Spatial distribution of Cephalopods of the European Shelf and their associated oceanographic parameters based on occurrence in standardized demersal fishing trawls

Changing oceans impact the whole marine ecosystem in different ways. For example, rising ocean temperatures can affect the presence / absence of species, especially when local environmental conditions exceed individual species’ physiological tolerances. Accordingly, climate change has caused shifts in distribution and expansions for various cephalopods worldwide. Cephalopods play an important role in the ecosystem, especially in food webs. Consequently, spatial distribution shifts might help explain observed ecosystem changes. Therefore, maps for cephalopod distributions need to be reviewed and updated. Meanwhile, information on the associated environmental conditions will permit future occurrence of cephalopods to be modelled, which is interesting from a fishery and ecological perspective. Some information about physiological tolerances of cephalopods are known from laboratory studies and aquaculture experience, as well as from field observations. Laboratory data are often based on narrow ranges, depending on the experimental design, and can therefore provide only a limited understanding of physiological tolerances. On the other hand, field observations are also limited due to the spatial and temporal limitations of surveys, but these might provide a more realistic picture of natural tolerances. Here, we use the ICES Datras dataset to, first, describe the current distribution of cephalopods associated with the European shelf and, second, advance the knowledge regarding environmental ranges of the various species included in the analysis by combining occurrence data with in-situ oceanographic data. An additional literature review will provide information about the different environmental requirements of various life stages. The results allow us to increase the knowledge of physiological preferences of various cephalopod species within the North-East Atlantic Ocean. Finally, we will discuss and present potential future trends in cephalopod occurrence within the NE Atlantic. In order to further strengthen our knowledge of physiological tolerances of various cephalopod species more data on life history and life stages is needed to develop a more advanced mechanistic model.info:eu-repo/semantics/publishedVersio

Working group on cephalopod fisheries and life history (Wgceph; outputs from 2022 meeting)

Rapports Scientifiques du CIEM. Volume 5, nº 1WGCEPH worked on six Terms of Reference. These involved reporting on the status of stocks; reviewing advances in stock identification, assessment for fisheries management and for the Ma- rine Strategy Framework Directive (MSFD), including some exploratory stock assessments; re- viewing impacts of human activities on cephalopods; developing identification guides and rec- ommendations for fishery data collection; describing the value chain and evaluating market driv- ers; and reviewing advances in research on environmental tolerance of cephalopods. ToR A is supported by an annual data call for fishery and survey data. During 2019–2021, com- pared to 1990–2020, cuttlefish remained the most important cephalopod group in terms of weight landed along the European North Atlantic coast, while loliginid squid overtook octopus as the second most important group. Short-finned squid remained the least important group in land- ings although their relative importance was almost double in 2019–2022 compared to 1992–2020. Total cephalopod landings have been fairly stable since 1992. Cuttlefish landings are towards the low end of the recent range, part of a general downward trend since 2004. Loliginid squid landings in 2019 were close to the maximum seen during the last 20 years but totals for 2020 and 2021 were lower. Annual ommastrephid squid landings are more variable than those of the other two groups and close to the maximum seen during 1992– 2021. Octopod landings have generally declined since 2002 but the amount landed in 2021 was higher than in the previous four years. Under ToR B we illustrate that the combination of genetic analysis and statolith shape analysis is a promising method to provide some stock structure information for L. forbsii. With the sum- mary of cephalopod assessments, we could illustrate that many cephalopod species could al- ready be included into the MSFD. We further provide material from two reviews in preparation, covering stock assessment methods and challenges faced for cephalopod fisheries management. Finally, we summarise trends in abundance indices, noting evidence of recent declines in cuttle- fish and some octopuses of the genus Eledone. Under ToR C, we describe progress on the reviews of (i) anthropogenic impacts on cephalopods and (ii) life history and ecology. In relation to life history, new information on Eledone cirrhosa from Portugal is included. Under ToR D we provide an update on identification guides, discuss best practice in fishery data collection in relation to maturity determination and sampling intensity for fishery monitoring. Among others, we recommend i) to include the sampling of cephalopods in any fishery that (a) targets cephalopods, (b) targets both cephalopods and demersal fishes or (c) takes cephalopods as an important bycatch, ii) Size-distribution sampling, iii) the use of standardized sampling pro- tocols, iv) an increased sampling effort in cephalopod. Work under ToR E on value chains and market drivers, in conjunction with the Cephs & Chefs INTERREG project, has resulted in two papers being submitted. Abstracts of these are in the report. Finally, progress under ToR F on environmental tolerance limits of cephalopods and climate en- velope models is discussed, noting the need to continue this work during the next cycle.info:eu-repo/semantics/publishedVersio

