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... and invertebrates have the ability to migrate over long-distance to find suitable habitats. As sea water temperature fluctuates seasonally and inter-annually, these species migrate to maintain physiologically suitable temperature in their surrounding water. For instance, Sardinella and other pelagic fishes migrate northward and southward, along the Northwest African coast such that they remain at the same temperature, despite strong seasonal fluctuation (Pauly 1994). Similarly, tuna and billfish migrate according to changes in sea surface temperature (SST) (Buxton & Smale 1989). Higher catch rates of yellowfin tuna ( Thunnus albacares ) and bigeye tuna ( Thunnus obesus ) were found in regions where SST increased during El Niño and La Niña periods (Lu et al. 2001). Specifically, yellowfin tuna displayed movements from tropical to higher latitude when temperatures in the tropical regions were low i.e. during La Niña periods. Given seasonal sea water temperature data, it is possible to predict intra-annual changes in distribution of marine pelagic fishes and invertebrates. The Sea Around Us project predicted annual average distributions of over 1,230 commercial marine fishes and invertebrates, of which over 190 were pelagic species (www.seaaroundus.org). Distributions of the species were predicted based on existing knowledge of species’ north-south latitudinal ranges, depth ranges, affinities to habitats, and known distribution boundaries from published literature (Close et al. 2006). Assuming that species’ north-south latitudinal boundaries are correlated with sea water temperature, their geographic ranges should shift northward and southward as sea water temperature fluctuates seasonally. Predicting seasonal distributions of pelagic marine fishes and invertebrates allows more accurate modelling of climate change impacts on these species. A dynamic bioclimate envelope model was developed to predict changes in geographic range of marine fishes and invertebrates under climate change scenarios (see Cheung et al . this vol.). This model is based largely on species temperature preference, as inferred from predicted species distributions. Assuming that highly mobile pelagic fishes and invertebrates migrate seasonally according to water temperature, ignoring such seasonal migration would over-estimate the temperature limits of the species and, thus under-estimate the impacts of sea water warming. Thus, the temperature preference of a species can be more accurately predicted if seasonal distributions are available. However, since data on seasonal distributions of the majority of marine fishes and invertebrates are lacking, predictions of seasonal distributions have to be based on some simple, but sensible, assumptions that allow application to a wide range of species. This is essentially the objective of this study. This paper documents an algorithm to predict seasonal changes in distributions of mobile marine fishes and invertebrates. The algorithm based on simple assumptions of correlation between seasonally (summer and winter) changes in species’ north-south latitudinal boundaries and fluctuation in sea water temperature. We apply the algorithm to predict distributions of commercially-exploited pelagic fishes and invertebrates. We illustrate the results from the algorithm with examples, and we discuss its pros and cons and its potential applications. We developed an algorithm that can predict the seasonal distribution of marine pelagic fishes and invertebrates. This algorithm was modified from the species distribution prediction model presented in Close et al . (2006). The details of the algorithm, together with its theoretical basis and assumptions, are summarized in the followings: We assume that the north-south latitudinal limits of species’ geographic ranges change according to ocean temperature in different seasons. The monthly average sea surface temperature data from 1956 to 2006 was obtained from Met Office Hadley Centre observation datasets (Rayner et al . 2007). The average annual sea surface temperature data within this period were computed. Two seasons were considered: summer and winter. In the northern hemisphere, summer includes July to September and winter includes January to March. On the other hand, Austral summer and winter are January to March and July to September, respectively. Globally, average sea water temperature at each latitudinal zone increases in summer and decreases in winter. As highly mobile fishes and invertebrates generally attempt to occupy regions with their preferred sea water temperature, we assume that their southern range boundaries move northward in summer to avoid the excessive temperatures near the southern boundary. In winter, their northern range boundaries move southward to avoid sea water temperature at the northern boundary being under the species’ physiological limits. Also, the contrast between summer and winter temperature increases towards higher latitude (Figure 