be particularly challenging.
In the Northwestern Mediterranean Sea, the European sardine (Sardina pilchardus) and the European anchovy (Engraulis encrasicolus) are the most important small pelagic fish in terms of biomass and commercial interest. During the last years, these species have experimented changes in their abundance and biomass trends in the Northwestern Mediterranean Sea, in addition to changes in growth, reproduction and body condition. These species are particularly sensitive to environmental fluctuations with possible cascading effects as they play a key role in connecting the lower and upper trophic levels of marine food webs. It is therefore essential to understand the factors that most profoundly affect sardine and anchovy dynamics. This study used a two-step approach to understand how the environment influences the adult stages of these species in the Northwestern Mediterranean Sea. First, we explored the effects of environmental change over time using Random Forests and available datasets of species occurrence, abundance, biomass and landings. We then applied species distribution models to test the impact of the extreme pessimistic and optimistic Intergovernmental Panel on Climate Change (IPCC) pathway scenarios, and to identify possible climate refuges: areas where these species may be able to persist under future environmental change. Findings from the temporal modeling showed mixed effects between environmental variables and for anchovy and sardine datasets. Future pathway projections highlight that both anchovy and sardine will undergo a reduction in their spatial distributions due to future climate conditions. The future climate refuges are the waters around the Rhone River (France) and the Ebro River (Spain) for both species. This study also highlights important knowledge gaps in our understanding of the dynamics of small pelagic fish in the region, which is needed to progress towards an ecosystem approach to fisheries management.
The MEDITS programme started in 1994 in the Mediterranean with the cooperation among research institutes from four countries: France, Greece, Italy and Spain. Over the years, until the advent of the European framework for the collection and management of fisheries data (the Data Collection Framework, DCF), new partners from Slovenia, Croatia, Albania, Montenegro, Malta and Cyprus joined MEDITS. The FAO regional projects facilitated the cooperation with non-European countries. MEDITS applies a common sampling protocol and methodology for sample collection, data storage and data quality checks (RoME routines). For many years, MEDITS represented the most important data source supporting the evaluation of demersal resources by means of population and community indicators, assessment and simulation models based on fishery-independent data. With the consolidation of the DCF, MEDITS routinely provides abundance indices of target species for tuning stock assessment models of intermediate complexity. Over the years, the survey scope has broadened from the population of demersal species to their fish community and ecosystems, and it has faced new challenges, such as the identification of essential fish habitats, providing new scientific insights linked to the Marine Strategy Framework Directive (e.g. biodiversity, trophic webs, allochthonous species and marine macro-litter evaluations) and to the ecosystem approach to fishery and marine spatial planning.
The state of marine systems subject to natural or anthropogenic impacts can be generally summarized by suites of ecological indicators carefully selected to avoid redundancy. Length-based indicators capture the status of fish community structure, fulfilling the Marine Strategy Framework Directive (MSFD) requirement for Descriptor 3 (status of commercial fish species). Although the MSFD recommends the development of regional indicators, a comparison among alternative length-based indicators is so far missing for the Mediterranean Sea. Using principal component analysis and dynamic factor analysis, we identified the most effective subset of length-based indicators, whether or not based on maximum length. Indicator trends and time series of fishing effort and environmental variables are also compared in order to highlight the individual and combined capability of indicators to track system changes across geographical sub-areas. Two indicators, typical length and mean maximum length, constitute the smallest set of non-redundant indicators, capturing together 87.45% of variability. Only in combination can these indicators disentangle changes in the fish community composition from modifications of size structure. Our study supports the inclusion of typical length among the regional MSFD Descriptor 3 indicators for the Medi-terranean Sea. Finally, we show dissimilarity between the western and eastern-central Mediterranean, suggesting that there are sub-regional differences in stressors and community responses.
The present study provides updated information on the occurrence, abundance and biomass distribution patterns and length frequencies of Merluccius merluccius in the Mediterranean Sea, by analysing a time series of data from the Mediterranean International Trawl Surveys (MEDITS) from 1994 to 2015. The highest values of abundance and biomass were observed in the Sardinian Seas. The use of a generalized additive model, in which standardized biomass indices (kg km–2) were analysed as a function of environmental variables, explained how ecological factors could affect the spatio-temporal distribution of European hake biomass in the basin. High biomass levels predicted by the model were observed especially at 200 m depth and between 14°C and 18°C, highlighting the preference of the species for colder waters. A strong reduction of biomass was observed since the year 2009, probably due to the strengthening of the seasonal thermocline that had greatly reduced the availability of food. The general decrease in biomass of several stocks of anchovy and sardine, preys of European hake, might be indirectly connected to the decreasing biomass detected in the present study. The length analysis shows median values lower than 200 mm total length of most of the investigated areas.
