Species distribution modeling of northern sea otters (Enhydra lutris kenyoni) in a data-limited ecosystem.

Species distribution models (SDMs) are used to map and predict the geographic distributions of animals based on environmental covariates. Typically, SDMs require high-resolution habitat data and time series information on animal locations. For data-limited regions, defined as having scarce habitat or animal survey data, modeling is more challenging, often failing to incorporate important environmental attributes. For example, for sea otters (Enhydra lutris), a federally protected keystone species with variable population trends across the species' range, predictive modeling of distributions has been successfully conducted in areas with robust sea otter population and habitat data. We used open-access data and employed a presence-only model, maximum entropy (MaxEnt), to investigate subtidal habitat associations (substrate and algal cover, bathymetry, and rugosity) of northern sea otters (E. lutris kenyoni) for a data-limited ecosystem, represented by Kachemak Bay, Alaska. Habitat association results corroborated previous findings regarding the importance of bathymetry and understory kelp as predictors of sea otter presence. Novel associations were detected as filamentous algae and shell litter were positively and negatively associated with northern sea otter presence, respectively, advancing existing knowledge of sea otter benthic habitat associations useful for predicting habitat suitability. This study provides a quantitative framework for conducting species distribution modeling with limited temporal and spatial animal distribution and abundance data. Utilizing drop camera information, our novel approach allowed for a better understanding of habitat requirements for a stable northern sea otter population, including bathymetry, understory kelp, and filamentous algae as positive predictors of sea otter presence in Kachemak Bay, Alaska.

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay.

This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 "Reefs" habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.

Identifying Critical Vegetation Types for Biodiversity Conservation in the Americas / Identificación de tipos de vegetación críticos para la conservación de la biodiversidad en las Américas / Identificação de Tipos de Vegetação Críticos para a Conservação da Biodiversidade nas Américas

The Americas contain highly biodiverse yet vulnerable ecosystems, with many threatened species inadequately protected. Finer-scale, localized habitat assessments are crucial for effective conservation planning, but continental-scale high-resolution vegetation maps remain limited. This study addresses this gap by identifying critical vegetation types across the Americas using the standardized framework of the International Vegetation Classification (IVC) system at the macrogroup level, representing the finest vegetation classification available across the region, as well as the highest-resolution Area of Habitat (AOH) maps currently available. By combining these high-resolution IVC macrogroup maps with detailed AOH maps, we highlight at-risk vegetation types based on 1) threatened and macrogroup-associated species (species that have at least 50% of their AOH in one macrogroup), 2) current protection levels, and 3) projected threats from land use changes, and 4) develop a conservation value index (CVI) that accounts for all these factors. The results highlighted the remarkable diversity of high conservation value macrogroups across the Americas, emphasizing their significance in regions such as the Andes, montane Mesoamerica, the Caribbean, Brazil's Cerrado, and the Atlantic Forest. Among the highest-scoring macrogroups, the Northern Andean Montane & Upper Montane Humid Forest emerged as critically important, harboring a high number of threatened and macrogroup-associated species. Other macrogroups of immediate conservation concern include the Brazilian Atlantic Montane Humid Forest, Pacific Mesoamerican Seasonal Dry Forest, Caribbean Lowland Humid Forest, and Central Midwest Oak Forest, Woodland and Savanna. However, the study revealed that nearly three-quarters of the over 300 macrogroups in the Americas fall below the global target of 30% protection. Notably, a fifth of all species were macrogroup-associated species, including over 40% of threatened species. Our findings emphasize the need for targeted conservation strategies that consider finer-scale habitat classifications and paired with high-quality species distribution data to guide conservation strategies for biodiversity across the Americas.