Nature Index. General framework, statistical method and data collection for Norway

Certain, G. and Skarpaas, O. 2010. Nature Index: General framework, statistical method and data collection for Norway – NINA Report 542. 47 pp. The Nature Index for Norway has been developed to be an aggregated measure of biodiversity in Norway, reflecting the state of terrestrial and marine ecosystems and providing comprehensive information to environmental managers and to the public in a simplified and understandable way. It consists of a set of 310 biodiversity indicators that encompass important aspects of natural biodiversity. The present report is a general description of the Nature Index framework. It summarises the basic concepts and definitions used, and displays the associated mathematical developments. The report builds on and extends previous pilot studies on concepts and practical implementation (NINA Reports 347, 425 and 426). The final results of the Nature Index will be presented elsewhere (Nybø (ed.) 2010a,b); here we present the data collection process and an analysis of the indicator set collected so far in order to provide information on the ecological significance and on the inferences that can be expected. Data on indicators were collected from experts who provided estimates of the indicator values at several points in time using expert judgement, monitoring data or models. Experts also provided an estimate of uncertainty with each data point in the form of quartiles, and they were asked to indicate where insufficient information was available to provide an estimate of the indicator value. To combine the indicators to produce an index, the indicators are scaled by a reference value, i.e. their value in a reference state. This serves two purposes: First, the reference state, for each indicator, is supposed to reflect an ecologically sustainable state for the indicator, and the scaled value measures the departure from this state. Second, because the scaled values are all dimensionless numbers between 0 and 1, they can be averaged across, for instance, municipality, major habitat, or taxonomic group. Thus the use of a reference value facilitates a flexible combination of indicators expressed in different measurement units, such as abundance or species richness. Plain averaging of scaled indicators implies a “complete equivalence” assumption, i.e. that no municipality, no major habitat, and no indicator is more important than another. This assumption is not always true. Moreover, despite efforts to balance the indicator set, the indicators are not homogeneously distributed among taxonomic groups, pressures, major habitats etc. In the specific case of Norway, we decided, with the support of the Ecological Reference group for the Nature Index, to apply weighting mainly to deal with heterogeneities within the indicator set. Weights were applied across two axes of the Nature Index: across the spatial axis, so that the index remains area-representative, and across the indicator axis, to solve issues concerning the ecological significance of the index. Equivalence was maintained between major habitats because this ensures that the nature index will be maximised with beta (regional) diversity as well as alpha (local) diversity: complete loss of a major habitat implies a decrease in beta diversity, and this will always result in a decrease of the index under equivalence between major habitats. biodiversity, indicators, Norway, biologisk mangfold, indikatorer, NorgeCertain, G. and Skarpaas, O. 2010. Nature Index: General framework, statistical method and data collection for Norway – NINA Rapport 542. 