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Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Marine phytoplankton biomass dynamics are affected by eutrophication, ocean warming, and ocean acidification. These changing abiotic conditions may impact phytoplankton biomass and its spatiotemporal dynamics. In this study, we used a nutrient–phytoplankton–zooplankton (NPZ) model to quantify the relative importance of the bottom-up and top-down determinants of phytoplankton biomass dynamics in the Belgian part of the North Sea (BPNS). Using four years (2014–2017) of monthly observations of nutrients, solar irradiance, sea surface temperature, chlorophyll-a, and zooplankton biomass at ten locations, we disentangled the monthly, seasonal, and yearly variation in phytoplankton biomass dynamics. To quantify how the relative importance of determinants changed along a near–offshore gradient, the analysis was performed for three spatial regions, i.e., the nearshore region (<10 km to the coastline), the midshore region (10–30 km), and the offshore region (>30 km). We found that, from year 2014 to 2017, the phytoplankton biomass dynamics ranged from 1.4 to 23.1 mg Chla m−3. Phytoplankton biomass dynamics follow a general seasonal cycle, as is the case in other temperate regional seas, with a distinct spring bloom (5.3–23.1 mg Chla m−3) and a modest autumn bloom (2.9–5.4 mg Chla m−3). This classic bimodal bloom pattern was not observed between 2003 and 2010 in the BPNS. The seasonal pattern was most expressed in the nearshore region. The relative contribution of factors determining phytoplankton biomass dynamics varied spatially and temporally. Throughout a calendar year, solar irradiance and zooplankton grazing were the most influential determinants in all regions, i.e., they jointly explained 38–65% of the variation in the offshore region, 45–71% in the midshore region, and 56–77% in the nearshore region. In the near- and midshore regions, nutrients were the greatest limit on phytoplankton production in the month following the spring bloom (44–55%). Nutrients were a determinant throughout the year in the offshore region (27–62%). During winter, sea surface temperature was a determinant in all regions (15–17%). By the high-resolution spatiotemporal analysis of the relative contributions of different determinants, this study contributes to a better mechanistic understanding of the spatiotemporal dynamics of phytoplankton biomass in the southern North Sea. This detailed understanding is anticipated to contribute to the definition of targeted management strategies for the BPNS and to support sustainable development in Belgium’s blue economy.

In several places around the world, coastal marsh vegetation is converting to open water through the formation of pools. This is concerning, as vegetation die-off is expected to reduce the marshes’ capacity to adapt to sea level rise by vegetation-induced sediment accretion. Quantitative analyses of the spatial and temporal development of marsh vegetation die-off are scarce, although these are needed to understand the biogeomorphic feedback effects of vegetation die-off on flow, erosion, and sedimentation. In this study, we quantified the spatial and temporal development of marsh vegetation die-off with aerial images from 1938 to 2010 in a submerging coastal marsh along the Blackwater River (Maryland, U.S.A). Our results indicate that die-off begins with conversion of marsh vegetation into bare open water pools that are relatively far (> 75 m) from tidal channels. As vegetation die-off continues, pools expand, and new pools emerge at shorter and shorter distances from channels. Consequently larger pools are found at larger distances from the channels. Our results suggest that the size of the pools and possibly the connection of pools with the tidal channel system have important bio-geomorphic implications and aggravate marsh deterioration. Moreover, we found that the temporal development of vegetation die-off in moderately degraded marshes is similar as the spatial die-off development along a present-day gradient, which indicates that the contemporary die-off gradient might be considered a chronosequence that offers a unique opportunity to study vegetation die-off processes.

Coastal marshes provide valuable ecosystem functions, but some are facing increasing risks of vegetation loss due to sea level rise and other stressors. A key question is how tidal flow and sedimentation patterns are affected by the spatiotemporal patterns of vegetation loss, as sediment accretion with sea level rise largely affects the potential for marsh recovery. Here, we performed a field study in a macrotidal reed marsh and simulated typical spatiotemporal patterns of vegetation loss by consecutive mowing. For each mowing pattern, the spatial patterns of flow velocities and sedimentation rates were recorded. Our results indicate that initial vegetation loss in inner marsh portions, with an intact vegetation belt alongside channel edges, has limited effect on tidal flows over the marsh. However, subsequent creation of unvegetated corridors connecting the bare inner marsh and the channels increases flow velocities in these corridors but not in remaining vegetation patches. Finally, when all vegetation is removed, sheet flow occurs over the whole marsh instead of concentrated channel flow. Effects on spatial sedimentation patterns are complex and not significant on all measuring locations. Nevertheless, our study indicates that complete vegetation removal results in redistributed sedimentation patterns, with a general tendency of locally reduced sedimentation rates close (< 15 m) to channels and increased sediment supply to inner marshes 15–50 m from channels. Our results highlight that feedbacks between spatial patterns of vegetation loss, tidal sediment transport, and deposition are key to understanding and mitigating risks of marsh loss in face of sea level rise.

