Species distribution modelling in fisheries science

Latest fisheries directives propose adopting an ecosystem approach to manage fisheries \citep{FAO-EAFM}. Such an approach aims to protect important ecosystems based on the principle that healthy ecosystems produce more and thus enhance sustainability. Unfortunately, quantifying the importance of an ecosystem is a difficult task to do due the immense number of interactions involved in marine systems. This PhD dissertation relies on the fact that good fisheries distribution maps could play a very important role as they allow a visual and intuitive assessment of different marine areas. Unfortunately, the limited amount of data available and the inherent difficulties of modelling fishery data has resulted in relatively low quality maps in the near past (see \citep{atlas} and \url{http://www.ices.dk/marine-data/maps/Pages/ICES-FishMap.aspx)}. As a result, the spatial fisheries management framework requires competent statistical approaches to quantify the importance of different marine areas with an appropriate measure of uncertainty associated to the estimates. The aim of this PhD is to provide competent spatial and spatio-temporal modelling approaches that allow us characterise different fishery processes that are relevant for their sustainable management

Bayesian feedback in the framework of ecological sciences

In ecology we may find scenarios where the same phenomenon (species occurrence, species abundance, etc.) is observed using two different types of samplers. For instance, species data can be collected from scientific surveys with a completely random sample pattern, but also from opportunistic sampling (e.g., whale or bird watching fishery commercial vessels), in which observers tend to look for a specific species in areas where they expect to find it. Species Distribution Models (SDMs) are a widely used tool for analyzing this kind of ecological data. Specifically, we have two models available for the above data: an independent model (IM) for the data coming from a complete random sampler and a dependent model (DM) for data from opportunistic sampling. In this work, we propose a sequential Bayesian procedure to connect these two models through the update of prior distributions. Implementation of the Bayesian paradigm is done through the integrated nested Laplace approximation (INLA) methodology, a good option to make inference and prediction in spatial models with high performance and low computational costs. This sequential approach has been evaluated by simulating several scenarios and comparing the results of sharing information from one model to another using different criteria. Our main results imply that, in general, it is better to share information from the independent (completely random) to the dependent model than the alternative way. However, it depends on different factors such as the spatial range or the spatial arrangement of sampling locations.Comment: 39 pages, 14 figures and 2 table

Modelling spatially sampled proportion processes​​

Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset

Evaluating strategies for managing anthropogenic mortality on marine mammals : an R implementation with the package RLA

Funding: ADERA provided support for salaries (MA).Bycatch, the undesirable and non-intentional catch of non-target species in marine fisheries, is one of the main causes of mortality of marine mammals worldwide. When quantitative conservation objectives and management goals are clearly defined, computer-based procedures can be used to explore likely population dynamics under different management scenarios and estimate the levels of anthropogenic removals, including bycatch, that marine mammal populations may withstand. Two control rules for setting removal limits are the Potential Biological Removal (PBR) established under the US Marine Mammal Protection Act and the Removals Limit Algorithm (RLA) inspired from the Catch Limit Algorithm (CLA) developed under the Revised Management Procedure of the International Whaling Commission. The PBR and RLA control rules were tested in a Management Strategy Evaluation (MSE) framework. A key feature of PBR and RLA is to ensure conservation objectives are met in the face of the multiple uncertainties or biases that plague real-world data on marine mammals. We built a package named RLA in the R software to carry out MSE of control rules to set removal limits in marine mammal conservation. The package functionalities are illustrated by two case studies carried out under the auspices of the Oslo and Paris convention (OSPAR) (the Convention for the Protection of the Marine Environment of the North-East Atlantic) Marine Mammal Expert Group (OMMEG) in the context of the EU Marine Strategy Framework Directive. The first case study sought to tune the PBR control rule to the conservation objective of restoring, with a probability of 0.8, a cetacean population to 80% of carrying capacity after 100 years. The second case study sought to further develop a RLA to set removals limit on harbor porpoises in the North Sea with the same conservation objective as in the first case study. Estimation of the removals limit under the RLA control rule was carried out within the Bayesian paradigm. Outputs from the functions implemented in the package RLA allows the assessment of user-defined performance metrics, such as time to reach a given fraction of carrying capacity under a given level of removals compared to the time needed given no removals.Publisher PDFPeer reviewe

