Factors controlling bed and bank erosion in the Illgraben (CH)

Debris flows can grow greatly in size and hazardous potential by eroding bed and bank materials. However, erosion mechanisms are poorly understood because debris flows are complex hybrids between a fluid flow and a moving mass of colliding particles, bed erodibility varies between events, and field measurements are hard to obtain. Here, we (i) quantify the spatio-temporal patterns of erosion and deposition and (ii) identify the key controls on debris-flow erosion in the Illgraben (CH). We use a dataset that combines information on flow properties, antecedent rainfall, and bed and bank erosion for 13 debris flows that occurred between 2019 and 2021. We show that spatio-temporal patterns of erosion and deposition in natural debris-flow torrents can be highly variable and dynamic, and we identify a memory effect where erosion is strong at locations of strong deposition during previous flows and vice versa. We find that flow conditions and antecedent rainfall (affecting bed wetness) jointly control debris-flow erosion. We find statistically significant correlations between channel erosion/deposition and a wide range of flow conditions, including frontal flow depth, velocity, and discharge, and flow volume, cumulative shear stress and seismic energy, as well as antecedent rainfall. Overall, flow conditions describing the cumulative forces exerted at the bed during an event, such as cumulative shear stress and flow volume, best explain erosion. A shear-stress approach accounting for bed erodibility may therefore be applicable for modelling and predicting debris-flow erosion. This work can provide input for model development by identifying correlations of flow and bed conditions with erosion that models should oblige

Image-Based Classification of Double-Barred Beach States Using a Convolutional Neural Network and Transfer Learning

Nearshore sandbars characterize many sandy coasts, and unravelling their dynamics is crucial to understanding nearshore sediment pathways. Sandbar morphologies exhibit complex patterns that can be classified into distinct states. The tremendous progress in data-driven learning in image recognition has recently led to the first automated classification of single-barred beach states from Argus imagery using a Convolutional Neural Network (CNN). Herein, we extend this method for the classification of beach states in a double-barred system. We used transfer learning to fine-tune the pre-trained network of ResNet50. Our data consisted of labelled single-bar time-averaged images from the beaches of Narrabeen (Australia) and Duck (US), complemented by 9+ years of daily averaged low-tide images of the double-barred beach of the Gold Coast (Australia). We assessed seven different CNNs, of which each model was tested on the test data from the location where its training data came from, the self-tests, and on the test data of alternate, unseen locations, the transfer-tests. When the model trained on the single-barred data of both Duck and Narrabeen was tested on unseen data of the double-barred Gold Coast, we achieved relatively low performances as measured by F1 scores. In contrast, models trained with only the double-barred beach data showed comparable skill in the self-tests with that of the single-barred models. We incrementally added data with labels from the inner or outer bar of the Gold Coast to the training data from both single-barred beaches, and trained models with both single- and double-barred data. The tests with these models showed that which bar the labels used for training the model mattered. The training with the outer bar labels led to overall higher performances, except at the inner bar. Furthermore, only 10% of additional data with the outer bar labels was needed for reasonable transferability, compared to the 20% of additional data needed with the inner bar labels. Additionally, when trained with data from multiple locations, more data from a new location did not always positively affect the model’s performance on other locations. However, the larger diversity of images coming from more locations allowed the transferability of the model to the locations from where new training data were added

Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model

Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems

Factors controlling bed and bank erosion in the Illgraben (CH)

Debris flows can grow greatly in size and hazardous potential by eroding bed and bank materials. However, erosion mechanisms are poorly understood because debris flows are complex hybrids between a fluid flow and a moving mass of colliding particles, bed erodibility varies between events, and field measurements are hard to obtain. Here, we (i) quantify the spatio-temporal patterns of erosion and deposition and (ii) identify the key controls on debris-flow erosion in the Illgraben (CH). We use a dataset that combines information on flow properties, antecedent rainfall, and bed and bank erosion for 13 debris flows that occurred between 2019 and 2021. We show that spatio-temporal patterns of erosion and deposition in natural debris-flow torrents can be highly variable and dynamic, and we identify a memory effect where erosion is strong at locations of strong deposition during previous flows and vice versa. We find that flow conditions and antecedent rainfall (affecting bed wetness) jointly control debris-flow erosion. We find statistically significant correlations between channel erosion/deposition and a wide range of flow conditions, including frontal flow depth, velocity, and discharge, and flow volume, cumulative shear stress and seismic energy, as well as antecedent rainfall. Overall, flow conditions describing the cumulative forces exerted at the bed during an event, such as cumulative shear stress and flow volume, best explain erosion. A shear-stress approach accounting for bed erodibility may therefore be applicable for modelling and predicting debris-flow erosion. This work can provide input for model development by identifying correlations of flow and bed conditions with erosion that models should oblige

Predictive performance of deep-learning-enhanced remote-sensing data for ecological variables of tidal flats over time

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R2) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management

Sea wrack delivery and accumulation on islands: factors that mediate marine nutrient permeability

Sea wrack provides an important vector of marine-derived nutrients to many terrestrial environments. However, little is known about the processes that facilitate wrack transport, deposition, and accumulation on islands. Three broad factors can affect the stock of wrack along shorelines: the amount of potential donor habitat nearby, climatic events that dislodge seaweeds and transfer them ashore, and physical characteristics of shorelines that retain wrack at a site. To determine when, where, and how wrack accumulates on island shorelines, we surveyed 455 sites across 101 islands in coastal British Columbia, Canada. At each site, we recorded wrack biomass, species composition, and shoreline biogeographical characteristics. Additionally, over a period of 9 mo, we visited a smaller selection of sites (n = 3) every 2 mo to document temporal changes in wrack biomass and species composition. Dominant wrack species were Zostera marina, Fucus distichus, Macrocystis pyrifera, Nereocystis luetkeana, Pterygophora californica, and Phyllospadix spp. The amount of donor habitat positively affected the presence of accumulated biomass of sea wrack, whereas rocky substrates and shoreline slope negatively affected the presence of sea wrack biomass. Biomass was higher during winter months, and species diversity was higher during summer months. These results suggest that shorelines with specific characteristics have the capacity to accumulate wrack, thereby facilitating the transfer of marine-derived nutrients to the terrestrial environment