Las Américas contienen ecosistemas altamente biodiversos pero vulnerables, con muchas especies amenazadas insuficientemente protegidas. Las evaluaciones de hábitat hechas a escala más detallada son cruciales para una planificación eficaz de la conservación, pero los mapas de vegetación de alta resolución a escala continental siguen siendo limitados. Este estudio aborda esta brecha, identificando tipos de vegetación críticos en las Américas utilizando el marco estandarizado del sistema de Clasificación Internacional de Vegetación (IVC) a nivel de macrogrupo, que representa la clasificación de vegetación más detallada disponible en toda la región, así como los mapas de Área de Hábitat (AOH) de mayor resolución disponibles en la actualidad Al combinar estos mapas de IVC de alta resolución con mapas de AOH, determinamos los tipos de vegetación en riesgo basados ​​en 1) especies amenazadas y asociadas a macrogrupos (especies que tienen al menos el 50% de su AOH en un macrogrupo), 2) niveles de protección de las especies, y 3) amenazas proyectadas por cambios en el uso del suelo , y 4) desarrollar un índice de valor de conservación (CVI) que tenga en cuenta los tres factores anteriores. Los resultados demuestran la notable diversidad de macrogrupos de alto valor de conservación en las Américas, enfatizando su importancia en regiones como los Andes, la Mesoamérica montañosa, el Caribe, el Cerrado de Brasil y el Bosque Atlántico. Entre los macrogrupos con mayor puntuación, el Bosque Húmedo Montano Norte Andino y Alto Montano emergieron como de importancia crítica, ya que albergan un gran número de especies amenazadas y asociadas a macrogrupos. Otros macrogrupos de interés para la conservación incluyen el Bosque Húmedo Montano Atlántico de Brasil, el Bosque Seco Estacional Mesoamericano del Pacífico, el Bosque Húmedo de Tierras Bajas del Caribe y el Bosque de Robles, Bosques y Sabanas del Medio Oeste Central. Sin embargo, el estudio reveló que casi tres cuartas partes de los más de 300 macrogrupos en las Américas se encuentran por debajo del objetivo global del 30% de protección. En particular, una quinta parte de todas las especies estaban asociadas a un único   macrogrupo, incluidas más del 40% de especies amenazadas. Nuestros hallazgos enfatizan la necesidad de estrategias de conservación específicas que consideren clasificaciones de hábitats a escala detallada y se combinen con datos de distribución de especies de alta resolución para guiar las estrategias de conservación de la biodiversidad en las Américas.

As Américas abrigam ecossistemas altamente biodiversos, porém vulneráveis, com muitas espécies ameaçadas insuficientemente protegidas. Avaliações de habitat em escalas mais detalhadas são cruciais para um planejamento eficaz de conservação, mas mapas de vegetação de alta resolução em escala continental ainda são limitados. Este estudo aborda essa lacuna, identificando tipos críticos de vegetação nas Américas usando o quadro padronizado do sistema de Classificação Internacional de Vegetação (IVC) no nível de macrogrupo, que representa a classificação de vegetação mais detalhada disponível em toda a região, bem como os mapas de Área de Hábitat (AOH) de maior resolução disponíveis atualmente. Ao combinar esses mapas de IVC de alta resolução com mapas de AOH, determinamos os tipos de vegetação em risco com base em 1) espécies ameaçadas e associadas a macrogrupos (espécies que têm pelo menos 50% de sua AOH em um macrogrupo), 2) níveis de proteção das espécies, e 3) ameaças projetadas por mudanças no uso do solo, e 4) desenvolvemos um índice de valor de conservação (CVI) que leva em consideração os três fatores anteriores. Os resultados demonstram a notável diversidade de macrogrupos de alto valor de conservação nas Américas, enfatizando sua importância em regiões como os Andes, a Mesoamérica montanhosa, o Caribe, o Cerrado do Brasil e a Mata Atlântica. Entre os macrogrupos com maior pontuação, o Bosque Úmido Montano Norte Andino e Alto Montano emergiram como de importância crítica, abrigando um grande número de espécies ameaçadas e associadas a macrogrupos. Outros macrogrupos de interesse para a conservação incluem o Bosque Úmido Montano Atlântico do Brasil, o Bosque Seco Estacional Mesoamericano do Pacífico, o Bosque Úmido de Terras Baixas do Caribe e o Bosque de Carvalhos, Florestas e Savanas do Centro-Oeste Central. No entanto, o estudo revelou que quase três quartos dos mais de 300 macrogrupos nas Américas estão abaixo da meta global de 30% de proteção. Em particular, um quinto de todas as espécies estava associado a um único macrogrupo, incluindo mais de 40% das espécies ameaçadas. Nossas descobertas enfatizam a necessidade de estratégias de conservação específicas que considerem classificações de habitats em escala detalhada e se combinem com dados de distribuição de espécies de alta resolução para orientar as estratégias de conservação da biodiversidade nas Américas.

Geospatial modelling of dry season habitats of the malaria vector, Anopheles funestus, in south-eastern Tanzania.