Coastal marsh conversion into ponds, which may be triggered by sea-level rise, is considered an important driver of marsh loss and their valuable ecosystem services. Previous studies have focused on the role of wind waves in driving the expansion of interior marsh ponds, through lateral erosion of marsh edges surrounding the ponds. Here, we propose another mechanism between sea-level rise, increasing marsh inundation, and decreasing marsh soil strength (approximated here as resistance to shear and penetration stress), that further contributes to marsh erosion and pond expansion. Our field measurements in the Blackwater marshes (Maryland, USA), a microtidal marsh system with organic-rich soils, indicate that (1) an increase in tidal inundation time of the marsh surface above a certain threshold (around 50 % of the time) is associated with a substantial loss of strength of the surficial soils; and (2) this decrease in soil strength is strongly related to the amount of belowground vegetation biomass, which is also found to decrease with increasing tidal inundation at pond bottoms, where the soil has a very low strength. Our finding of decreasing marsh soil strength along a spatial gradient of increasing marsh inundation coincides with a gradient of increasing historical marsh loss by pond expansion, suggesting that feedbacks between sea-level rise, increasing marsh inundation and decreasing marsh soil strength combine to amplify marsh erosion and pond expansion.

Aim To verify which vegetation and environmental factors are the most important in determining the spatial and temporal variability of average and maximum values of radiation use efficiency (RUEann and RUEmax, respectively) of cold and temperate forests. Location Forty‐eight cold and temperate forests distributed across the Northern Hemisphere. Major taxa studied Evergreen and deciduous trees. Time period 2000–2011. Methods We analysed the impact of 17 factors as potential determinants of mean RUE (at 8 days interval, annual and interannual level) and RUEmax (at annual and interannual level) in cold and temperate forests by using linear regression and random forests models. Results Mean annual RUE (RUEann, c. 1.1 gC/MJ) and RUEmax (c. 0.8 gC/MJ) did not differ between cold and temperate forests. However, for cold forests, RUEann was affected by temperature‐related variables, while for temperate forests RUEann was affected by drought‐related variables. Leaf area index (LAI) was important for both forest types, while N deposition only for cold forests and cloud cover only for temperate forest. RUEmax of cold forests was mainly driven by N deposition and LAI, whereas for temperate forests only a weak relationship between RUEmax and CO2 concentration was found. Short‐term variability of RUE was strongly related to the meteorological variables and varied during the season and was stronger in summer than spring or autumn. Interannual variability of RUEann and RUEmax was only weakly related to the interannual variability of the environmental drivers. Main conclusions Cold and temperate forests show different relationships with the environment and vegetation properties. Among the RUE drivers observed, the least anticipated was N deposition. RUE is strongly related to short‐term and seasonal changes in meteorological variables among seasons and among sites. Our results should be considered in the formulation of climate zone‐specific tools for remote sensing and global models.

The presence of bare patches within otherwise vegetated coastal marshes is sometimes considered to be a symptom of marsh dieback and the subsequent loss of important ecosystem services. Here we studied the topographical conditions determining the presence and revegetation of bare patches in three marsh sites with contrasting tidal range, sediment supply, and plant species: the Scheldt estuary (the Netherlands), Venice lagoon (Italy), and Blackwater marshes (Maryland, USA). Based on GIS (geographic information system) analyses of aerial photos and lidar imagery of high resolution (≤2×2 m pixels), we analyzed the topographic conditions under which bare patches occur, including their surface elevation, size, distance from channels, and whether they are connected or not to channels. Our results demonstrate that, for the different marsh sites, bare patches can be connected or unconnected to the channel network and that there is a positive relationship between the width of the connecting channels and the size of the bare patches, in each of the three marsh sites. Further, pixels located in bare patches connected to channels occur most frequently at the lowest elevations and farthest distance from the channels. Pixels in bare patches disconnected from channels occur most frequently at intermediate elevations and distances from channels, and vegetated marshes dominate at highest elevations and shortest distances from channels. In line with previous studies, revegetation in bare patches is observed in only one site with the highest tidal range and highest sediment availability, and it preferentially occurs from the edges of small unconnected bare patches at intermediate elevations and intermediate distances from channels. Although our study is only for three different marsh sites with large variations in local conditions, such as tidal range, sediment availability, and plant species, it suggests that similar topographic conditions determine the occurrence of bare patches. Such insights may inform decision makers on coastal marsh management on where to focus monitoring of early signatures of marsh degradation.