The Mediterranean distributions of two species of anglerfish, the blackbellied anglerfish (Lophius budegassa) and the white anglerfish (Lophius piscatorius), were analysed from trawl survey data (MEDITS project – Spain, France, Italy and Greece) from 2006 to 2015 implementing a Delta model approach with residuals autocovariate boosted regression trees. Sea bottom temperature (SBT), sea bottom salinity (SBS), bathymetry, slope of the seabed and distance to the coast were considered possible predictors. The results show that the locations with a higher presence, abundance and biomass of L. budegassa are those with a depth range between 150 to 300 m, with an SBT range between 17.5 and 18.5°C, and SBS of 3738 PSU. Similarly, L. piscatorius shows a higher probability of presence, abundance and biomass in location with a bathymetry range of 200-400 m, an SBT of 17.5°C to 18.5°C and an SBS of 36.5 to 37.5. Our results identify preference habitats for the anglerfishes in the Mediterranean Sea such as the Aegean Sea, the Gulf of Lions, south and southeast Spain and the northwestern Ionian Sea. In general terms, these findings enhance our understanding of the differences in the spatio-temporal distribution of these two species, providing useful information that can help their fisheries management and conservation.
Species distribution models (SDMs) are a common approach to describing species’ space‐use and spatially‐explicit abundance. With a myriad of model types, methods and parameterization options available, it is challenging to make informed decisions about how to build robust SDMs appropriate for a given purpose. One key component of SDM development is the appropriate parameterization of covariates, such as the inclusion of covariates that reflect underlying processes (e.g. abiotic and biotic covariates) and covariates that act as proxies for unobserved processes (e.g. space and time covariates). It is unclear how different SDMs apportion variance among a suite of covariates, and how parameterization decisions influence model accuracy and performance. To examine trade‐offs in covariation parameterization in SDMs, we explore the attribution of spatiotemporal and environmental variation across a suite of SDMs. We first used simulated species distributions with known environmental preferences to compare three types of SDM: a machine learning model (boosted regression tree), a semi‐parametric model (generalized additive model) and a spatiotemporal mixed‐effects model (vector autoregressive spatiotemporal model, VAST). We then applied the same comparative framework to a case study with three fish species (arrowtooth flounder, pacific cod and walleye pollock) in the eastern Bering Sea, USA. Model type and covariate parameterization both had significant effects on model accuracy and performance. We found that including either spatiotemporal or environmental covariates typically reproduced patterns of species distribution and abundance across the three models tested, but model accuracy and performance was maximized when including both spatiotemporal and environmental covariates in the same model framework. Our results reveal trade‐offs in the current generation of SDM tools between accurately estimating species abundance, accurately estimating spatial patterns, and accurately quantifying underlying species–environment relationships. These comparisons between model types and parameterization options can help SDM users better understand sources of model bias and estimate error.
Trawling pressure and environmental changes may affect the composition of fish assemblages. Our knowledge on large spatio-temporal patterns of demersal fish composition remains incomplete for the Mediterranean Sea. We investigated (1) the spatio-temporal stability of demersal assemblages, (2) the relationships between these assemblages and potential structuring factors (trawling pressure and environmental conditions) in order to assess the dynamic of the assemblage structure at the scale of the northern Mediterranean Sea. We analysed a dataset of 18062 hauls from 10 to 800 m depth performed annually during the last two decades across 17 Geographical Sub-Areas (GSAs) (MEDITS program). A multi-table analysis (STATICO-CoA) evidenced a strong inter-GSAs stability in the organization of assemblages, with specificities for some GSAs. The most stable structuring factors were linked to combined gradients of chlorophyll a, phytoplancton carbon biomass and temperature, inversely correlated with depth, salinity and nutrient gradients (axis 1 of the STATICO-CoA compromise, 93.74% of the total variability). A common pattern linking the distribution of species to these environmental gradients was evidenced for most of the 17 GSAs. Estimate of trawling pressure showed a minor role in the organization of the assemblages for the spatial scale and years investigated (axis 2, 4.67%).