BACKGROUND: Anopheles funestus is a major malaria vector in Eastern and Southern Africa and is currently the dominant malaria-transmitting vector in many parts of Tanzania. Previous research has identified its preference for specific aquatic habitats, especially those that persist in dry months. This observation suggests the potential for targeted control through precise habitat mapping and characterization. In this study, we investigated the influence of habitat characteristics, land cover and human population densities on An. funestus distribution during dry seasons. Based on the results, we developed a habitat suitability model for this vector species in south-eastern Tanzania. METHODS: Eighteen villages in south-eastern Tanzania were surveyed during the dry season from September-December 2021. Water bodies were systematically inspected for mosquito larvae and characterized by their physico-chemical characteristics and surrounding environmental features. A generalized linear model was used to assess the presence of An. funestus larvae as a function of the physico-chemical characteristics, land use and human population densities. The results obtained from this model were used to generate spatially explicit predictions of habitat suitability in the study districts. RESULTS: Of the 1466 aquatic habitats surveyed, 440 were positive for An. funestus, with river streams having the highest positivity (74%; n = 322) followed by ground pools (15%; n = 67). The final model had an 83% accuracy in predicting positive An. funestus habitats, with the most important characteristics being permanent waters, clear waters with or without vegetation or movement and shading over the habitats. There was also a positive association of An. funestus presence with forested areas and a negative association with built-up areas. Human population densities had no influence on An. funestus distribution. CONCLUSIONS: The results of this study underscore the crucial role of both the specific habitat characteristics and key environmental factors, notably land cover, in the distribution of An. funestus. In this study area, An. funestus predominantly inhabits river streams and ground pools, with a preference for clear, perennial waters with shading. The strong positive association with more pristine environments with tree covers and the negative association with built-up areas underscore the importance of ecological transitions in vector distribution and malaria transmission risk. Such spatially explicit predictions could enable more precise interventions, particularly larval source management, to accelerate malaria control.

Quantifying the risk of plastic ingestion by ichthyofauna in the Balearic Islands (western Mediterranean Sea).

This study investigates the risk plastic debris ingestion poses to coastal marine taxa in the Balearic Islands in the western Mediterranean Sea. Here, we use species observations and environmental data to model habitat maps for 42 species of fish. For each species, we then match estimates of habitat suitability against the spatial distribution of plastic debris to quantify plastic exposure, which we further combine with species-wise ingestion rates to map the risk of plastic ingestion. The results indicate that the risk of plastic ingestion is particularly high in the north-west and south-east regions and the risks varied strongly between species, with those at higher trophic levels being the most vulnerable overall. Extending this work to other coastal regions within the Mediterranean Sea and beyond will allow managers and policymakers to target the most appropriate areas and types of interventions for mitigating plastic pollution on coastal diversity in the marine environment.

Natural stocks of Pinctada margaritifera pearl oysters in Tuamotu and Gambier lagoons: New assessments, temporal evolutions, and consequences for the French Polynesia pearl farming industry.

Knowledge of the status of the black-lipped pearl oyster Pinctada margaritifera populations is critical for the sustainability of the French Polynesia black pearl farming industry. Indeed, this activity relies on collection of spat settling out of the water column, which is inherently related to the abundance of in situ benthic stocks and their reproductive capacities. From surveys performed between 2016 and 2021, we present new stock assessments of natural wild oyster populations from four contrasted pearl farming lagoons (Gambier, Takapoto, Raroia, Takume). Also using Ahe atoll historical data, we first highlight how results vary with the methods (Direct, Zonal and Cokriging) used to scale in situ density measurements to lagoon-scale stocks. Takapoto and Gambier populations were also previously surveyed at least twice since the 1980s, allowing to demonstrate with field data clear changes in stock distributions and population structures. The consequences of our findings are discussed to provide recommendations for stock monitoring and management in the future.

Chiropteran chatter in Chautauqua, NY (USA): Using acoustic sampling and geographic information systems to create a baseline bat habitat dataset.

Understanding bat habitat use and how bat activity changes in response to differing habitats across time and space is critical in developing and implementing effective bat conservation actions. To investigate the utility of geographic information systems (GIS) in studying bat habitat interactions, habitat delineations and bioacoustic sampling were conducted along two transects in Chautauqua County, NY (USA) from mid-May until the end of August 2013. Surveys were vehicular, and driven between 29 and 32 kmph in order to match bats' flying speed. They were conducted starting 30 min after sunset on nights where the temperature was greater than 13 °C. In total, twenty surveys were completed, and 1248 bat calls were identified to species. Mixed models regression analysis revealed significant interactions among all of the species of bat analyzed in the model. The model was supported with a secondary analysis comparing bat call density with land cover. This study supports the hypothesis that bats forage in different habitats at the species level and indicates the importance of forested areas to bats. Additionally, the methodology for this study has the potential to gather large data sets in a short period of time, while collecting data on several species of bat at once and has been shown to be useful in identifying important habitat features for bats using bioacoustics and geospatial analysis. Since the data has been collected following state guidelines, the dataset and its analysis establish a baseline for future data collection campaigns and in performing a similar analysis for other regions within the state of New York or areas worldwide.