Equivalent roles of marine subsidies and island characteristics in shaping island bird communities

AimSpecies distributions across islands are shaped by dispersal limitations, environmental filters and biotic interactions but the relative influence of each of these processes has rarely been assessed. Here, we examine the relative contributions of island characteristics, marine subsidies, species traits, and species interactions on avian community composition.LocationCentral Coast region of British Columbia, Canada.TaxonTerrestrial breeding birds.MethodsWe observed 3610 individuals of 32 bird species on 89 islands that spanned multiple orders of magnitude in area (0.0002–3 km$^{2}$). We fit a spatially explicit joint species distribution model to estimate the relative contributions of island physical characteristics, island‐specific inputs of marine subsidies, species' traits, and biotic interactions on species distributions. Biogeographic characteristics included island area, isolation, and habitat heterogeneity, while marine influence was represented by forest‐edge soil δ$^{15}$N, wrack biomass, shoreline substrate, and distance to shore. This approach also allowed us to estimate how much variation in distributions resulted from species' biological traits (i.e. body mass, feeding guild, feeding height, and nesting height).ResultsBird species distributions were determined almost equivalently by island biogeographic characteristics (23.5% of variation explained) and marine influence (24.8%). We detected variation in species‐specific responses to both island biogeographic characteristics and marine influence, but no significant effect of any biological trait examined. Additionally, we found evidence that habitat preferences were a more important driver than competitive interactions.Main ConclusionsAlthough most island biogeographic studies focus only on islands' physical characteristics, we found evidence for an equivalent role of marine subsidy in structuring island bird communities. Our study suggests that for small islands, disentangling the effects of island biogeographic characteristics, marine inputs, and biotic interactions is a useful next step in understanding species distributions

Human practices promote presence and abundance of disease-transmitting mosquito species

Humans alter the environment at unprecedented rates through habitat destruction, nutrient pollution and the application of agrochemicals. This has recently been proposed to act as a potentially significant driver of pathogen-carrying mosquito species (disease vectors) that pose a health risk to humans and livestock. Here, we use a unique set of locations along a large geographical gradient to show that landscapes disturbed by a variety of anthropogenic stressors are consistently associated with vector-dominated mosquito communities for a wide range of human and livestock infections. This strongly suggests that human alterations to the environment promote the presence and abundance of disease vectors across large spatial extents. As such, it warrants further studies aimed at unravelling mechanisms underlying vector prevalence in mosquito communities, and opens up new opportunities for preventative action and predictive modelling of vector borne disease risks in relation to degradation of natural ecosystems

Scale-dependent effects of marine subsidies on the island biogeographic patterns of plants

Although species richness can be determined by different mechanisms at different spatial scales, the role of scale in the effects of marine inputs on island biogeography has not been studied explicitly. Here, we evaluated the potential influence of island characteristics and marine inputs (seaweed wrack biomass and marine-derived nitrogen in the soil) on plant species richness at both a local (plot) and regional (island) scale on 92 islands in British Columbia, Canada. We found that the effects of subsidies on species richness depend strongly on spatial scale. Despite detecting no effects of marine subsidies at the island scale, we found that as plot level subsidies increased, species richness decreased; plots with more marine-derived nitrogen in the soil hosted fewer plant species. We found no effect of seaweed wrack at either scale. To identify potential mechanisms underlying the decrease in diversity, we fit a spatially explicit joint species distribution model to evaluate species level responses to marine subsidies and effects of biotic interactions among species. We found mixed evidence for competition for both light and nutrients, and cannot rule out an alternative mechanism; the observed decrease in species richness may be due to disturbances associated with animal-mediated nutrient deposits, particularly those from North American river otters (Lontra canadensis). By evaluating the scale-dependent effects of marine subsidies on island biogeographic patterns of plants and revealing likely mechanisms that act on community composition, we provide novel insights on the scale dependence of a fundamental ecological theory, and on the rarely examined links between marine and terrestrial ecosystems often bridged by animal vectors

Biogeographic features mediate marine subsidies to island food webs

Although marine subsidies often enrich terrestrial ecosystems, their influence is known to be context-dependent. Additionally, the multitrophic impact of marine subsidies has not been traced through food webs across physically diverse islands. Here, we test predictions about how island characteristics can affect marine enrichment of food web constituents and how nutrients flow through island food webs. To evaluate enrichment and trace marine nutrients across food webs, we used stable isotopes of soil, flora, and fauna (n = 4752 samples) collected from 97 islands in British Columbia, Canada. Island area was the strongest predictor of enrichment across taxa; we found that samples were more 15N-rich on smaller islands. Enrichment declined with distance from shore but less so on small islands, implying a higher per-unit-area subsidy effect. These area and distance-to-shore effects were taxon-specific, and nearly twice as strong in basal food web groups. We also found that increases in δ15N correlated with increases in %N in basal trophic groups, as well as in songbirds, implying biologically relevant uptake of a potentially limiting nutrient. Path analysis demonstrated that subsidies in soil flow through plants and detritivores, and into upper-level consumers. Our results reveal an interplay between island biogeography and marine subsidies in shaping island food webs through bottom-up processes