Identifying the best fishing-suitable areas under the new European discard ban

The spatial management of fisheries has been repeatedly proposed as a discard mitigation measure. A number of studies have assessed the fishing suitability of an area based on units of by-catch or discard per unit effort. However, correct identification of fishing-suitable areas should assess biomass loss with respect to the benefits. This study therefore, proposes the analysis of by-catch ratios, which do represent benefit vs. loss and are standardized to a wide range of effort characteristics. Furthermore, our study proposes the use of two ratios: the proportion of total unwanted biomass out of the total catch as an indicator of the overall ecological impact, and the proportion of unwanted but regulated species biomass as a proxy for the economic impact on fishers resulting from the new European discard ban that prohibits the discard of regulated species. These discard ratios are modelled by means of a Bayesian hierarchical model, specifically, a spatio-temporal beta regression model, which has several advantages over the traditional arcsine transformation. Results confirm the standardizing capacity of by-catch ratios across vessels and identify at least two economically fishing-suitable areas where discards ratios are minimized by reducing unwanted catch

Accounting for preferential sampling in species distribution models

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain).Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non‐randomized and/or non‐systematic sampling.Publisher PDFPeer reviewe

Modeling discards in Trawling Mediterranean Northern Alboran Sea Fishery

Target and Bycatch species metrics estimated from fishery-dependent data were explored to assess their use in governance of habitat conservation in respect to fisheries. Fishing data collected by onboard observers in otter-trawl boats between 2011 and 2012 at monthly sampling frequency in the Alboran Sea (Western Mediterranean) were used to build maps of sensitivity to fishing stress. Maps were drawn by means kriging interpolation techniques of biomass and abundance (Catch Per Unit of Effort, CPUE) in kilogram and number per fishing hour of blue whiting (Micromesistius poutassou), European hake (Merluccius merluccius), and red mullets (Mullus barbatus and Mullus surmuletus) target species, seabreams (Pagellus acarne, Pagellus bogaraveo, and Pagellus erythrinus), and mackerels (Trachurus mediterraneus, Trachurus trachurus, and Trachurus picturatus) bycatch species and Bogue (Boops boops) bycatch discarded species. Modelling discards by means Generalised Additive Models (GAMs) use environmental (sea surface temperature and chlorophyll-a from satellite data and NAO climatic index); spatial (latitude, longitude, depth and port) and temporal (season, haul duration, moon phase), as well as technical (boat length and power) explanatory variables. The main causes of discards, for both target and bycatch species, are associated to the seasonality of the recruitment and the changes on the spatial distribution of habitat preferences along their ontogeny. Environmental variables did not reveal significant effects, showing that operational oceanography standard products must be not enough to assess discards, and therefore products providing information on specific ecological processes to discards must be designed with this purpose. In Bycatch species, such as sea breams, mackerels and bogue, discards were also highly dependent of the port and boat (fleet/boat strategies, power, etc, and market preferences). The higher discards corresponded to these bycatch pelagic or bentho-pelagic species. Keywords: Discards, Otter-trawl fisheries, fishery conservation, operational oceanography, spatial modelin

Long-term coexistence between the macroalga Caulerpa prolifera and the seagrass Cymodocea nodosa in a Mediterranean lagoon

En prensa1,51

Estandarización espacio-temporal de índices CPUE para evaluar la incertidumbre en especies demersales de interés pesquero

Poster.-- Iberian Symposium on Modeling and Assessment of Fishery Resources, 19-22 October, Vigo, SpainLos índices de captura por unidad de esfuerzo (CPUE) son una de las principales fuentes de información empleadas en los modelos de evaluación de los stocks pesqueros (Zhou et al., 2019). Existen múltiples técnicas para la estandarización de dichos índices, desde modelos lineales generalizados (GLMs) o modelos aditivos generalizados (GAMs) hasta la inclusión de modelos más complejos como los geoestadísticos (Zhou et al., 2019). El objetivo del presente trabajo es evaluar la precisión e incertidumbre asociada a los índices CPUE derivados de fuentes de datos con distinta información espacial. Para ello, se realiza una estandarización de los índices CPUE utilizando modelos geoestadísticos en diferentes escenarios espaciales, comparándolos con modelos GLMs y GAMs. Respecto a la simulación, las capturas (por año) se simulan dentro de un área en base a los siguientes escenarios espaciales: (1) datos de campañas georreferenciadas, (2) datos georreferenciados provenientes de pesquerías, y (3) datos obtenidos de la pesca con referencia espacial lattice/areal data. En el caso del escenario (1) el muestreo es aleatorio, mientras que, para los escenarios (2) y (3) el muestreo es preferencial. Una vez simulados los datos, se ajustan los modelos empleando la técnica Bayesiana Integrated Nested Laplace Aproximation (INLA) mediante el software estadístico R. Finalmente, los índices CPUE obtenidos para cada escenario se introducirán en un modelo de evaluación, con el fin de valorar la precisión en la evaluaciónN