In the last decades, the Mediterranean Sea experienced an increasing trend of fish stocks in overfishing status. Therefore, management actions to achieve a more sustainable exploitation of fishery resources are required and compelling. In this study, a spatially explicit multi-species bio-economic modeling approach, namely, SMART, was applied to the case study of central Mediterranean Sea to assess the potential effects of different trawl fisheries management scenarios on the demersal resources. The approach combines multiple modeling components, integrating the best available sets of spatial data about catches and stocks, fishing footprint from vessel monitoring systems (VMS) and economic parameters in order to describe the relationships between fishing effort pattern and impacts on resources and socio-economic consequences. Moreover, SMART takes into account the bi-directional connectivity between spawning and nurseries areas of target species, embedding the outcomes of a larvae transport Lagrangian model and of an empirical model of fish migration. Finally, population dynamics and trophic relationships are considered using a MICE (Models of Intermediate Complexity) approach. SMART simulates the fishing effort reallocation resulting from the introduction of different management scenarios. Specifically, SMART was applied to evaluate the potential benefits of different management approaches of the trawl fisheries targeting demersal stocks (deepwater rose shrimp Parapenaeus longirostris, the giant red shrimp Aristaeomorpha foliacea, the European hake Merluccius merluccius, and the red mullet Mullus barbatus) in the Strait of Sicily. The simulated management scenarios included a reduction of both fishing capacity and effort, two different sets of temporal fishing closures, and two sets of spatial fishing closures, defined involving fishers. Results showed that both temporal and spatial closures are expected to determine a significant improvement in the exploitation pattern for all the species, ultimately leading to the substantial recovery of spawning stock biomass for the stocks. Overall, one of the management scenarios suggested by fishers scored better and confirms the usefulness of participatory approaches, suggesting the need for more public consultation when dealing with resource management at sea.
The Mediterranean Sea is now recognized as a hotspot of global change, ranking among the fastest warming ocean regions. In order to project future plausible scenarios of marine biodiversity at the scale of the whole Mediterranean basin, the current challenge is to develop an explicit representation of the multispecies spatial dynamics under the combined influence of fishing pressure and climate change. Notwithstanding the advanced state-of-the-art modeling of food webs in the region, no previous studies have projected the consequences of climate change on marine ecosystems in an integrated way, considering changes in ocean dynamics, in phyto- and zoo-plankton productions, shifts in Mediterranean species distributions and their trophic interactions at the whole basin scale. We used an integrated modeling chain including a high-resolution regional climate model, a regional biogeochemistry model and a food web model OSMOSE to project the potential effects of climate change on biomass and catches for a wide array of species in the Mediterranean Sea. We showed that projected climate change would have large consequences for marine biodiversity by the end of the 21st century under a business-as-usual scenario (RCP8.5 with current fishing mortality). The total biomass of high trophic level species (fish and macroinvertebrates) is projected to increase by 5 and 22% while total catch is projected to increase by 0.3 and 7% by 2021–2050 and 2071–2100, respectively. However, these global increases masked strong spatial and inter-species contrasts. The bulk of increase in catch and biomass would be located in the southeastern part of the basin while total catch could decrease by up to 23% in the western part. Winner species would mainly belong to the pelagic group, are thermophilic and/or exotic, of smaller size and of low trophic level while loser species are generally large-sized, some of them of great commercial interest, and could suffer from a spatial mismatch with potential prey subsequent to a contraction or shift of their geographic range. Given the already poor conditions of exploited resources, our results suggest the need for fisheries management to adapt to future changes and to incorporate climate change impacts in future management strategy evaluation.
In this paper we consider what may happen to the marine ecosystem of Gran Canaria Island within the 2030 horizon, if fishing strategies different from those currently in place were implemented and we evaluate the effect of, for example, reduction of recreational–artisanal fishing, limitation of catches (e.g. total allowable catches, TAC), or spatial distribution of fishing sectors. From all scenarios tested, only those that significantly reduce the high effort of the recreational fishing would allow the recovery of the most exploited stocks in the marine ecosystem in the short and medium‐term. Moreover, the best management strategy, in contribution to abundance, was obtained with a scenario that has a spatial partition of exploitation rights between artisanal and recreational fishermen and includes no‐fishing zones (NTZ). This work is a first attempt to use spatial and temporal models to assess the effectiveness of alternative fishery policies in the Canary Islands.
The Global Deal for Nature (GDN) is a time-bound, science-driven plan to save the diversity and abundance of life on Earth. Pairing the GDN and the Paris Climate Agreement would avoid catastrophic climate change, conserve species, and secure essential ecosystem services. New findings give urgency to this union: Less than half of the terrestrial realm is intact, yet conserving all native ecosystems—coupled with energy transition measures—will be required to remain below a 1.5°C rise in average global temperature. The GDN targets 30% of Earth to be formally protected and an additional 20% designated as climate stabilization areas, by 2030, to stay below 1.5°C. We highlight the 67% of terrestrial ecoregions that can meet 30% protection, thereby reducing extinction threats and carbon emissions from natural reservoirs. Freshwater and marine targets included here extend the GDN to all realms and provide a pathway to ensuring a more livable biosphere.
