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Conservation of marine biodiversity requires understanding the joint influence of ongoing environmental change and fishing pressure. Addressing this challenge requires robust biodiversity monitoring and analyses that jointly account for potential drivers of change. Here, we ask how demersal fish biodiversity in Canadian Pacific waters has changed since 2003 and assess the degree to which these changes can be explained by environmental change and commercial fishing. Using a spatiotemporal multispecies model based on fisheries independent data, we find that species density (number of species per area) and community biomass have increased during this period. Environmental changes during this period were associated with temporal fluctuations in the biomass of species and the community as a whole. However, environmental changes were less associated with changes in species’ occurrence. Thus, the estimated increases in species density are not likely to be due to environmental change. Instead, our results are consistent with an ongoing recovery of the demersal fish community from a reduction in commercial fishing intensity from historical levels. These findings provide key insight into the drivers of biodiversity change that can inform ecosystem-based management.

The layers provided represent three community metrics: 1) species density (i.e., species richness), 2) Hill-Shannon diversity, and 3) community biomass. All layers are provided at a 3 km resolution across the study domain for the period of 2003 to 2019. For each metric, we provide layers for three summary statistics: 1) the mean value in each grid cell over the temporal range, 2) the probability that the grid cell is a hotspot for that metric, and 3) the temporal coefficient of variation (i.e., standard deviation/mean) across all years.

Methods:
The analysis that produced these layers is presented in Thompson et al. (2022). The analysis uses data from the Groundfish Synoptic Bottom Trawl Research surveys in Queen Charlotte Sound (QCS), Hecate Strait (HS), West Coast Vancouver Island (WCVI), and West Coast Haida Gwaii (WCHG) from 2003 to 2019. Cartilaginous and bony fish species caught in DFO groundfish surveys that were present in at least 15% of all trawls over the depth range in which they were caught were included. This depth range was defined as that which included 95% of all trawls in which that species was present. The final dataset used in our analysis consisted of 57 species (Table S1 in Thompson et al. 2022).

The spatiotemporal dynamics of the demersal fish community were modeled using the Hierarchical Modeling of Species Communities (HMSC) framework and package (Tikhonov et al. 2021) in R. This framework uses Bayesian inference to fit a multivariate hierarchical generalized mixed model. We modeled community dynamics using a hurdle model, which consists of two sub models: a presence-absence model and a biomass model that is conditional on presence. Our list of environmental covariates included bottom depth, bathymetric position index (BPI), mean summer tidal speed, substrate muddiness, substrate rockiness, whether the trawl was inside or outside of the ecosystem-based trawling footprint, and survey region (QCS &amp; HS vs. WCVI &amp; WCHG)), mean summer near-bottom temperature deviation, mean summer near-bottom dissolved oxygen deviation, mean summer cross-shore and along-shore current velocities near the seafloor, mean summer depth-integrated primary production, and local-scale commercial fishing effort.

Layers are provided for three community metrics. All metrics should be interpreted as the value that would be expected in the catch from an average tow in the Groundfish Synoptic Bottom Trawl Research Surveys taken in a given 3 km grid cell. Species density (sometimes called species richness) should be interpreted as the number of the 57 species that would be caught in a trawl. Hill-Shannon diversity is a measure of diversity that gives greater weight to communities where biomass is spread equally across species. Community biomass is the total biomass across all 57 species that would be expected to be caught per square km in an average tow. 

Data Sources:
Research data was provided by Pacific Science's Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2019 that occurred in four regions: Queen Charlotte Sound, Hecate Strait, West Coast Haida Gwaii, and West Coast Vancouver Island. Our analysis excludes species that are rarely caught in the research trawls and so our estimates would not include the occurrence or biomass of these rare species.

Commercial fishing data was accessed through a DFO R script detailed here: https://github.com/pbsassess/gfdata. Local scale commercial fishing effort was calculated from this data. 

The substrate layers were obtained from a substrate model (Gregr et al. 2021). 

The oceanographic layers (bottom temperature, dissolved oxygen, tidal and circulation speeds, primary production) were obtained from a hindcast simulation of the British Columbia continental margin (BCCM) model (Peña et al. 2019).

Uncertainties:
Species that are not well sampled by the trawl surveys may not be accurately estimated by our model. The model did not include spatiotemporal random effects, which likely underestimates spatiotemporal variability in the region. It is also important to underline covariate uncertainty and model uncertainty. The hotspot estimates provide one measure of model uncertainty/certainty.</gco:CharacterString>
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                  <gmd:LocalisedCharacterString locale="#fra">Description
Pour préserver la biodiversité marine, il faut comprendre l’influence conjointe des changements environnementaux constants et des pressions exercées par la pêche. Pour relever ce défi, il faut mener des analyses et une surveillance rigoureuses de la biodiversité qui tiennent compte des facteurs de changement potentiels. Ici, nous nous demandons comment la biodiversité des poissons démersaux dans les eaux canadiennes du Pacifique a changé depuis 2003 et évaluons dans quelle mesure ces changements peuvent être expliqués par les changements environnementaux et la pêche commerciale. À l’aide d’un modèle spatiotemporel multispécifique fondé sur les données indépendantes des pêches, nous constatons que la densité des espèces (nombre d’espèces par zone) et la biomasse communautaire ont augmenté durant cette période. Les changements environnementaux survenus durant cette période ont été associés aux fluctuations temporelles de la biomasse des espèces et de la communauté en général. Toutefois, les changements environnementaux étaient moins associés aux changements dans la présence des espèces. Par conséquent, les augmentations estimées de la densité des espèces ne seront probablement pas attribuables aux changements environnementaux. Nos résultats correspondent plutôt au rétablissement continu de la communauté des poissons démersaux attribuable à une réduction de l’intensité de la pêche commerciale par rapport aux niveaux historiques. Ces résultats donnent des renseignements clés sur les facteurs du changement de la biodiversité qui peuvent éclairer la gestion axée sur les écosystèmes.

