Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.
Understanding tipping point dynamics in harvested ecosystems is of crucial importance for sustainable resource management because ignoring their existence imperils social-ecological systems that depend on them. Fisheries collapses provide the best known examples for realizing tipping points with catastrophic ecological, economic and social consequences. However, present-day fisheries management systems still largely ignore the potential of their resources to exhibit such abrupt changes towards irreversible low productive states. Using a combination of statistical changepoint analysis and stochastic cusp modelling, here we show that Western Baltic cod is beyond such a tipping point caused by unsustainable exploitation levels that failed to account for changing environmental conditions. Furthermore, climate change stabilizes a novel and likely irreversible low productivity state of this fish stock that is not adapted to a fast warming environment. We hence argue that ignorance of non-linear resource dynamics has caused the demise of an economically and culturally important social-ecological system which calls for better adaptation of fisheries systems to climate change.
The stock–recruitment relationship is the basis of any stock prediction and thus fundamental for fishery management. Traditional parametric stock–recruitment models often poorly fit empirical data, nevertheless they are still the rule in fish stock assessment procedures. We here apply a multi-model approach to predict recruitment of 20 Atlantic cod (Gadus morhua) stocks as a function of adult biomass and environmental variables. We compare the traditional Ricker model with two non-parametric approaches: (i) the stochastic cusp model from catastrophe theory and (ii) multivariate simplex projections, based on attractor state-space reconstruction. We show that the performance of each model is contingent on the historical dynamics of individual stocks, and that stocks which experienced abrupt and state-dependent dynamics are best modelled using non-parametric approaches. These dynamics are pervasive in Western stocks highlighting a geographical distinction between cod stocks, which have implications for their recovery potential. Furthermore, the addition of environmental variables always improved the models’ predictive power indicating that they should be considered in stock assessment and management routines. Using our multi-model approach, we demonstrate that we should be more flexible when modelling recruitment and tailor our approaches to the dynamical properties of each individual stock.
The objective of this study is to analyse at fine scale the annual, seasonal and spatial distributions of several species in the Eastern English Channel (EEC). On the one hand, data obtained from scientific surveys are not available all year through, but are considered to provide consistent yearly and spatially resolved abundance indices. On the other hand, on-board commercial data do cover the whole year, but generally provide a biased perception of stock abundance. The combination of scientific and commercial catches per unit of effort (CPUEs), standardized using a delta-generalized linear model, allowed to infer spatial and monthly dynamics of fish distributions in the EEC, which could be compared with previous knowledge on their life cycles. Considering the scientific survey as a repository, the degree of reliability of commercial CPUEs was assessed with survey-based distribution using the Local Index of Collocation. Large scale information was in agreement with literature, especially for cuttlefish. Fine scale consistency between survey and commercial data was significant for half of the 19 tested species (e.g. whiting, cod). For the other species (e.g. plaice, thornback ray), the results were inconclusive, mainly owing to poor commercial data coverage and/or to particular aspects of the species biology.Ecosystem-Based Fisheries Management (EBFM) requires enhancing knowledge of 32 ecosystem functioning, therefore allowing forecasting the impact of fisheries on salient 33 ecosystem components (Long et al., 2015) and to design future management plans and tools 34 including Marine Protected Areas (Meyer et al., 2007) or fishing closures (Hunter et al., 35 2006). This necessitates a stepwise approach, the first tier of which, and one of the most 36 important, is to gain fine scale knowledge on the seasonal and geographic distribution of 37 marine organisms, in general, and fish stocks in particular (Booth, 2000). 38 Scientific surveys have been implemented for decades to derive spatially-and yearly-resolved 39 abundance indices of commercial fish and shellfish species (e.g. van Keeken et al., 2007). 40Surveys provide abundance indices, derived from standardized and controlled protocols, 41 which allow for a wide spatial coverage associated with a weak selectivity (Verdoit et al., 42 2003). Survey data, however, are costly to obtain and therefore rarely provide for adequate 43 seasonal coverage of the resource distribution. In contrast, information derived from 44 commercial fisheries are generally available all year through. Consequently, the catch per unit 45 of effort (CPUE), the most common and easily collected fishery-dependent index of 46 abundance (Maunder and Punt, 2004), has the potential to reflect fish distributions. However, 47 commercial CPUEs can generally not be used directly as abundance indicators. This is 48 because fishers target rather than sample fish densities, and continuously adapt their activities 49 to prevailing conditions, through technological development and tactical ...
Spatial interactions between saithe (Pollachius virens) and hake (Merluccius merluccius) were investigated in the North Sea. Saithe is a well-established species in the North Sea, while occurrence of the less common hake has recently increased in the area. Spatial dynamics of these two species and their potential spatial interactions were explored using binomial generalized linear models (GLM) applied to the International Bottom Trawl Survey (IBTS) data from 1991 to 2012. Models included different types of variables: (i) abiotic variables including sediment types, temperature, and bathymetry; (ii) biotic variables including potential competitors and potential preys presence; and (iii) spatial variables. The models were reduced and used to predict and map probable habitats of saithe, hake but also, for the first time in the North Sea, the distribution of the spatial overlap between these two species. Changes in distribution patterns of these two species and of their overlap were also investigated by comparing species’ presence and overlap probabilities predicted over an early (1991–1996) and a late period (2007–2012). The results show an increase in the probability over time of the overlap between saithe and hake along with an expansion towards the southwest and Scottish waters. These shifts follow trends observed in temperature data and might be indirectly induced by climate changes. Saithe, hake, and their overlap are positively influenced by potential preys and/or competitors, which confirms spatial co-occurrence of the species concerned and leads to the questions of predator–prey relationships and competition. Finally, the present study provides robust predictions concerning the spatial distribution of saithe, hake, and of their overlap in the North Sea, which may be of interest for fishery managers.
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