1). Thus, the extent of seasonal shifting in north-south range boundaries should be higher in higher latitude as species must migrate further in higher latitude to maintain similar temperature in their surrounding water. We assume that the centroid of the distribution range also shifts according to temperature. Theoretically, the centroid of a species distribution range generally overlaps with regions where environmental conditions (e.g., sea water temperature) are optimal for the species (MacCall 1990). Thus, we assume that the average temperature at the latitudinal position of the centroid is close to the optimal preferred temperature of the species. We further assume that when latitudinal gradients of sea water temperature shift seasonally, the centroid of the distribution range shifts accordingly. Moreover, the mid-point between the centroid of the distribution range and the north/south range boundary shifts according to temperature. Predicting the seasonal shift in positions of the centroid, range boundaries and the mid-points centroid and range boundaries allow us to calculate seasonal distributions of the species. Longitudinal and vertical movement are not considered here, as these are not generally observed in large-scale seasonal migration patterns of pelagic marine fishes and squids. We determined the maximum potential shift in centroid and the mid-point based on the annual average distribution of a species, and the annual and seasonal sea surface temperature with such distribution. Firstly, the latitudinal position of the centroid of species’ annual average relative distribution, C ( Annual ), was calculated from: where A i is the species relative abundance in each spatial cell on the map, L i is the latitudinal coordinate of the cell, and n is the total number of cells within the species’ geographic range. When calculating the latitudinal position of the mid-point, we only include the cells lie within the distribution range between the centroid and northern/southern boundary. The latitudinal position of the mid-point, MP(Annual) , was computed from: where m is the total number of cells within the distribution range between the centroid and the northern or southern boundary. Secondly, we calculated the annual average sea surface temperature at C(Annual ) and MP(Annual) . The temperature at C ( Annual ) is assumed to be the optimal (preferred) temperature by the species T a . Moreover, we calculated the average sea surface temperature within the species range by latitudinal bands (every 30’) for each season (summer and winter). The latitudinal positions of the centroid in summer and winter, i.e., C S ’ and C W ’, respectively, were assumed to be the latitudinal bands with average temperature that was closest to the optimal preferred temperature T a . Thus, the maximum potential shift in centroid’s latitudinal position was calculated from: where CS s ‘ and CS w ’ are the maximum potential shift in centroid’s latitudinal positions in summer and winter, respectively. The latitudinal position of the mid-point in summer and winter i.e., MP S ’ and MP W ’ , were assumed to be the latitudinal bands with temperature closest to the temperature at MP(Annual) . In summer, MP(Annual) was calculated from the latitudinal values of the centroid and the southern bound i.e. MP S . In winter, MP(Annual) was calculated from the latitudinal values of the centroid and the northern bound i.e. MP W . The maximum potential shift in the latitudinal position of the mid-point was calculated from: where MS s ’ and MS w ’ are the maximum potential shift in the latitudinal positions of the mid- point in summer and winter, respectively. The actual shifts in latitudinal positions of the centroid and the mid-point were determined by the motility of the species. Species’ motility was represented here by a ‘motility index’ ( MI ). This index was calculated by using a fuzzy logic expert system that determines species’ motility from species’ maximum body length and aspect ratio of caudal fin (i.e., the ratio between the square of the height of fish’s caudal fin to the caudal fin area) (Cheung et al . this vol.). As aspect ratio is not available for invertebrates, ordinal levels representing the motility (Sedentary = 1; Low motility = 2; motility = 3; motility = 4) was assigned for each species (see Cheung et al . this vol. for details). The calculated motility index scales from 0 to 100. Higher index value indicates higher motility, i.e., higher ability to move, and vice versa ...
Context 2
... procedures lead to distribution range maps from which various inferences can be drawn, notably the temperature preference of the fish, since temperature is not used directly in any step in this process. Thus, when distribution ranges are mapped onto a temperature atlas, a temperature preference profile (TPP) can be inferred whose mode should indicate the preferred temperature of the animal in question, while the flanks indicate the normal temperature range of the species (see Figure 1, for an ...