Les couches montrées représentent trois paramètres communautaires : 1) la densité des espèces (c.-à-d. richesse spécifique), 2) la diversité de Hill-Shannon et 3) la biomasse communautaire. Toutes les couches sont fournies à une résolution de 3 km dans le domaine d’étude pour la période s’échelonnant de 2003 à 2019. Pour chaque paramètre, nous présentons des couches pour trois statistiques sommaires, soit 1) la valeur moyenne de chaque cellule de grille sur la plage temporelle, 2) la probabilité que la cellule de grille soit un point chaud pour ce paramètre et 3) le coefficient de variation temporel (c.-à-d. écart-type/moyenne) pour toutes les années.

Méthodes :
L’analyse qui a produit ces couches est présentée dans Thompson et al., 2022. L’analyse s’appuie sur les données des relevés synoptiques au chalut de fond des poissons démersaux dans le bassin Reine-Charlotte, le détroit d’Hécate, la côte ouest de l’île de Vancouver et la côte ouest d’Haida Gwaii. Ces relevés ont été effectués de 2003 à 2019. Les espèces de poissons cartilagineux et osseux capturées indiquées dans les relevés des poissons de fond du MPO qui étaient présentes dans au moins 15 % de tous les chaluts de la plage de profondeurs dans laquelle elles étaient capturées ont été incluses. Cette plage de profondeurs était définie comme comprenant 95 % de tous les chaluts dans lesquels ces espèces étaient présentes. L’ensemble de données final utilisé dans notre analyse comprenait 57 espèces (tableau S1 du rapport).

La dynamique spatiotemporelle de la communauté des poissons démersaux a été modélisée à l’aide du cadre et du progiciel de modélisation hiérarchique des communautés d’espèces (HMSC) (Tikhonov et al., 2021) dans R. Ce cadre utilise l’inférence bayésienne pour rajuster un modèle mixte hiérarchique généralisé multivariable. Nous avons modélisé la dynamique communautaire au moyen d’un modèle à obstacles qui comprend deux sous-modèles : un modèle présence-absence et un modèle de biomasse conditionnel à la présence. Notre liste de covariables environnementales comprenait la profondeur du fond, l’indice de position bathymétrique (BPI), la vitesse moyenne des marées estivales, la turbidité du substrat, la roccosité du substrat, la question à savoir si le chalut se trouvait à l’intérieur ou à l’extérieur de l’empreinte de chalutage fondée sur l’écosystème, et la région du relevé, l’écart de température moyen près du fond en été, l’écart de l’oxygène dissous moyen près du fond en été, les vitesses moyennes du courant entre les rives et le long de la rive près du fond océanique en été, la production primaire intégrée moyenne à la profondeur en été et l’effort de pêche commerciale à l’échelle locale.

Des couches sont présentées pour trois paramètres relatifs aux communautés. Tous les paramètres doivent être interprétés comme représentant la valeur à laquelle on s’attendrait dans la prise d’un trait moyen dans les relevés synoptiques au chalut de fond des poissons démersaux effectués dans une cellule de grille de 3 km donnée. La densité des espèces (parfois appelée richesse spécifique) doit être interprétée étant comme le nombre des 57 espèces qui seraient prises dans un chalut. La diversité de Hill-Shannon est une mesure de la diversité qui accorde une plus grande importance aux communautés où la biomasse est répartie également entre les espèces. La biomasse communautaire est la biomasse totale des 57 espèces qui devrait être capturée par kilomètre carré dans un trait moyen. 

Sources de données :
Les données de recherche ont été fournies par l’Unité des données sur le poisson de fond de la direction des sciences du Pacifique pour les relevés de recherche de la base de données GFBio entre 2003 et 2019 qui ont été effectués dans quatre régions: le bassin Reine-Charlotte, le détroit d'Hécate, la côte ouest de l’île de Vancouver et la côte ouest d’Haida Gwaii. Notre analyse exclut les espèces qui sont rarement prises dans les chaluts de recherche; nos estimations n’incluraient donc pas l’occurrence ou la biomasse de ces espèces rares.

Les données sur la pêche commerciale ont été consultées au moyen d’un script R du MPO détaillé à https://github.com/pbs-assess/gfdata. L’effort local de pêche commerciale a été calculé à partir de ces données.

Les couches de substrat ont été obtenues à partir d’un modèle de substrat (Gregr et al., 2021).

Les couches océanographiques (température au fond, oxygène dissous, vitesses de marée et de circulation, production primaire) ont été obtenues à partir d’une simulation rétrospective du modèle de la marge continentale de la Colombie-Britannique (Peña et al., 2019).

Incertitudes :
Il est possible que les espèces qui ne sont pas bien échantillonnées par les relevés au chalut ne soient pas estimées avec exactitude par notre modèle. Le modèle ne comprenait pas d’effets aléatoires spatiotemporels, ce qui sous-estime probablement la variabilité spatiotemporelle dans la région. Il importe également de souligner l’incertitude des covariables et du modèle. Les estimations des points chauds donnent une mesure de l’incertitude/la certitude du modèle.</gmd:LocalisedCharacterString>
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