The present work examines the spatio-temporal biomass trends of Mullus barbatus and Mullus surmuletus in the Mediterranean Sea through the analysis of a time series of data coming from the Mediterranean International Trawl Surveys (MEDITS), accomplished annually from 1994 to 2015. The biomass of both species showed clear declining trends below 150 to 200 m depth, which were steeper in the case of M. barbatus. Increases in temporal biomass trends were observed for M. barbatus from 2008 onward in most geographic sub-areas (GSAs), while stability was mostly observed for M. surmuletus. For both species, dynamic factor analysis revealed similarities among neighbouring GSAs and the subsequent cluster analysis identified two major GSA groups corresponding to the eastern and western basins of the Mediterranean. Overall, the results suggested that the combined effects of fishing and environmental conditions determine species abundance variations, but the relative importance of each component may vary among areas.
Aim
The idea of combining predictions from different models into an ensemble has gained considerable popularity in species distribution modelling, partly due to free and comprehensive software such as the R package BIOMOD. However, despite proliferation of ensemble models, we lack oversight of how and where they are used for modelling distributions, and how well they perform. Here, we present such an overview.
Location
Global.
Methods
Since BIOMOD is freely available and widely used by ensemble species distribution modellers, we focused on articles that apply BIOMOD, filtering the initial 852 papers identified in our structured literature search to a relevant final subset of 224 eligible peer‐reviewed journal articles.
Results
BIOMOD‐based ensembles are used across many taxa and locations, with terrestrial plants being the most represented group of species (n = 72) and Europe being the most represented continent (n = 106). These studies often focus on forecasting distributions in the future (n = 109), and commonly use presence‐only species data (n = 139) and climatic environmental predictors (n = 219). An average of six models are used in ensembles, and approximately half of ensembles weight contributions of models by their cross‐validation performance. However, discussion about choices made in the modelling process and unambiguous information on the performance of ensemble models versus individual models are limited. The use of independent data to validate model performance is particularly uncommon.
Main conclusions
We document the breadth of ensemble applications, but could not draw strong quantitative conclusions about the predictive performance of ensemble models, due to lack of unambiguous information reported. Understanding how and where ensembles are best used when modelling species distributions is important for enabling best choices for different applications. To enable this objective to be achieved, we provide recommendations for thorough reporting practices in a BIOMOD‐based ensemble workflow.
Benthic—pelagic coupling plays a pivotal role in aquatic ecosystems but the effects of fishery driven interactions on its functioning has been largely overlooked. Disentangling the benthic—pelagic links including effects of mixed fisheries, however, needs sketching a whole description of ecosystem interactions using quantitative tools. A holistic food web model has been here developed in order to understand the interplay between the benthic-pelagic coupling and mixed fisheries in a Mediterranean system such as the Strait of Sicily. The reconstruction of the food web required review and integration of a vast set of local and regional biological information from bacteria to large pelagic species that were aggregated into 72 functional groups. Fisheries were described by 18 fleet segments resulting from combination of fishing gears and fishing vessel size. The input-output analysis on the food web of energy pathways allowed identifying effects of biological and fishery components. Results showed that the structure of the Strait of Sicily food web is complex. Similarly to other Mediterranean areas, the food web of the Strait of Sicily encompasses 4.5 trophic levels (TLs) with the highest TLs reached by bluefin tuna, swordfish and large hake and largely impacted by bottom trawling and large longline. Importantly, benthic-pelagic coupling is affected by direct and indirect impacts among groups of species, fleets and fleets-species through the whole trophic spectrum of the food web. Moreover, functional groups able to move on large spatial scales or life history of which is spent between shelf and slope domains play a key role in linking subsystems together and mediate interactions in the Mediterranean mixed fisheries.
Crab species are economically and ecologically important in coastal ecosystems, and their spatial distributions are pivotal for conservation and fisheries management. This study was focused on modelling the spatial distributions of three Portunidae crabs (Charybdis bimaculata, Charybdis japonica, and Portunus trituberculatus) in Haizhou Bay, China. We applied three analytical approaches (Generalized additive model (GAM), random forest (RF), and artificial neural network (ANN)) to spring and fall bottom trawl survey data (2011, 2013–2016) to develop and compare species distribution models (SDMs). Model predictability was evaluated using cross-validation based on the observed species distribution. Results showed that sea bottom temperature (SBT), sea bottom salinity (SBS), and sediment type were the most important factors affecting crab distributions. The relative importance of candidate variables was not consistent among species, season, or model. In general, we found ANNs to have less stability than both RFs and GAMs. GAMs overall yielded the least complex response curve structure. C. japonica was more pronounced in southwestern portion of Haizhou Bay, and C. bimaculata tended to stay in offshore areas. P. trituberculatus was the least region-specific and exhibited substantial annual variations in abundance. The comparison of multiple SDMs was informative to understand species responses to environmental factors and predict species distributions. This study contributes to better understanding the environmental niches of crabs and demonstrates best practices for the application of SDMs for management and conservation planning.
When applied to structured data, conventional random cross‐validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.
We present the r package block CV , a new toolbox for cross‐validation of species distribution modelling. Although it has been developed with species distribution modelling in mind, it can be used for any spatial modelling.