Nation-wide hierarchical and spatially-explicit framework to characterize seagrass meadows in New-Caledonia, and its potential application to the Indo-Pacific.

Despite their ecological role and multiple contributions to human societies, the distribution of Indo-Pacific seagrasses remains poorly known in many places. Herein, we outline a hierarchical spatially-explicit assessment framework to derive nation-wide synoptic knowledge of the distribution of seagrass species and communities. We applied the framework to New Caledonia (southwest Pacific Ocean) and its 36,200 km2 of reefs and lagoons. The framework is primarily field-based but can leverage various habitat maps derived from remote sensing. Field data collection can be stratified by map products and retrospectively contribute to developing new seagrass distribution maps. Airborne and satellite remote sensing alone do not allow for the spatial generalisation of the finest attributes (species distribution and types of seagrass beds), but staged stratified field sampling provides synoptic views of these attributes. Using three examples, we discuss how the hierarchical and spatial information generated from this framework's application can inform conservation and management objectives.

Benthic habitat mapping of Plazh Gradina - Zlatna ribka (Black Sea) and Karpathos and Saria Islands (Mediterranean Sea).

BACKGROUND: Habitat mapping is nеcessary for the efficient conservation and protection of marine ecosystems. In addition, it is a requirement for EU Member States as stated in the European Union (EU) Habitats Directive (92/43/EEC), as well as necessary for the achievement and maintenance of 'good environmental status (GES)' of benthic marine habitats in the framework of the EU Marine Strategy Framework Directive (2008/56/EC). NEW INFORMATION: This study provides baseline information on the marine benthic habitats of Sozopol Bay (Black Sea) and Karpathos and Saria Islands (Mediterranean Sea). These two Natura 2000 sites were selected as study sites of the RECONNECT project, which aimed at creating a transnational cooperative network to confront the environmental threats of ecosystems with a high natural and cultural interest, by the establishment of common practices and a joint regional strategy. The specific objective was to map the marine habitats using a defined a priori classification (EUNIS), with the ultimate purpose of supporting government marine spatial planning, management and decision-making processes through the development of a Decision Support System.

Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea.

Benthic habitat mapping is a rapidly growing field of underwater remote sensing studies. This study provides the first insight for high-resolution hydroacoustic surveys in the Slupsk Bank Natura 2000 site, one of the most valuable sites in the Polish Exclusive Zone of the Southern Baltic. This study developed a quick and transparent, automatic classification workflow based on multibeam echosounder and side-scan sonar surveys to classify benthic habitats in eight study sites within the Slupsk Bank. Different predictor variables, four supervised classifiers, and the generalisation approach, improving the accuracy of the developed model were evaluated. The results suggested a very high significance for the classification performance of specific geomorphometric features that were not used in benthic habitat mapping before. These include, e.g., Fuzzy Landform Element Classification, Multiresolution Index of the Valley Bottom Flatness, and Multiresolution Index of the Ridge Top Flatness. Comparison of classification results with manual maps demonstrated that Random Forest had the highest performance of four tested supervised classifiers. Because the current needs include benthic habitat mapping for the whole area of the Polish Exclusive Economic Zone, the key findings of this study may be further applied to extensive areas in the Polish waters and other vast areas worldwide.

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species Eucalyptus forests.

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high-resolution multispectral imagery, collected by a lightweight and low-cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand-scale from leaf-scale chemistry measurements across a gradient of mixed-species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50-1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross-validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.

A high-resolution remotely sensed benthic habitat map of the Qatari coastal zone.

A comprehensive, high resolution, ground truthed benthic habitat map has been completed for Qatar's coastal zone and Halul Island. The objectives of this research were to; 1. Systematically compare and contrast pixel- and object-based classifiers for benthic mapping in a limited focus area and then to, 2. Apply these learnings to develop an accurate high resolution benthic habitat map for the entirety of the Qatari coastal zone. Results indicate object-based methods proved more efficient and accurate when compared to pixel based classifiers. The developed country-wide map covers 4500 km2 and underscores the complex interplay of seagrass, macroalgal, and reefal habitats, as well as areas of expansive mangrove forests and microbial mats. The map developed here is a first of its kind in the region. Many potential applications exist for the datasets collected to provide fundamental information that can be used for ecosystem-based management decision making.