Context 3
... FishBase (Froese & Pauly 2007) provide most of the information needed for the development of a global bioclimate envelope model for commercially exploited marine species. This is important as predicting future responses to climate change in marine biomes lags behind those for terrestrial species. Specifically, application of a bioclimate envelope model to large marine ecosystem is limited. This is partly because of the general lack of large-scale biological, ecological and biogeographical data for most marine species. However, such data are made available from the aforementioned global databases. For instance, distributions of relative abundance of all commercially exploited marine species are available from the Sea Around Us Project (Close et al . 2006). Combining such data with physical attributes such as global ocean temperature, bioclimate envelopes of the marine species could be inferred. These make it possible to construct bioclimate envelope models to predict impacts of climate change on all exploited marine species. This contribution documents a bioclimate envelope model that aims to predict the effects of global climate change on marine fishes and invertebrates. A major advance of the bioclimate envelope model presented in this contribution is the incorporation of population and dispersal dynamics for predicting impacts of climate change on distribution range. Such dynamics are important factors in determining biogeography of marine system under climate change scenarios (Pearson & Dawson 2003; Harley et al . 2006). Although our model does not explicitly deal with the effects of biological interactions and evolutionary changes (Pearson & Dawson 2003), we discuss the implications of these factors for the uncertainty of our model predictions. For the time being, we evaluate the performance of our model by using hypothetically generated climate data, as a first step to quantitatively evaluate the impacts of climate change on marine fishes and invertebrates. We then discuss how this approach will be applied to the study of likely effects of global changes to fisheries at a global scale. We developed a simulation model to predict changes in global distributions of commercial species under different climate change scenarios. This model is essentially a bioclimate envelope model combined with dynamic dispersals of animals. The Sea Around Us Project uses the distribution of commercial species (fishes and invertebrates) to map marine fisheries (Watson et al . 2004). The distributions have all been recently improved by Close et al . (2006), Lam et al. (this vol.) and Pauly et al . (this vol.). The future distributions of these species were assumed to be predictable from changes in ocean temperature, ocean advections and habitats (coral and sea ice coverage). Details of the model are described in the following. Descriptions of current distribution of marine species are fundamental to predicting changes in species distributions. The Sea Around Us Project produced distribution maps of over 1,200 commercially exploited fishes and invertebrates (www.seaaroundus.org). Each species’ distribution map is presented as potential relative abundance in 30’ latitude x 30’ longitude cells of the world ocean. The map was generated by a bio-climate model that predicts the suitability of each 30’ lat. x 30 long. cell to the studied species. Boundaries of each species’ distribution were delineated by the following information: (1) latitudinal range; (2) depth range; (3) affinity to certain habitats; (4) known distribution boundaries from published literature or experts’ knowledge, e.g., presence in a United Nations Food and Agriculture Organization (FAO) statistical area. Realistic assumptions were made on distributions of relative abundance within the above ecological limits. For instance, an ‘equatorial submergence’ filter was used to account for the tendency of demersal species to inhabit shallower waters in higher latitude (Ekman 1957; Close et al . 2006). It is emphasized that the Sea Around Us Project does not explicitly use temperature and primary production for any of the procedures discussed above. A description of the procedures to predict current species distributions are documented in Close et al . (2006). Lam et al . (this vol.) and Pauly et al . (this vol.) present the modifications required to represent the distribution of seasonally migrating fishes and latitudinal and longitudinal distribution asymmetry, respectively. Predicted current distributions of seven species: Nassau grouper ( Epinephelus striatus , Epinephelidae), Small yellow croaker ( Larimichthys polyactis , Sciaenidae) , Polar cod ( Boreogadus saida , Gadidae), Atlantic cod ( Gadus morhua , Gadidae), Western Australian rock lobster ( Panulirus Cygnus , Palinuridae), Antarctic toothfish ( Dissostichus mawsoni , Nototheniidae), and summer and winter distributions of Australian ruff ( Arripis georgianus , Australian ruff) were shown here as examples (Figure 1). These examples represent species with different life history and ecology, and from different geographic areas. We used these examples to illustrate the models described in this paper. Profiles of affinity to environmental and climatic attributes (i.e., seawater temperature, depth, habitat-associations and distance from edge of sea ice) for each species were based on current distribution maps generated by the Sea