The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.
Package block CV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.
Mixed fisheries are the dominant type of fishery worldwide. Overexploitation in mixed fisheries occurs when catches continue for available quota species while low quota species are discarded. As EU fisheries management moves to count all fish caught against quota (the "landing obligation"), the challenge is to catch available quota within new constraints, else lose productivity. A mechanism for decoupling exploitation of species caught together is spatial targeting, which remains challenging due to complex fishery and population dynamics. How far spatial targeting can go to practically separate species is often unknown and anecdotal. We develop a dimension-reduction framework based on joint dynamic species distribution modelling to understand how spatial community and fishery dynamics interact to determine species and size composition. In application to the highly mixed fisheries of the Celtic Sea, clear common spatial patterns emerge for three distinct assemblages. While distribution varies interannually, the same species are consistently found in higher densities together, with more subtle differences within assemblages, where spatial separation may not be practically possible. We highlight the importance of dimension reduction techniques to focus management discussion on axes of maximal separation and identify spatiotemporal modelling as a scientific necessity to address the challenges of managing mixed fisheries.
The objective of this study is to provide additional evidence of the utilization of Cold-Water Corals (CWC) and Submarine Canyons (SC) by fishes as areas for growing to maturity and to reproduce and thus for the classification of CWC as Essential Fish habitats (EFH). Data were collected with longline experimental surveys carried out during spring-summer and autumn-winter from 2010 to 2014, in different CWC sites distributed along the Apulian margin: Gondola Slide (GS), Bari Canyon (BC), Monopoli (Mn) and Santa Maria di Leuca (SML). In the present study the reproductive phase of Galeus melastomus, Conger conger, Heli-colenus dactylopterus, Merluccius merluccius, Pagellus bogaraveo and Phycis blennoides collected in the abovementioned CWC communities has been analysed with respect to fish size. Maturing and mature individuals as well as post-reproductive specimens of G. melastomus, H. dactylopterus and M. merluccius were observed in all the investigated CWC sites. Mature gonads were also found in the other three species, although the investigated period was outside their reproductive peak, indicating that these CWC sites act as spawning areas and therefore as a potential 'renewal network' for fish species exploited in the neighbouring fishing grounds. This provides a strong argument for the categorization of CWC as EFH in the design of management programs.
The spot-tail mantis shrimp, Squilla mantis (L., 1758), is very abundant in the northern and central Adriatic Sea (GSA 17), where it represents 66% of the national catches for this species. The main objectives of this study were to investigate some reproductive biology aspects and to provide a macroscopic sexual maturity scale to assess the maturation process of female by examination of ovaries and cement glands. The spawning period was from winter to spring and the sex ratio for the whole sample was always in favour of males except during autumn, when the females were not involved in spawning. The carapace length at first maturity for females was estimated at 25.36±0.21 mm.
The role of spatial management, including marine protected areas, in achieving fisheries outcomes alongside conservation goals is debated. In fisheries that fail to meet fishing mortality targets, closed areas are sometimes implemented to reduce fishing mortality. However, fisheries with stronger management, including rights‐based approaches that can address overcapacity and overfishing problems, often employ spatial management as well. Here, we compare the objectives, design, and performance of spatial management in nine temperate demersal fisheries in North America, Oceania, Europe, and Africa that employ rights‐based systems. Common objectives of spatial management included protecting habitat, juveniles, and spawners and reducing discards. Recovering age structure and creating scientific reference sites were less common objectives, despite being widely cited benefits of spatial management. Some fisheries adopted single closures to achieve single objectives, whereas others adopted diverse networks to achieve multiple objectives. Importantly, many spatial protections are implemented primarily through industry initiatives. Environmental change compromised the efficacy of spatial management in some cases, suggesting the need to design spatial management systems that are robust to changing ocean conditions. Fisheries with diverse and extensive spatial management systems have generally healthier fish stocks. Whether this implies that spatial management contributed substantially to fishery performance is unclear due to an absence of large‐scale, long‐term studies aimed at discerning different drivers of success. Although these targeted monitoring studies of closed areas are limited, such studies are necessary to help resolve the ongoing debate and to enable more purposeful design of spatial management for fisheries and conservation.