47 s. Naturindeksen er et sammensatt mål for biologisk mangfold i Norge som gjenspeiler tilstanden i terrestre og marine natursystemer og formidler denne omfattende informasjonen til miljøforvaltningen og allmenheten på en forenklet og forståelig måte. Den består av 310 indikatorer som dekker viktige aspekter ved biologisk mangfold. Denne rapporten gir en generell beskrivelse av rammeverket for Naturindeksen. Den gjennomgår grunnleggende begreper og definisjoner, og tilhørende matematiske formuleringer. Rapporten bygger videre på tidligere forslag til rammeverk og pilotstudier (NINA Rapport 347, 425 og 426). Hovedresultatene for naturindeksen presenteres i to kommende DN-utredninger (Nybø (ed.) 2010a,b); her presenterer vi metoder for datainnsamling og en analyse av indikatorsettet for å informere om den økologiske betydningen av naturindeksen og slutningene man kan forvente å gjøre på grunnlag av denne. Data om indikatorene ble samlet inn fra eksperter som ga estimater av indikatorverdier på flere tidspunkter på grunnlag av ekspertvurderinger, overvåkingsdata eller modeller. Ekspertene ga også et estimat av usikkerheten til hver verdi i form av kvartiler, og de ble bedt om å angi i hvilke tilfeller grunnlaget var for svakt til å gi estimater. For å kunne kombinere indikatorene til en indeks, ble hver enkelt indikator skalert med en referanseverdi, dvs. verdien av indikatoren i en referansetilstand. Dette tjener to formål: For det første reflekterer referansetilstanden en økologisk bærekraftig tilstand for indikatoren, og den skalerte verdien måler avvik fra denne tilstanden. For det andre kan de skalerte verdiene, som alle er enhetsløse verdier mellom null og en, benyttes til å beregne gjennomsnitt på tvers av for eksempel kommuner, hovedgrupper av natursystemer og taksonomiske grupper. Bruken av en referanse muliggjør dermed fleksible kombinasjoner av indikatorer med ulike måleenheter som bestandsstørrelse eller artsrikdom. Rene gjennomsnitt av skalerte indikatorverdier kan beregnes under en antagelse om ”fullstendig ekvivalens”, dvs. at ingen kommune, ingen natursystemer og ingen indikatorer er viktigere enn andre. Dette vil ikke alltid være tilfelle. Indikatorene er heller ikke jevnt fordelt mellom taksonomiske grupper, påvirkninger, etc., på tross av forsøk på å balansere indikatorsettet. I implementeringen for Norge har vi derfor valgt, med støtte fra Faggruppen for Naturindeksen, å tilordne vekter langs to akser: den geografiske aksen, slik at indeksen blir arealrepresentativ, og indikatoraksen, for å løse problemer med økologisk representativitet. Mellom hovedgrupper av natursystemer antar vi fullstendig ekvivalens, fordi dette sikrer at Naturindeksen maksimeres med betadiversitet (regional diversitet), i tillegg til alfadiversitet (lokal diversitet): tap av et natursystem medfører reduksjon i betadiversitet, og dette medfører alltid en reduksjon i indeksen under antagelsen om fullstendig ekvivalens. I Naturindeksen brukes datausikkerhet og manglende data aktivt på flere måter: Informasjon om kilder til indikatorestimater (ekspertvurdering, data, modeller), usikkerheten i estimatene og tilfeller med fullstendig mangel på kunnskap, kan brukes til å målrette framtidig forskning og utredning. Usikkerhet i indikatorestimater aggregeres til indeksnivå ved hjelp av Monte Carlometoder: simulering av fordelingene tilpasset gjennomsnitt og kvartiler til hver enkelt indikator. Naturindeksen kan fange opp og sammenstille informasjon fra ulike økologiske fagfelt, både terrestre og marine, og avlevere to hovedtyper av informasjon: tilstanden til natursystemer, gitt dagens kunnskap, og områder med manglende kunnskap kan begge tydeliggjøres og gi innspill til forvaltning og forskning. Informasjonen i naturindeksen kan aggregeres eller splittes opp langs flere akser, slik som geografiske enheter, økologiske enheter eller forvaltningstema. Dette gir Naturindeksen et stort potensial som forvaltningsverktøy og katalysator for økologisk forskning og utredning i Norge, og for internasjonal anvendelse

MEECertain2017_SI3_SimExp.tar.gz

This is Supporting Information SI3 for the Paper entitled <br>"How do MAR(1) models cope with hidden nonlinearities in<br>ecological dynamics?" to be published in Methods in Ecology and Evolution, Authored by G. Certain, F. Barraquand and A. Gårdmark. <br><br>See SI3_readme.pdf for more information. <br

How do MAR(1) models cope with hidden nonlinearities in ecological dynamics?