Around Us Project using the methodology documented in Close et al . (2006). We assume that the predicted current distributions realistically depict the bioclimate envelopes preferred by the species. In this paper, bio-climate envelopes are defined by (a) sea water temperature; (b) bathymetry; (c) habitats and (d) distance from sea ice. For each of these four bioclimate attributes, we expressed a species’ preference to different attribute values by its relative abundance. a. Sea water temperature Distributions of marine ectothermic animals are strongly dependent on temperature, as these animals are limited by their insufficient capacity of circulation and ventilation under low and high temperature (Pörtner 2001). Physiological performance of marine invertebrates and fishes changes continuously from optimum level to outside their thermal tolerance limits (Frederich & Pörtner 2000; Pörtner 2001). Also, foraging theory predicts that animals will select areas where, eventually, their growth rates can be maximized (Stephens & Krebs 1986). As growth is strongly dependent on physiological performance (Pauly 1980; Elliott 1982; Regier et al . 1990), it is reasonable to assume that ectothermic animals tend to inhabit area within their optimal temperature range (Hughes & Grand 2000). Thus, current distributions of marine animals should depict, at least roughly, their temperature preference. We calculated the temperature preference profile (TPP) of each species by combining current sea temperature with species’ predicted distribution ranges, the latter being determined by the Sea Around Us Project algorithm (Close et al. 2006). We define TPP as the probability of occurrence of a species at different sea water temperatures. To infer TPP from the predicted distribution maps, firstly we converted the observed sea temperature data obtained from Met Office Hadley Centre observations datasets ( to the 30’ x 30’ resolution of the Sea Around Us Project distribution maps. We overlaid the sea temperature over current distribution maps and calculated species’ relative abundance in different temperatures. We assume that relative abundances of demersal and benthopelagic species (e.g., Atlantic cod) depend mainly on annual sea bottom temperature while pelagic species (e.g. Atlantic herring) depend on seasonally-averaged (summer and winter) sea surface temperature (see Lam et al . this vol.). We examined the TPP of the studied species to ensure that temperature preference by each species is reasonable. Criteria for judging the acceptability of the temperature profiles include (1) whether the profile is approximately unimodal; and (2) the coefficient of variation of preferred temperature is less than 50%. Distribution maps that resulted in clearly multi-modal temperature profiles or a wide range of preferred temperature might be predicted inaccurately and were reviewed. Also, we assume a linear change in species preference (relative abundance) to water temperature between consecutive temperature classes (Figure 2). In some cases, sea-water temperature preference by a species is not uni-modal, i.e., there is more than one distinct peak of relative abundance in different temperatures (e.g., Atlantic cod and Western Australian rock lobster, Figure 2d and 2e). Physiological performance of marine ectotherms generally peaks at certain optimum temperature from where it declines to their thermal tolerance limits (Frederich & Pörtner 2000; Pörtner 2001). As we assume that each species distribution represents a single uniform distribution, we consider the multi-modal temperature preference distributions as artifacts which resulted from uncertainties of the original species distribution. As this may lead to unrealistic predictions of species’ responses to changes in sea temperature, we smoothed the TPP with running-mean to ensure that the distributions were generally uni-modal. To minimize distortion to the original temperature preference distribution, the number of temperature class averaged in the running mean calculation was increased from 3 until a uni-modal distribution was obtained. In the case of Western Australian rock lobster, a 3- temperature-classes running-mean is required to change the original bi-modal distribution to uni-modal (Figure 3). b. Depth limits We assume that a species’ distribution is also limited indirectly by depth. Thus, there are lower and upper limits of water depth outside of which a species does not occur. Different levels of temperature, oxygen concentration, food availability and ...
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Understanding the marine hydro-thermohaline environment is essential for terrestrial meteorology and the coastal ecosystem. Here, we provide insight into the hydro-thermohaline environment at the Qiongdongnan continental slope of the northern South China Sea and the mechanism controlling it, with focus on its short-term characteristics. We employ a...
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... Using this information, the model predicts the size-frequency distribution for each species by grid cell using a size-structured 'per recruit' model. In the last stage, the model estimates the abundance and biomass within a cell based on the carrying capacity of a cell (given by the SS component of the model), density-dependent population growth, larval dispersal and adult migration Cheung et al., 2008c). ...
