Identifying the spatial pattern and the drivers of the decline in the eastern English Channel chlorophyll-a surface concentration over the last two decades.

It has been established from previous studies that chlorophyll-a surface concentration has been declining in the eastern English Channel. This decline has been attributed to a decrease in nutrient concentrations in the rivers. However, the decrease in river discharge could also be a cause. In our study, rivers outflows and in-situ data have been compared to time series of satellite-derived chlorophyll-a concentrations. Dynamic Linear Model has been used to extract the dynamic and seasonally adjusted trends of several environmental variables. The results showed that, for the 1998-2019 period, chlorophyll-a levels stayed significantly lower than average and satellite images revealed a coast to offshore gradient. Chlorophyll-a concentration of coastal stations appeared to be related to the declining fluxes of phosphate while offshore stations were more related to nitrate-nitrite. Therefore, we can exclude that the climate variability, through river flows alone, has a dominant effect on the decline of chlorophyll-a concentration.

Trajectories of nutrients concentrations and ratios in the French coastal ecosystems: 20 years of changes in relation with large-scale and local drivers.

Along with their important diversity, coastal ecosystems receive various amounts of nutrients, principally arising from the continent and from the related human activities (mainly industrial and agricultural activities). During the 20th century, nutrients loads have increased following the increase of both the global population and need of services. Alongside, climate change including temperature increase or atmospheric circulation change has occurred. These processes, Ecosystem state changes are hard to monitor and predict. To study the long-term changes of nutrients concentrations in coastal ecosystems, eleven French coastal ecosystems were studied over 20 years as they encompass large climatic and land pressures, representative of temperate ecosystems, over a rather small geographical area. Both univariate (time series decomposition) and multivariate (relationships between ecosystems and drivers) statistical analyses were used to determine ecosystem trajectories as well as typologies of ecosystem trajectories. It appeared that most of the French coastal ecosystems exhibited trajectories towards a decrease in nutrients concentrations. Differences in trajectories mainly depended on continental and human influences, as well as on climatic regimes. One single ecosystem exhibited very different trajectories, the Arcachon Bay with an increase in nutrients concentrations. Ecosystem trajectories based on ordination techniques were proven to be useful tools to monitor ecosystem changes. This study highlighted the importance of local environments and the need to couple uni- and multi-ecosystem studies. Although the studied ecosystems were influenced by both local and large-scale climate, by anthropogenic activities loads, and that their trajectories were mostly similar based on their continental influence, non-negligible variations resulted from their internal functioning.

EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study.

SUMMARY: In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of 'easy-to-use' software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in the classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset. AVAILABILITY AND IMPLEMENTATION: EcoTransLearn is an open-source package. It is implemented in R and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Eutrophication: A new wine in an old bottle?

Eutrophication is one of the most common causes of water quality impairment of inland and marine waters. Its best-known manifestations are toxic cyanobacteria blooms in lakes and waterways and proliferations of green macro algae in coastal areas. The term eutrophication is used by both the scientific community and public policy-makers, and therefore has a myriad of definitions. The introduction by the public authorities of regulations to limit eutrophication is a source of tension and debate on the activities identified as contributing or having contributed decisively to these phenomena. Debates on the identification of the driving factors and risk levels of eutrophication, seeking to guide public policies, have led the ministries in charge of the environment and agriculture to ask for a joint scientific appraisal to be conducted on the subject. Four French research institutes were mandated to produce a critical scientific analysis on the latest knowledge of the causes, mechanisms, consequences and predictability of eutrophication phenomena. This paper provides the methodology and the main findings of this two years exercise involving 40 scientific experts.

Realized niche analysis of phytoplankton communities involving HAB: Phaeocystis spp. as a case study.

The link between harmful algal blooms, phytoplankton community dynamics and global environmental change is not well understood. To tackle this challenging question, a new method was used to reveal how phytoplankton communities responded to environmental change with the occurrence of an harmful algae, using the coastal waters of the eastern English Channel as a case study. The great interannual variability in the magnitude and intensity of Phaeocystis spp. blooms, along with diatoms, compared to the ongoing gradual decrease in anthropogenic nutrient concentration and rebalancing of nutrient ratios; suggests that other factors, such as competition for resources, may also play an important role. A realized niche approach was used with the Outlying Mean Index analysis and the dynamics of the species' realized subniches were estimated using the Within Outlying Mean Indexes calculations under low (L) and high (H) contrasting Phaeocystis spp. abundance. The Within Outlying Mean Indexes allows the decomposition of the realized niche into realized subniches, found within the subset of habitat conditions and constrained by a subset of a biotic factor. The two contrasting scenarios were characterized by significantly different subsets of environmental conditions and diatom species (BV-step analysis), and different seasonality in salinity, turbidity, and nutrients. The subset L environmental conditions were potentially favorable for Phaeocystis spp. but it suffered from competitive exclusion by key diatom species such as Skeletonema spp., Thalassiosira gravida, Thalassionema nitzschioides and the Pseudo-nitzchia seriata complex. Accordingly, these diatoms species occupied 81% of Phaeocystis spp.'s existing fundamental subniche. In contrast, the greater number of diatoms, correlated with the community trend, within subset H exerted a weaker biological constraint and favored Phaeocystis spp. realized subniche expansion. In conclusion, the results strongly suggest that both abiotic and biotic interactions should be considered to understand Phaeocystis spp. blooms with greater consideration of the preceeding diatoms. HABs needs must therefore be studied as part of the total phytoplankton community.