An exploration of the structure of demersal and benthopelagic assemblages in the north-western Ionian Sea was carried out by means of a set of statistical analyses. Self-organising map (SOM) and clustering methods applied to 183 taxa and their biomass (kg km⁻²) provided the classification of 1288 experimental hauls exploring the bathymetric range 10-800 m from 1995 to 2012. Six clusters were identified according to their similarities in species abundances (biomass), confirming the depth gradient as the main structuring agent. In order to identify key representative species in each cluster, the taxa were ranked by means of an indicator value index (IndVal) and the contribution of species to beta diversity (BD). Furthermore, the clusters were described by means of environmental and fishing characteristics. Particular habitat type, distance to canyon and fishing effort segregated the assemblages on the coastal shelf and slope. Temporal differences were detected in 2 bathyal groups, which were most likely affected by the 1990s environmental change in the deepwater circulation known as the Eastern Mediterranean Transient. The overall total BD in the study area was calculated as 0.79, with a temporal decrease observed at a rate of 0.7% yr⁻¹. The approaches used are useful to identify and characterize the species aggregations inside complex faunal assemblages, without a priori assumptions about data distribution. These results can be a starting point for defining functional groups for Mediterranean food web modelling approaches, as well as for identifying indicator species to assess the environmental status in the context of the Marine Strategy Framework Directive.
In the marine environment Species Distribution Models (SDMs) have been used in hundreds of papers for predicting the present and future geographic range and environmental niche of species. We have analyzed ways in which SDMs are being applied to marine species in order to recommend best practice in future studies. This systematic review was registered as a protocol on the Open Science Framework: https://osf.io/tngs6/. The literature reviewed (236 papers) was published between 1992 and July 2016. The number of papers significantly increased through time (R² = 0.92, p < 0.05). The studies were predominantly carried out in the Temperate Northern Atlantic (45%) followed by studies of global scale (11%) and studies in Temperate Australasia (10%). The majority of studies reviewed focused on theoretical ecology (37%) including investigations of biological invasions by non-native organisms, conservation planning (19%), and climate change predictions (17%). Most of the studies were published in ecological, multidisciplinary, or biodiversity conservation journals. Most of the studies (94%) failed to report the amount of uncertainty derived from data deficiencies and model parameters. Best practice recommendations are proposed here to ensure that novice and advanced SDM users can (a) understand the main elements of SDMs, (b) reproduce standard methods and analysis, and (c) identify potential limitations with their data. We suggest that in the future, studies of marine SDMs should report on key features of the approaches employed, data deficiencies, the selection of the best explanatory model, and the approach taken to validate the SDM results. In addition, based on the literature reviewed, we suggest that future marine SDMs should account for uncertainty levels as part of the modeling process.
Describing essential habitat is an important step toward understanding and conserving harvested species in ecosystem-based fishery management. Using data from fishery-independent ichthyoplankton, groundfish surveys, and commercial fisheries observer data, we utilized species distribution modeling techniques to predict habitat-based spatial distributions of federally managed species in Alaska. The distribution and abundance maps were used to refine existing essential fish habitat descriptions for the region. In particular, we used maximum entropy and generalized additive modeling to delineate distribution and abundance of early (egg, larval, and pelagic juvenile) and later (settled juvenile and adult) life history stages of groundfishes and crabs across multiple seasons in three large marine ecosystems (Gulf of Alaska, eastern Bering Sea, and Aleutian Islands) and the northern Bering Sea. We present a case study, featuring Kamchatka flounder (Atheresthes evermanni), from the eastern and northern Bering Sea to represent the >400 habitat-based distribution maps generated for more than 80 unique species–region–season–life-stage combinations. The results of these studies will be used to redescribe essential habitat of federally managed fishes and crabs in Alaska.
In this work we use a set of recent multi-year simulations to develop a simplified sea surface
height index (SSH). The index characterizes the dynamics of Ionian upper layer circulation and
its links with sea surface height and salinity in the Southern Adriatic and Aegean Seas during the
period 1987-2008. The analysis highlights a covariant behavior between Ionian Sea and Aegean
Sea associated with a mutual zonal exchange of water masses with different salinity
characteristics. Our analysis confirms that the variability observed in the period 1987-2008 in the
upper layer circulation of the Ionian was driven by the salinity variability in the Southern
Adriatic and Aegean Sea. This study supports and reinforces the hypothesis that two observed
BiOS-like reversals reflect the existence of multiple equilibrium states in the Mediterranean
Thermohaline circulation in the Eastern Mediterranean and that a complete characterization of
observed variability needs to take into account a fully coupled Adriatic-Ionian-Aegean System.
Deep-sea coral assemblages are key components of marine ecosystems that generate habitats for fish and invertebrate communities and act as marine biodiversity hot spots. Because of their life history traits, deep-sea corals are highly vulnerable to human impacts such as fishing. They are an indicator of vulnerable marine ecosystems (VMEs), therefore their conservation is essential to preserve marine biodiversity. In the Mediterranean Sea deep-sea coral habitats are associated with commercially important crustaceans, consequently their abundance has dramatically declined due to the effects of trawling. Marine spatial planning is required to ensure that the conservation of these habitats is achieved. Species distribution models were used to investigate the distribution of two critically endangered octocorals (Funiculina quadrangularis and Isidella elongata) in the central Mediterranean as a function of environmental and fisheries variables. Results show that both species exhibit species-specific habitat preferences and spatial patterns in response to environmental variables, but the impact of trawling on their distribution differed. In particular F. quadrangularis can overlap with fishing activities, whereas I. elongata occurs exclusively where fishing is low or absent. This study represents the first attempt to identify key areas for the protection of soft and compact mud VMEs in the central Mediterranean Sea.