... In the Size Spectrum component, the relationship between abundance and the size classes (both on a logarithmic scale) in the cell is linear, which can be not followed in reality (Blanchard et al., 2011). Regarding the DBEM component (Cheung et al., 2009): (i) it is assumed that current distributions represent habitat preferences of the species and are in equilibrium, (ii) estimates of population and dispersal parameters are obtained using indirect methods due to the lack of accurate parameters availability (see Cheung et al. (2008c)), (iii) only one parameter value is considered by species although differences could be found among ocean basins (Kolody et al., 2016), (vi) trophic interactions are modeled using theory and empirical data which is a simplification of the complexity among species but avoids the requirement of large and detailed data about the speciesspecific predatory interactions and, (v) model does not explicitly consider phenotypic or evolutionary adaptations of the species. Observations suggest that evolutionary processes and adaptations occurred with high frequency but they do not seem to change the absolute climate tolerance of the species (Parmesan, 2006). ...
... ) P = exp. (25.22 − E/kT) * W 0.76 E = activation energy of metabolism T = temperature in Kelvin ( • C + 273) k = Boltzmann's constantIntrinsic population growth rate(Hilborn and Walters, 1992) G = r * A * (1 − (A/KC)) r = intrinsic rate of population increase KC = population carrying capacity A = the relative abundance Larval recruitment(Cheung et al., 2008c;O'Connor, 2007) ln(PLD) = B0 − 1368* ( ln ( T / Tc ) ) -0.283* ( ln ( T / Tc ) ) 2 PLD = pelagic larvae duration T = surface temperature Tc = 15C DMis the developmental type of larvae (0 lecithotrophic, 1 planktotrophic) N = number of cells where species occur Larval dispersal (Cheung et al., 2008c; Hundsdorfer et al.M = − 0.4851 − 0.0824 + log (W ∞ ) + 0.6757 + log (K) + 0.4687 * log (T) W∞ = asymptotic weight T = average water temperature in the animal's range K = von Bertalanffy growth parameter Fishing mortality at MSY (Fernandes et al., 2013) F MSY = R/4 R = intrinsic population growth swordfish are mainly fished in the Pacific Ocean (WCPFC and IATTC areas) followed by the Atlantic Ocean. Atlantic and Southern bluefin tunas are only caught in a limited area (the Atlantic Ocean (ICCAT) and South to 10 • S (CCSBT), respectively). ...
... Adult Antarctic toothfish are demersal top predators, which can grow to over 2 m in length and reach over 50 years in age. The climate sensitivity of these fish populations (Cheung et al., 2008) and of the marine ecosystems and food webs they are part of is a main reason for the plan to establish a marine protected area (MPA) in this region ( Fig. 5; for the scientific justification of the eastern WSMPA phase 2 area, see Lowther et al., 2022). Regarding conservation of biodiversity, special attention has to be paid to rare habitats, since they are especially vulnerable. ...
Systematic long-term studies on ecosystem dynamics are largely
lacking from the East Antarctic Southern Ocean, although it is well
recognized that they are indispensable to identify the ecological impacts
and risks of environmental change. Here, we present a framework for
establishing a long-term cross-disciplinary study on decadal timescales. We
argue that the eastern Weddell Sea and the adjacent sea to the east, off
Dronning Maud Land, is a particularly well suited area for such a study,
since it is based on findings from previous expeditions to this region.
Moreover, since climate and environmental change have so far been
comparatively muted in this area, as in the eastern Antarctic in general, a
systematic long-term study of its environmental and ecological state can
provide a baseline of the current situation, which will be important for an
assessment of future changes from their very onset, with consistent and
comparable time series data underpinning and testing models and their
projections. By establishing an Integrated East Antarctic Marine
Research (IEAMaR) observatory, long-term changes in ocean dynamics,
geochemistry, biodiversity, and ecosystem functions and services will be
systematically explored and mapped through regular autonomous and ship-based
synoptic surveys. An associated long-term ecological research (LTER)
programme, including experimental and modelling work, will allow for
studying climate-driven ecosystem changes and interactions with impacts
arising from other anthropogenic activities. This integrative approach will
provide a level of long-term data availability and ecosystem understanding
that are imperative to determine, understand, and project the consequences
of climate change and support a sound science-informed management of future
conservation efforts in the Southern Ocean.