Underwater Hyperspectral Imaging Technology and Its Applications for Detecting and Mapping the Seafloor: A Review.

Common methods of ocean remote sensing and seafloor surveying are mainly carried out by airborne and spaceborne hyperspectral imagers. However, the water column hinders the propagation of sunlight to deeper areas, thus limiting the scope of observation. As an emerging technology, underwater hyperspectral imaging (UHI) is an extension of hyperspectral imaging technology in air conditions, and is undergoing rapid development for applications in shallow and deep-sea environments. It is a close-range, high-resolution approach for detecting and mapping the seafloor. In this paper, we focus on the concepts of UHI technology, covering imaging systems and the correction methods of eliminating the water column's influence. The current applications of UHI, such as deep-sea mineral exploration, benthic habitat mapping, and underwater archaeology, are highlighted to show the potential of this technology. This review can provide an introduction and overview for those working in the field and offer a reference for those searching for literature on UHI technology.

Applying an integrated approach to coastal marine habitat mapping in the north-western United Arab Emirates.

Habitat mapping is essential for the management and conservation of coastal marine habitats. However, accurate and up-to-date habitat maps are rarely available for the marine realm. In this study, we mapped the coastal marine habitats of >400 km of coastline in the north-western United Arab Emirates (UAE) using a combination of data sources including remote sensing, extensive ground-truthing points, local expert knowledge and existing information. We delineated 17 habitats, including critical habitats for marine biodiversity such as coral reefs and mangroves, and previously unreported oyster beds and deep seagrasses. This innovative approach was able to produce a coastal marine habitat map with an overall accuracy of 77%. The approach allowed for the production of a spatial tool well-suited for the needs of environmental management and conservation in a previously data-deficient area of the United Arab Emirates.

Discovery of widely available abyssal rock patches reveals overlooked habitat type and prompts rethinking deep-sea biodiversity.

Habitat heterogeneity and species diversity are often linked. On the deep seafloor, sediment variability and hard-substrate availability influence geographic patterns of species richness and turnover. The assumption of a generally homogeneous, sedimented abyssal seafloor is at odds with the fact that the faunal diversity in some abyssal regions exceeds that of shallow-water environments. Here we show, using a ground-truthed analysis of multibeam sonar data, that the deep seafloor may be much rockier than previously assumed. A combination of bathymetry data, ruggedness, and backscatter from a trans-Atlantic corridor along the Vema Fracture Zone, covering crustal ages from 0 to 100 Ma, show rock exposures occurring at all crustal ages. Extrapolating to the whole Atlantic, over 260,000 km2 of rock habitats potentially occur along Atlantic fracture zones alone, significantly increasing our knowledge about abyssal habitat heterogeneity. This implies that sampling campaigns need to be considerably more sophisticated than at present to capture the full deep-sea habitat heterogeneity and biodiversity.

Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea.

Hyperspectral imagers enable the collection of high-resolution spectral images exploitable for the supervised classification of habitats and objects of interest (OOI). Although this is a well-established technology for the study of subaerial environments, Ecotone AS has developed an underwater hyperspectral imager (UHI) system to explore the properties of the seafloor. The aim of the project is to evaluate the potential of this instrument for mapping and monitoring benthic habitats in shallow and deep-water environments. For the first time, we tested this system at two sites in the Southern Adriatic Sea (Mediterranean Sea): the cold-water coral (CWC) habitat in the Bari Canyon and the Coralligenous habitat off Brindisi. We created a spectral library for each site, considering the different substrates and the main OOI reaching, where possible, the lower taxonomic rank. We applied the spectral angle mapper (SAM) supervised classification to map the areal extent of the Coralligenous and to recognize the major CWC habitat-formers. Despite some technical problems, the first results demonstrate the suitability of the UHI camera for habitat mapping and seabed monitoring, through the achievement of quantifiable and repeatable classifications.

The value of information: Realising the economic benefits of mapping seagrass meadows in the British Virgin Islands.