Consistency in conservation
Marine protected areas (MPAs) are now well established globally as tools for conservation, for enhancing marine biodiversity, and for promoting sustainable fisheries. That said, which regions are labeled as MPAs varies substantially, from those that full protect marine species and prohibit human extraction to those that permit everything from intensive fishing to mining. This inconsistency can in some cases inhibit both conservation and quantifying the proportion of the marine environment that is truly protected. Grorud-Colvert et al . review the consistency of MPAs and propose a framework by which levels of protection can be evaluated and improved. —SNV
With consideration of sophisticated modern commercial fisheries, the commonly used metric catch per unit effort (CPUE) may not be a reasonable proxy for generating abundance indices (AIs) for all species. Presumably, spatiotemporal scale is a critical factor that affects the accuracy of local/aggregated AIs derived from spatial modelling approaches, thus it is necessary to evaluate how scale affects scientific estimates of abundance. We explored three commonly utilized AI proxies, including aggregated catch (CatchAI), aggregated effort (EffortAI), and CPUEAI from the perspective of accuracy and spatial representational ability using a neural network (NN) model at different spatiotemporal scales. As a case example, we grouped the Chinese fleet's Northwest Pacific neon flying squid (Ommastrephes bartramii) fishery dataset (2009–2018) at four spatial scales (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, 2° × 2°) to construct monthly and annual resolution models. The results showed that for both simulated and real datasets, AIs based on catch data had better accuracy, consistency, and spatial representational ability compared to CPUE and effort-dependent AI models at all spatial scales. Relative to the finest spatial scale, only results from the model with 0.5° × 0.5° resolution preserved enough distributional detail to reflect the known migration route for O. bartramii. Model results exhibited large variation dependent on spatial scale, particularly amongst CPUEAI scenarios. We suggest that scale comparisons among potential proxies should be conducted prior to AIs being used for applications such as population trends in stock assessment.
Delta-models (a.k.a. hurdle models) are widely used to fit biomass samples that include zeros and a skewed response for positive catches, and spatio-temporal extensions of these models are increasingly used to quantify trends in abundance (i.e., estimate abundance indices). Previous research has shown estimated indices are proportional to changes in abundance. However, little research has tested the performance of delta-models for estimating “scale”; that is, whether abundance indices are not just proportional to population changes but also have the correct absolute value. We use data for twenty species in the eastern Bering Sea and Gulf of Alaska as well as a factorial experiment conditioned on data for Gulf of Alaska Pacific cod to support five conclusions related to scale in spatio-temporal delta-models. First, we show that conventional (nonspatial) delta-models are surprisingly sensitive to the a priori choice of probability distribution for positive catches, where gamma and Tweedie models give similar scale estimates but other distributions generally differ. Second, these same distributions also estimate widely different scales when using spatio-temporal delta-models, and the delta-gamma and Tweedie models provide similar scale to design-based indices. Third, model selection using marginal AIC often identifies the lognormal distribution as most parsimonious, despite it resulting in systematically higher abundance than design-based indices for many species. Fourth, scale is sensitive to the spatial resolution (i.e., number of knots) used in fitting the spatio-temporal model when using a naïve “empirical Bayes” estimator, but less sensitive when applying an epsilon bias-correction estimator. Fifth, the factorial simulation experiment suggests that the Tweedie and delta-gamma distributions perform well even when applied to data simulated from an inverse-Gaussian or lognormal distribution, whereas the opposite is not true. We conclude that index scale is sensitive to delta-model specification, and we make five recommendations when using spatio-temporal delta-models for index standardization: (1) apply the epsilon or other bias-correction methods to reduce sensitivity of index scale on spatio-temporal model resolution; either (2) compare the scale of delta-model indices with that of design-based indices when design-based indices are available or (3) use the delta-gamma or Tweedie distribution by default when design-based indices are not available; (4) do not assume that AIC will identify the model specification that results in the most appropriate scale; and (5) consider apparent mismatches in index scale depending upon whether an assessment model specifies or estimates the associated catchability coefficient and whether the design-based index is believed to measure total abundance for a fully-selected age or length-class.