... Novel climatic conditions are fast emerging (Williams and Jackson 2007) and hence solutions that improve model transferability are urgently needed (Radeloff et al. 2015, Harris et al. 2018. Whilst mechanistic models offer accurate predictions at coarse spatial scales (Fernandes et al. 2013(Fernandes et al. , 2020, further integration with correlative frameworks may enable prediction at fine scales and in novel environments (Cheung et al. 2008, Fernandes et al. 2020, Gamliel et al. 2020. ...
The contributions of species to ecosystem functions or services depend not only on their presence but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance-based species distribution models fitted to 800 000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found a large amount of variation in the performance of abundance-based models. While many models performed poorly, a subset of models consistently reconstructed range-wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but our results indicate extrapolated predictions of abundance under changing climate have a much greater uncertainty. Our synthesis provides a road map for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation.
... The relative weight for each temperature class z of the temperature preference profile was calculated as TPP z = R z / R z , where R z is the relative abundance in each temperature class. A fuzzy logic model was used to model the movement between neighbouring cells based on differences in habitat suitability 50 . Emigration into a cell is favoured if habitat suitability is higher than surrounding cells, and immigration out of a cell is favoured if habitat suitability is lower than surrounding cells. ...
... We estimated larval production as 30% of spawning population biomass for each cell i, while larval mortality was 0.85 day −1 and settlement rate was 0.15 day −1 -these values were chosen based on the sensitivity testing of these parameters 50 . ...
Ocean acidification (OA) affects marine organisms through various physiological and biological processes, yet our understanding of how these translate to large-scale population effects remains limited. Here, we integrated laboratory-based experimental results on the life history and physiological responses to OA of the American lobster, Homarus americanus , into a dynamic bioclimatic envelope model to project future climate change effects on species distribution, abundance, and fisheries catch potential. Ocean acidification effects on juvenile stages had the largest stage-specific impacts on the population, while cumulative effects across life stages significantly exerted the greatest impacts, albeit quite minimal. Reducing fishing pressure leads to overall increases in population abundance while setting minimum size limits also results in more higher-priced market-sized lobsters (> 1 lb), and could help mitigate the negative impacts of OA and concurrent stressors (warming, deoxygenation). However, the magnitude of increased effects of climate change overweighs any moderate population gains made by changes in fishing pressure and size limits, reinforcing that reducing greenhouse gas emissions is most pressing and that climate-adaptive fisheries management is necessary as a secondary role to ensure population resiliency. We suggest possible strategies to mitigate impacts by preserving important population demographics.
... BOATS Carozza et al. (2016Carozza et al. ( , 2017 2D water temperature (averaged over top 75 m) 2D depth-integrated net primary production NA All commercial animal biomass from 10 g to 100 kg. DBEM Cheung et al. (2008Cheung et al. ( , 2010Cheung et al. ( , 2011Cheung et al. ( , 2016 2D sea surface temperature 2D depth-integrated net primary production 2D surface and bottom oxygen concentration, salinity and pH, sea ice, mixed layer depth, 3D current velocities >1200 fish and invertebrate species. ...
Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between -35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, and LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models’ drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts.
... Changes in catch potential (described below) of commercially exploited species were estimated using a dynamic bioclimatic envelope model (DBEM) (Cheung et al., 2008b(Cheung et al., , 2016b. The DBEM predicts how species abundance will change in space (on a 0.5 • longitude by 0.5 • latitude grid) and time (annual time steps) using an integrative approach by linking species distribution models (Jones et al., 2012), growth models (Pauly, 1980), physiological models (Pauly, 1981), population dynamics models (Pauly, 1980;Hilborn and Walters, 1992;O'Connor et al., 2007), and macroecological models (Cheung et al., 2008a). ...
... Species were assigned to two depth categories -pelagic or demersal -and environmental conditions were assigned accordingly (sea surface and sea bottom, respectively). Habitat preference was also incorporated to characterize a bioclimatic envelope and habitat preference profile for each species (Cheung et al., 2008b). Carrying capacity -the maximum possible biomass in a spatial cell -is A B C FIGURE 1 | Modelling the pathway of the impacts of ocean acidification from organism to population level in a multi-stressor framework. ...
... determined using the initial species distribution and is positively correlated with habitat suitability (Cheung et al., 2008b(Cheung et al., , 2016b. A major assumption of the DBEM is that each cell from the initial species distribution is at carrying capacity. ...