Over the last decade, plankton research has experienced extensive developments in automatic image acquisition for identifying and quantifying plankton species. This information is useful for the reporting of plankton occurrences and ecological data. Imagery instruments can vary in the way they sample (benchtop or in situ imagers) and the particle’s size range they target (see Lombard et al. (2019) for an extensive comparison of instruments and specifications). However, due to the wide variety of instruments and their (automatic) output data and formats, it is challenging to integrate datasets that originate from different sources. For this reason, we developed recommendations for plankton imagery data management, which can promote the ability to make these datasets as FAIR (Findable, Accessible, Interoperable and Reusable principles), as possible. The workflow presented here could inspire other Biodiversity Information Standards TDWG communities working with (automated) imagery data (e.g., camera traps) such as the Audubon Core and Machine Observations Interest Group. The recommended data format follows the OBIS-ENV-DATA format (De Pooter et al. 2017), a Darwin Core-based approach to standardise biodiversity data (Wieczorek et al. 2012) used in EurOBIS, the European node of the Ocean Biodiversity Information System (OBIS) and EMODnet Biology, the European Marine Biodiversity Data Network. However, this format does not include sufficient information for imagery data, therefore we propose the use of additional Darwin Core terms. For example, by including the terms identifiedBy, identificationVerificationStatus and identificationReferences in the Occurrence table, more clarity is reported regarding the uncertainty of the classification made by an algorithm. Thus, data providers can publish manually validated datasets or datasets produced by fully automated plankton identification workflows; and users can choose to use validated or not validated data. See in Suppl. material 1 a practical example on how to report an imagery dataset following the best practices. Moreover, the OBIS-ENV-DATA format allows the ingestion of additional information thanks to the use of the Darwin Core (DwC) Extended Measurement Or Facts or eMoF extension in the DwC Event core. The eMoF stores biotic, abiotic and sampling measurements and facts that are related to the Event and Occurrence table. An important aspect of this extension is that it includes standardised terms and controlled vocabularies, such as the British Oceanographic Data Centre (BODC) vocabularies, to standardise parameters that are not covered by DwC. The advantages of these is to unambiguously report information and to include those measurements that cannot be reported in the Event and Occurrence table (e.g., reporting abundance or biomass of plankton), and that are crucial to investigate ecosystem functioning questions. As a consequence, biodiversity data aggregators can extend their scope beyond species occurrence data. Fig. 1 summarises a typical dataflow that goes from imagery data acquisition to publication in several steps: Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Images are cropped and classified with software. This can be done in EcoTaxa, a web application that allows users to taxonomically classify images of individual organisms. Data is formatted in OBIS-ENV-DATA format. This format can be exported from EcoTaxa through its API. Data is submitted to EurOBIS via the IPT (Integrated Publishing Toolkit). Data is quality controlled by the BioCheck tool. Data in EurOBIS can flow to EMODnet Biology, OBIS and GBIF (Global Biodiversity Information Facility). Plankton imagery instrument operators now have the possibility to format their data following the best practices and recommendations for plankton imagery data management (Martin-Cabrera et al. 2022). After a dataset is formatted following these guidelines, it can be submitted to the international biodiversity data aggregators, EurOBIS, EMODnet Biology and GBIF. Additionally a (semi) automated dataflow is presented where data providers can classify images in EcoTaxa and export the data in the required formats using an API before submission to EurOBIS. The next steps are to disseminate these best practices, encouraging plankton imagery data generators to implement these workflows to share their data easily, enriching these data portals and encouraging cross collaborations to create data products covering broader geographic scales and plankton species.