Article 4 of EU Regulation 1380/2013 on the Common Fisheries Policy (CFP) define ‘technical measure’ as “a measure that regulates the composition of catches by species and size and the impacts on components of the ecosystems resulting from fishing activities by establishing conditions for the use and structure of fishing gear and restrictions on access to fishing areas.” Thus, these are a set of rules that govern where, when and how fishing can take place. Most of the fisheries management systems in place worldwide employ technical measures based on control of inputs and outputs. For Europe, the European Commission is reforming the CFP legislative framework and has updated and amended the rules for technical measures (EU Regulation 2019/1241). This is particularly important for management systems in the European parts of the Mediterranean, where input measures play a major role, in contrast to management systems in the North East Atlantic, which uses mainly output measures (catch quota).
We discuss here the main advantages and disadvantages of these instruments with a particular focus on the European part of the Mediterranean Sea, our main aim being to foster a debate on the best measures for fisheries management.
In recent years, the use of ecological niche models (ENMs) and species distribution models (SDMs) to explore the patterns and processes behind observed distribution of species has experienced an explosive growth. Although the use of these methods has been less common and more recent in marine ecosystems than in a terrestrial context, they have shown significant increases in use and applications. Herein, we provide a systematic review of 328 articles on marine ENMs and SDMs published between 1990 and 2016, aiming to identify their main applications and the diversity of methodological frameworks in which they are developed, including spatial scale, geographic realm, taxonomic groups assessed, algorithms implemented, and data sources. Of the 328 studies, 48 % were at local scales, with a hotspot of research effort in the North Atlantic Ocean. Most studies were based on correlative approaches and were used to answer ecological or biogeographic questions about mechanisms underlying geographic ranges (64 %). A few attempted to evaluate impacts of climate change (19 %) or to develop strategies for conservation (11 %). Several correlative techniques have been used, but most common was the machine-learning approach Maxent (46 %) and statistical approaches such as generalized additive models GAMs (22 %) and generalized linear models, GLMs (14 %). The groups most studied were fish (23 %), molluscs (16 %), and marine mammals (14 %), the first two with commercial importance and the last important for conservation. We noted a lack of clarity regarding the definitions of ENMs versus SDMs, and a rather consistent failure to differentiate between them. This review exposed a need to know, reduce, and report error and uncertainty associated with species’ occurrence records and environmental data. In addition, particular to marine realms, a third dimension should be incorporated into the modelling process, referring to the vertical position of the species, which will improve the precision and utility of these models. So too is of paramount importance the consideration of temporal and spatial resolution of environmental layers to adequately represent the dynamic nature of marine ecosystems, especially in the case of highly mobile species.
The reformed Common Fisheries Policy [Regulation (EU) 1380/2013] introduces the obligation to land unwanted catches gradually from
2015 to 2019 with the aim to reduce discards. The ecological and economic consequences of this controversial regulation are evaluated here
using an ecosystem model for the North-Eastern Adriatic Sea to quantify the long-term stocks’ biomass, landings, and fisheries revenues under
future scenarios with and without landing obligation. Results indicate that landings will increase by þ13%, causing an increase in fishermen
workload, reduction of biomasses at sea (��0.20%) for species of both commercial and non-commercial interest, thus a small decrease in
fisheries revenue (��0.50%). Selling landed unwanted catches for fishmeal production will not compensate the economic losses. Additional
adaptation scenarios were tested: (i) introduction of quotas for small pelagics, (ii) reduction of effort for bottom trawlers, (iii) improvement
of gear selectivity, and (iv) a combination of (i) and (iii). Improving selectivity and introducing quotas resulted the best alternative but none
of the adaptation scenarios compensated the adverse effects of the landing obligation, suggesting that this management measure has ecological
and economic negative effects in systems where fisheries are not regulated by quota such as the Mediterranean Sea.
Because of its centrallocation in the Old World, the Adriatic Sea has long been explored and studied. Modern methods of investigation, however, have accelerated the pace of study during the last decade. These are the ADCP currentmeter, satellite imagery, drifter technology, and, last but not least, the computer with its arsenal of tools for data analysis and model simulations. As a result of this renaissance, the Adriatic Sea and its sub-basins are currently the object of intensified scrutiny by a number of scientific teams, in Europe and be yond. Questions concerning the mesoscale variability that dominates regional motions, the seasonal circulation of the sea, and its long-term climatic role in the broader Mediterranean, have become topics of lively discussions. The time was ripe then when an international workshop dedicated to the physical oceanography of the Adriatic Sea was convened in Trieste on 21-25 September 1998. Its objectives were to assess the current knowledge of the oceanography of the Adriatic Sea, to review the newly acquired observations, to create syn ergy between model simulations and observations, and to identify directions for future Adriatic oceanography. This book, however,is not the mere proceedings of the workshop. It was written as a monograph synthetizing the current knowledge of the physical oceanography of the Adriatic Sea, with the hope that it will serve as a reference to anyone interested in the Adriatic. The book also identifies topics in need of additional inquiry and proposes research directions for the next decade.