Carbon capturing coastal and marine habitats around the world are decreasing in extent every year, habitats found in abundance in Small Island Developing States' territories. However, these habitats are under threat by increased levels of economic activities and extreme weather events. Consequently, as those ecosystems become scarce their value is expected to increase. In this paper the "value of information", the increase in knowledge that renders a system or a function more valuable, from marine habitat mapping is presented through the (monetary) valuation of one regulating service provided by the newly mapped habitats. Mapping a section of a channel with a multibeam echosounder revealed more seagrass resources than in previous studies. Using values for both the Social Cost of Carbon and Abatement Cost methods, from the literature we estimate the value of the carbon sequestration and storage service these seagrass meadows provide. The impacts of hurricanes in the newly mapped seagrasses were also investigated. Despite the costs of mapping, monitoring and of projected losses of ecosystem services provision due to hurricanes, net benefits over a time period of 50 years were considerably larger. The new information provided highlights carbon capturing habitats as more important, enabling the "value of information" to inform policymaking.

Advances in assessing Sabellaria spinulosa reefs for ongoing monitoring.

Standardized and repeatable data acquisition and analyses are required to enable the mapping and condition monitoring of reefs within Marine Protected Areas (MPAs). Changes in habitat condition must be reliably identified and reported to best support evidence-based management. Biogenic reefs in temperate waters, that is, hard matter created by living organisms and raised above the seabed, provide food and shelter for many plant and animal species. This article explores the feasibility of habitat mapping, using remote sensing datasets, as well as metrics for repeatable and suitable assessment of areas of Sabellaria spinulosa for their status as biogenic reef. Data were gathered within the North Norfolk Sandbanks and Saturn Reef candidate Special Area of Conservation/Site of Community Importance in the southern North Sea. Six study areas were identified as potential locations of biogenic reef using previously acquired data, and these were targeted for further investigation using a combination of high resolution multibeam echosounder and sidescan sonar. Where potential S. spinulosa was identified from the acoustic data, a drop-down camera system was employed for visual verification. Areas of known and potential S. spinulosa reef were mapped successfully at two of the six study areas, although future approaches should take careful consideration of the seabed morphology and predominant habitat backdrop to successfully interpret such data. Camera tows from S. spinulosa reef areas were broken up into 5-s segments, with each segment scored for (a) average tube elevation; (b) average percentage cover; and (c) for the presence or absence of S. spinulosa. These metrics were utilized to create summary statistics, including a value of patchiness derived from presence/absence data, that is recommended for application as part of future monitoring programs. The application of this methodology could benefit wider assessments of similar threated or declining habitats such as intertidal Mytilus edulis beds on mixed and sandy sediments, Maerl beds, Modioulus modiolus beds, Ostrea edulis beds, and Zostera beds where patchiness may also be considered of environmental importance.

The influence of landscape's dynamics on the Oriental Migratory Locust habitat change based on the time-series satellite data.

Landscape structure and vegetation coverage are important habitat conditions for Oriental Migratory Locust infestation in East Asia. Characterizing the landscape's dynamics of locust habitat is meaningful for reducing the occupation of locusts and limiting potential risks. To better understand causes and consequences of landscape pattern and locust habitat, it is not enough to simply detect locust habitat of each year. Rather, landcover transitions causing the change of locust habitat area must also be explored. This paper proposes an integrated implement to quantify the influence of landscape's dynamics on locust habitat changes based on three tenets: 1) temporal context can provide insight into the land cover transitions, 2) the detection of locust habitat area is operated on patches rather than pixels with full consideration of landscape's ecology, 3) the modeling must be flexible and unsupervised. These ideas have not been previously explored in demonstrating the possible role of changes in landscape characteristics to drive locust habitat transitions. The case study focuses on the Dagang district, a hot spot of locust infestation of China, from 2000 to 2015. Firstly, the seasonal characteristics of typical landcovers in NDVI, TVI, and LST were extracted from fused Landsat-MODIS surface reflectance imagery. Subsequently, a landscape membership-based random forest (LMRF) algorithm was proposed to quantify the landscape structure and hydrological regimen of locust habitat at the patch level. Finally, we investigated the correlations between the specific landcover transitions and habitat changes. Within the 16 years observations, our findings suggest that the sparse reeds and weeds in the vicinity of beach land, riverbanks, and wetlands are the dominant landscape structure associated with locust habitat change (R2 > 0.68), and the fluctuation in the water level is a key ecological factor to facilitate the locust habitat change (R2 > 0.61). These results are instrumental for developing precision pesticide use to reduce environmental degradation, and providing positive perspectives for ecological management and transformation of locust habitats.