1.Multivariate autoregressive (MAR) models are an increasingly popular technique to infer interaction strengths between species in a community and to predict the community response to environmental change. The most commonly employed MAR(1) models, with one time lag, can be viewed either as multispecies competition models with Gompertz density‐dependence or, more generally, as a linear approximation of more complex, nonlinear dynamics around stable equilibria. This latter interpretation allows for broader applicability, but may come at a cost in terms of interpretation of estimates and reliability of both short‐ and long‐term predictions. 2.We investigate what these costs might be by fitting MAR(1) models to simulated two‐species competition, consumer‐resource and host‐parasitoid systems, as well as a larger food web influenced by the environment. We review how MAR(1) coefficients can be interpreted and evaluate how reliable are estimates of interaction strength, rank, or sign; accuracy of short‐term forecasts; as well as the ability of MAR(1) models to predict the long‐term responses of communities submitted to environmental change such as PRESS perturbations. 3.The net effects of species j on species i are usually (90 to 95%) well recovered in terms of sign or rank, with the notable exception of overcompensatory dynamics. In actual values, net effects of species j on species i are not well recovered when the underlying dynamics are nonlinear. MAR(1) models are better at making short‐term, qualitative forecasts (next point going up or down) than at predicting long‐term responses to environmental perturbations, which can be severely over‐ as well as under‐estimated. 4.We conclude that when applying MAR(1) models to ecological data, inferences on net effects among species should be limited to signs, or the Gompertz assumption should be tested and discussed. This particular assumption on density‐dependence (log‐linearity) is also required for unbiased long‐term predictions. Overall, we think that MAR(1) models are highly useful tools to resolve and characterize community dynamics, but we recommend to use them in conjunction with alternative, nonlinear models resembling the ecological context in order to improve their interpretation in specific applications

Projet INPOLAG (Indicateur Poisson en Lagunes). Résultats de la première campagne d'Automne 2019

Afin de répondre aux attentes de la DCE concernant l'évaluation de l'état écologique des masses d'eau de transition dont les lagunes font parties, l'Ifremer pilote le projet INPOLAG (2019-2021). Ce projet a pour objectif de développer un des indicateurs manquants aux exigences de la DCE : l'indicateur "Poisson en lagunes" adapté au contexte français. Pour cela, trois campagnes de terrain sont prévues pour la définition de cet indicateur. Ce rapport fait l'objet de l'analyse de la première campagne de terrain qui s'est déroulée en Automne 2019

How do MAR(1) models cope with hidden nonlinearities in ecological dynamics?

1.Multivariate autoregressive (MAR) models are an increasingly popular technique to infer interaction strengths between species in a community and to predict the community response to environmental change. The most commonly employed MAR(1) models, with one time lag, can be viewed either as multispecies competition models with Gompertz density‐dependence or, more generally, as a linear approximation of more complex, nonlinear dynamics around stable equilibria. This latter interpretation allows for broader applicability, but may come at a cost in terms of interpretation of estimates and reliability of both short‐ and long‐term predictions. 2.We investigate what these costs might be by fitting MAR(1) models to simulated two‐species competition, consumer‐resource and host‐parasitoid systems, as well as a larger food web influenced by the environment. We review how MAR(1) coefficients can be interpreted and evaluate how reliable are estimates of interaction strength, rank, or sign; accuracy of short‐term forecasts; as well as the ability of MAR(1) models to predict the long‐term responses of communities submitted to environmental change such as PRESS perturbations. 3.The net effects of species j on species i are usually (90 to 95%) well recovered in terms of sign or rank, with the notable exception of overcompensatory dynamics. In actual values, net effects of species j on species i are not well recovered when the underlying dynamics are nonlinear. MAR(1) models are better at making short‐term, qualitative forecasts (next point going up or down) than at predicting long‐term responses to environmental perturbations, which can be severely over‐ as well as under‐estimated. 4.We conclude that when applying MAR(1) models to ecological data, inferences on net effects among species should be limited to signs, or the Gompertz assumption should be tested and discussed. This particular assumption on density‐dependence (log‐linearity) is also required for unbiased long‐term predictions. Overall, we think that MAR(1) models are highly useful tools to resolve and characterize community dynamics, but we recommend to use them in conjunction with alternative, nonlinear models resembling the ecological context in order to improve their interpretation in specific applications