Elevated atmospheric carbon dioxide (CO 2 ) is causing global ocean changes and drives changes in organism physiology, life-history traits, and population dynamics of natural marine resources. However, our knowledge of the mechanisms and consequences of ocean acidification (OA) – in combination with other climatic drivers (i.e., warming, deoxygenation) – on organisms and downstream effects on marine fisheries is limited. Here, we explored how the direct effects of multiple changes in ocean conditions on organism aerobic performance scales up to spatial impacts on fisheries catch of 210 commercially exploited marine invertebrates, known to be susceptible to OA. Under the highest CO 2 trajectory, we show that global fisheries catch potential declines by as much as 12% by the year 2100 relative to present, of which 3.4% was attributed to OA. Moreover, OA effects are exacerbated in regions with greater changes in pH (e.g., West Arctic basin), but are reduced in tropical areas where the effects of ocean warming and deoxygenation are more pronounced (e.g., Indo-Pacific). Our results enhance our knowledge on multi-stressor effects on marine resources and how they can be scaled from physiology to population dynamics. Furthermore, it underscores variability of responses to OA and identifies vulnerable regions and species.
... For deeper water species, such as toothfish, the prognosis is less clear. One study indicated the potential for Antarctic toothfish to become extinct, due to its restricted range and affinity for below freezing temperatures (Cheung et al., 2008). ...
Southern Ocean ecosystems are globally important and vulnerable to global drivers of change, yet they remain challenging to study. Fish and squid make up a significant portion of the biomass within the Southern Ocean, filling key roles in food webs from forage to mid-trophic species and top predators. They comprise a diverse array of species uniquely adapted to the extreme habitats of the region. Adaptations such as antifreeze glycoproteins, lipid-retention, extended larval phases, delayed senescence, and energy-conserving life strategies equip Antarctic fish and squid to withstand the dark winters and yearlong subzero temperatures experienced in much of the Southern Ocean. In addition to krill exploitation, the comparatively high commercial value of Antarctic fish, particularly the lucrative toothfish, drives fisheries interests, which has included illegal fishing. Uncertainty about the population dynamics of target species and ecosystem structure and function more broadly has necessitated a precautionary, ecosystem approach to managing these stocks and enabling the recovery of depleted species. Fisheries currently remain the major local driver of change in Southern Ocean fish productivity, but global climate change presents an even greater challenge to assessing future changes. Parts of the Southern Ocean are experiencing ocean-warming, such as the West Antarctic Peninsula, while other areas, such as the Ross Sea shelf, have undergone cooling in recent years. These trends are expected to result in a redistribution of species based on their tolerances to different temperature regimes. Climate variability may impair the migratory response of these species to environmental change, while imposing increased pressures on recruitment. Fisheries and climate change, coupled with related local and global drivers such as pollution and sea ice change, have the potential to produce synergistic impacts that compound the risks to Antarctic fish and squid species. The uncertainty surrounding how different species will respond to these challenges, given their varying life histories, environmental dependencies, and resiliencies, necessitates regular assessment to inform conservation and management decisions. Urgent attention is needed to determine whether the current management strategies are suitably precautionary to achieve conservation objectives in light of the impending changes to the ecosystem.
... The Antarctic community has indicated that climate change will have a direct impact on the diversity and food resources of its marine system (Chown et al., 2012;Turner et al., 2013). A gradual increase in water temperature from global warming may result in changes in species composition (Cheung, Lam, & Pauly, 2008;O'Connor et al., 2007). However, the current understanding on how organisms and ecosystems will respond to atmospheric warming is poor. ...
There is a growing interest in exploiting Antarctic fisheries for human consumption. However, information on how the nutritional qualities of these resources will respond to the predicted seawater warming in the region for the next century is poor. The present research investigates changes in various nutritional indices of dietary importance (e.g. the ratio polyunsaturated to saturated fatty acids, the atherogenicity index, the thrombogenicity index, the hypo-cholesterolemic to hyper-cholesterolemic index, the health-promoting index, the flesh lipid quality and the ratio omega-3 to omega-6 index) by determining the fatty acid composition in muscle of Trematomus bernacchii (an Antarctic fish species) in its natural habitat (-1.87 °C) and warmer temperatures (0.0, 1.0, 2.0 °C). Comparison of the estimated nutritional indices at −1.87 °C with those at warmer temperatures revealed that seawater warming caused changes in the nutritional indices in the range of −12%<Δ < 30%. The observed changes were not statistically significant and ascribed to biological variability. Therefore, the nutritional values of T. bernacchii muscle were preserved after increasing the temperature of its natural habitat by + 4 °C. The present research is the first report describing the nutritional quality indices for an Antarctic fish species and the consequences of seawater warming on the nutritional value of T. bernacchii.
