A region of freshwater influence (ROFI) under hypertidal conditions is used to demonstrate inherent problems for nested operational modelling systems. Such problems can impact the accurate simulation of freshwater export within shelf seas, so must be considered in coastal ocean modelling studies. In Liverpool Bay (our UK study site), freshwater inflow from 3 large estuaries forms a coastal front that moves in response to tides and winds. The cyclic occurrence of stratification and remixing is important for the biogeochemical cycles, as nutrient and pollutant loaded freshwater is introduced into the coastal system. Validation methods, using coastal observations from fixed moorings and cruise transects, are used to assess the simulation of the ROFI, through improved spatial structure and temporal variability of the front, as guidance for best practise model setup. A structured modelling system using a 180 m grid nested within a 1.8 km grid demonstrates how compensation for error at the coarser resolution can have an adverse impact on the nested, high resolution application. Using 2008, a year of typical calm and stormy periods with variable river influence, the sensitivities of the ROFI dynamics to initial and boundary conditions are investigated. It is shown that accurate representation of the initial water column structure is important at the regional scale and that the boundary conditions are most important at the coastal scale. Although increased grid resolution captures the frontal structure, the accuracy in frontal position is determined by the offshore boundary conditions and therefore the accuracy of the coarser regional model.
Camera trap surveys are a popular ecological monitoring tool that produce vast numbers of images making their annotation extremely time‐consuming. Advances in machine learning, in the form of convolutional neural networks, have demonstrated potential for automated image classification, reducing processing time. These networks often have a poor ability to generalise, however, which could impact assessments of species in habitats undergoing change. Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes. Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively). Generalisability to new camera locations within a disturbance level was poor (mean F1‐score: 0.32). Performance across unseen habitat disturbance levels was worse (mean F1‐score: 0.27). Training the network on multiple disturbance levels improved generalisability (mean F1‐score on unseen disturbance levels: 0.41). Cropping images to bounding boxes improved overall performance (F1‐score: 0.77 vs. 0.47) and generalisability (mean F1‐score on unseen disturbance levels: 0.73), but at a cost of losing images that contained animals which the detector failed to detect. These results suggest researchers should consider using an object detector before passing images to a classifier, and an improvement in classification might be seen if labelled images from other studies are added to their training data. Composition of training data was shown to be influential, but including rarer classes did not compromise performance on common classes, providing support for the inclusion of rare species to inform conservation efforts. These findings have important implications for use of these methods for long‐term monitoring of habitats undergoing change, as they highlight the potential for misclassifications due to poor generalisability to impact subsequent ecological analyses. These methods therefore need to be considered as dynamic, in that changes to the study site would need to be reflected in the updated training of the network.
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