Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Food-web dynamics in the Portuguese continental shelf ecosystem between 1986 and 2017: unravelling drivers of sardine decline’

The Ecopath food web model that describes the Portuguese continental shelf ecosystem was fitted to available time series to investigate the ecosystem dynamics and decline of the Portuguese sardine stock. Our results showed that the Portuguese continental shelf ecosystem model is a good predictor of biomass and catch time-series trends for many species. Moreover, our results suggested that the main factors that drove the ecosystem dynamics were trophic interactions, fishing and environmental forcing (sea surface temperature). The same drivers were important in explaining sardine decline among which the largest contribution was observed when incorporating sea surface temperature forcing on adult sardine, followed by fishing. Moreover, sardine eggs predators (i.e., chub mackerel, horse mackerel and bogue) were emphasised as the most important among trophic interactions in explaining sardine trend between 1986 and 2017. Furthermore, a flow control hypothesis test, showed that model parametrization allows explaining sardine behaviour through wasp-waist control mechanism, characteristic for upwelling systems. This study represents an important step forward in understanding the changes that occurred in the Portuguese continental shelf ecosystem and provides helpful insights to explain Portuguese sardine decline. •Portuguese continental shelf Ecopath model was fitted to available time-series.•Environment, fishing and trophic interactions drove ecosystem dynamics.•Environmental factors were the major contributor to sardine decline.•Sardine eggs predators were identified as key species interacting with sardine.•Sardine behaviour can be explained through the wasp-waist control mechanism

Workshop on the production of abundance estimates for sensitive species (WKABSENS). ICES Scientific Reports, 3:96.

The Workshop on the production of annual estimates of abundance of sensitive species (WKABSENS) met to define sensitive species, collate ICES assessments of abundance where these are available, and estimate indices of their abundance per swept-area where not, for the OSPAR area. The analyses identified 140 potentially sensitive species or species complexes, among which 10 are diadromous and three are coastal, 20 have uncertain species ID and nine were identified as sensitive in only one of the sources examined. Among the sensitive species and species complexes, there was sufficient data to provide abundance indices for 50 species, of which 16 had existing stock assessments whereas the workshop derived abundance estimates for the remaining 34 species from survey data. Three statistical modelling approaches (binomial, General Additive Models (GAMs) and VAST) and were explored and the final abundance indices were calculated using GAMs. The species were divided into stocks before estimating abundance indices where these could be identified from the spatial distribution of the species in the survey. The group considered that a similar analysis using data from additional surveys, commercial indices or data from bycatch observers can potentially provide improved abundance estimates for species with variable or low catchability, such as deep-water and pelagic species