Evaluation des fermetures spatio-temporelles mises en oeuvre à partir du 1er janvier 2020 pour la pêche au chalut en mer Méditerranée

L’examen des captures de juvéniles de merlu réalisées en 2020 montre une diminution de 55% par rapport à la moyenne sur la période 2015-2017 (223 tonnes contre 500 tonnes), tandis que la zone de fermeture concernée, quelles que soient les composantes de stocks concernés (merlu et rouget, juvéniles et adultes), contribue entre 15 à 20 % aux captures, et entre 30 et 40 % aux abondances estimées par la campagne MEDITS. En outre, la zone de fermeture englobe également des zones sensibles du point de vue des peuplements benthiques. En l’état des connaissances actuelles, la zone de fermeture mise en place au 1er Janvier 2020 semble montrer une bonne efficacité au regard des critères mis en avant par la Commission européenne et le CSTEP. Cette forte baisse pour le merlu est la conséquence de la mise en place des fermetures spatio-temporelles décidées par la France, mais également, pour partie, de la réduction d’effort des chalutiers de 10% conformément au plan de gestion, l’effet de la pandémie COVID-19 étant estimé négligeable. En revanche, pour le rouget, les augmentations de captures, consécutives aux bons recrutements récents, ne permettent pas de statuer sur l’efficacité de ces mesures sur cette espèce

Éléments de gestion de la pêcherie d’Octopus vulgaris en Occitanie et à travers le monde

En Occitanie, le poulpe roc figure parmi les espèces phares débarquées par la petite pêche côtière. Ce stock est aujourd’hui géré par des mesures votées au sein du Conseil d’Administration du Comité Régional des Pêches Maritimes et des Elevages Marins Occitanie (CRPMEM-O). Ce rapport met en lumière les informations existantes sur ce stock, et propose un tour du monde des mesures de gestion employées par d’autres régions au sein de pêcheries similaires

Suitable habitats of fish species in the Barents Sea

Many marine species exhibit poleward migrations following climate change. The Barents Sea, a doorstep to the fast‐warming Arctic, is experiencing large scale changes in its environment and its communities. Tracking and anticipating changes for management and conservation purposes at the scale of the ecosystem necessitate quantitative knowledge on individual species distribution drivers. This paper aims at identifying the factors controlling demersal habitats in the Barents Sea, investigating for which species we can predict current and future habitats and inferring those most likely to respond to climate change. We used non‐linear quantile regressions (QGAM) to model the upper quantile of the biomass response of 33 fish species to 10 environmental gradients and revealed three environmental niche typologies. Four main predictors seem to be limiting species habitat: bottom and surface temperature, salinity and depth. We highlighted three cases of present and future habitat predictability:(1) habitats of widespread species are not likely to be limited by the existing conditions within the Barents Sea. (2) habitats limited by a single factor are predictable and could shift if impacted by climate change. If the factor is depth, the habitat may stagnate or shrink if the environment becomes unsuitable. (3) habitats limited by several factors are also predictable but need to be predicted from QGAM applied on projected environmental maps. These modelled suitable habitats can serve as input to species distribution forecasts and end‐to‐end models, and inform fisheries and conservation management