... 16 Managers and conservationists widely use statistical species distribution models (SDMs) to predict the potential range shifts in species distributions under climate change scenarios. 17,18 SDMs can appropriately assess the impact of climate change on species distribution on both terrestrial [e.g., 19,20] and marine species [e.g., 21]. A set of environmental conditions suitable for a given species has been defined as a bioclimatic envelope. ...
... Assessing shifts of the bioclimatic envelope under climate change scenarios can predict any shift in the species current range. 21 There is an important question that is how plant species will respond to climate change in the future, especially droughtresistant plant species. The drought-tolerant plant species grow naturally in different climates. ...
There is extensive evidence showing the impact of climate change on the biology and biogeography of species. Adopting drought-tolerant plants to conserve water is a potential adaptation to reduce the consequences of climate change. Accordingly, it was hypothesized that climate change would not affect potential distributions of drought-tolerant species. Here, this hypothesis was tested to model the potential distribution of three drought-resistant plant. Here, the potential distribution of Agropyron cristatum, Agropyron desertorum, and Festuca arundinacea was studied in Iran under current and future climate conditions, using 10 species distribution models. Sixty-two climate change scenarios (19 global climate models (GCMs) under four Representative Concentration Pathways (RCPs)) were used to model the potential distribution of the three plants in Iran in the future. The three species have different responses to predicted climate change due to species-environment interactions, species morphological and physiological advancement. The three species showed different responses to predicted climate change due to species-environment interactions. Festuca arundinacea and Agropyron cristatum will, respectively, experience the most and least severe decline in suitable habitats in the next 50 years. This result is because decreased annual precipitation caused an increase in habitat suitability for A. cristatum, while the same variable had the opposite effect for A. desertorum and F. arundinacea. On the other hand, F. arundinacea grows on moist soils that decreased annual precipitation caused a decrease in habitat suitability. Also, our results have clearly shown that plant species drought-stress tolerant are not immune to climate change and their current distributions undergo significant changes as a result of the changing of climate.
... Multiple spawning is a distinct reproductive strategy adapted by tropical and subtropical fish (Burt et al., 1988), probably to take advantage of the favourable environmental conditions for larval survival and recruitment (Massuti and Morales-Nin, 1997). A regional peak in spawning activity with year round spawning in varied proportion has been reported from various tropical regions (Cheung et al., 2008;Saroj et al., 2018). However, in temperate and subtropical regions, fish tend to synchronise spawning to the warm period of the year (Beardsley, 1967;Castro et al., 1999;Gatt et al., 2015;Molto et al., 2020). ...
The maturity, sex ratio, gonadosomatic index (GSI) and fecundity of Coryphaena hippurus was investigated from 347 specimens collected along Karnataka coast, southeastern Arabian Sea from August 2017 to May 2018. Overall sex ratio of 1:3.5 (male:female) indicated dominance of females in the fishery and differed significantly (p<0.05) in all the months, except in January, May and December. The fork length (FL) at 50% maturity (Lm 50) was estimated at 47.5 cm for both males and females. The pattern of GSI and maturity stages suggests peak spawning activity in August-September. However, the incidence of fishes capable of spawning all through the year in varied proportions implied that spawning activity occurs throughout the year. The absolute fecundity estimates varied from 1,00,298 eggs for a female of 53.5 cm FL to 6,15,267 eggs for female of 113 cm FL with an average fecundity of 3,18,446 eggs per female. The fecundity of fish increased with body length and weight as well as with gonad weight. The biological information on maturation, reproductive cycle, spawning periodicity and fecundity of C. hippurus in the tropical Arabian Sea could be useful for developing appropriate management tools and conservation strategies for this commercially important fish species.
