Habitat Classification Research Articles

Exceptionally Uniform Bat Assemblages across Different Forest Habitats Are Dominated by Single Hyperabundant Generalist Species

Woodland bat assemblages are usually structured in a space according to the distance from the ground, water, and obstacles, features that often define chiropteran hunting tactics. Consequently, the bat species composition differs strongly among various habitats, even within the same forest patch. However, when conducting local bat surveys in Wolin National Park (WPN), we revealed an unexpected uniformity in the qualitative and quantitative structure of bat assemblages, based on mist netting and ultrasound recording. In total, 10 vespertilionid species were detected. Across all methods and sampled habitats, a single species, Pipistrellus pygmaeus, predominated, while no Barbastella barbastellus, an old forest specialist, were detected, despite the abundance of their preferred daily roosts. We also reviewed the literature for mist-netted bat samples in four different habitats in lowland Polish forests. The samples usually clustered based on habitats, and the same habitat classes often clustered very closely despite representing geographically distant forests. The exception was WPN, where all four habitat classes formed a tightly packed cluster. We hypothesize that P. pygmaeus might act as a hyperabundant native species, a successful generalist that reduces the contribution of more specialized taxa in the assemblage. It probably benefits from both forest renaturation and anthropogenic cross-boundary subsidy.

Planktonic habitats in the Amazon Plume region of the Western Tropical North Atlantic

The Western Tropical North Atlantic is a highly dynamic marine system where the Amazon River Plume (ARP) generates a patchwork of environmental conditions that favor different phytoplankton groups. To study phytoplanktonic community structure in such heterogeneous conditions, we used a set of five standard ship-based measurements taken from oceanographic surveys between 2010 and 2021 to characterize different habitat types. We then utilized a variety of multiparametric approaches to examine phytoplankton biodiversity in the different habitats to assess the biological relevance of our delineated habitats. Our approach generated a consistent set of habitat types across cruises carried out in multiple different years and the Amazon’s two predominant (wet and dry) seasons. Our phytoplankton community analyses revealed strong distinctions among all habitats along the plume gradient using in-vivo fluorescence and diagnostic pigments, and clear contrasts of diazotroph community along the mesohaline waters using direct cell-count, a pattern consistent with niche partitioning among similar species. The few apparent mismatches we found between phytoplankton community composition and habitat may reflect recent hydrographic changes driven by mixing and/or upwelling and thus may be a useful index to biologically-relevant temporal variation. Our habitat classification approach is straightforward and broadly applicable in identifying biologically distinct areas within heterogeneous and dynamic regions of the ocean.

The Role of Vegetation Monitoring in the Conservation of Coastal Habitats N2000: A Case Study of a Wetland Area in Southeast Sicily (Italy)

The coastal wetlands are among the most vulnerable and threatened environments of the Mediterranean area. Targeted actions for their conservation require in-depth knowledge of current and past natural vegetation. In this paper, we surveyed the vegetation composition and the spatio-temporal changes of a coastal wetland area in southeastern Sicily (“Saline di Priolo” SAC). Based on 128 phytosociological surveys and several plant collections, a total of 304 taxa, 28 plant communities, and 16 habitats have been identified. Furthermore, three new plant associations were described, including two in wetland and one in rocky coast environments. For the classification of plant communities and habitats, a hierarchical clustering was performed by using Euclidean coefficient and beta-flexible algorithm. The life form spectrum of the current flora highlights the dominance of therophytes and hemicryptophytes. The Mediterranean species are largely prevailing with 123 taxa. The cartographic analysis performed with ArcGis 10.3 shows a radical reduction in the wetland habitats in the last 70 years, and a strong alteration of the ecological succession of the psammophilous-hygrophilous vegetation. Moreover, landscape configuration of the coastal vegetation and habitat types was well highlighted by a set of specific landscape metrics. In particular, our outcomes identify three habitats (2110, 2210, and 5220* EU code) with bad conservation status, among which we identified one of priority conservation (Zyziphus arborescent matorral) that requires urgent restoration measures.

Functional properties of planktic microalgae determine their habitat selection

In this study, we aim to investigate how the functional properties of microalgae help to delineate the major groups of aquatic habitats. Using functional trait-based and Reynolds’ functional group-based approaches similarities of the microalgal flora of all aquatic habitats occurring in Hungary were compared. The habitats covered the whole size spectrum of standing waters (10-2–108 m2) and water currents (watershed: 102–1011 m2), , limnological and chemical properties. Both functional trait-based and functional group-based habitat classifications overrode the hydromorphology-based typology, however, functional group-based clusters showed closer resemblance to limnological-hydromorphological types than clusters created by trait-based approaches both for qualitative and quantitative data. Most of the aquatic habitats that have similar limnological characteristics showed resemblance in the functional properties of their microflora. Rivers and river-related habitats were the most diverse functionally. These were followed by standing waters with extended macrophyte coverage. The small, unique habitats displayed the lowest functional richness. The occurrence of several functional groups in some extreme habitats implies two alternative explanations: first, the habitat template of the groups is wider than defined in the original description; second, detailed information on the autecology of species assigned to a functional group necessitates the creation of new groups specific for the unique habitats.

Benthic Habitat Classification Using Sentinel-2a Image With And Without Water Column Correction In Pengudang Village, Bintan Regency

Research on the mapping of benthic habitats in the waters of Pengudang Village, Bintan Regency, uses Sentinel-2A imagery. This mapping process has the potential to display the results of classification of benthic habitats in shallow waters. The purpose of this study was to assess the Maximum Likelihood Classification (MLH) algorithm model for mapping benthic habitat cover through Sentinel-2A satellite imagery using the lyzenga water column correction and without water column correction and to calculate the accuracy of the MLH classification results for benthic habitats in waters Pengudang Village. This classification uses a pixel-based image classification which is grouped into a cover class for each pixel. This classification groups into 4 benthic habitat classes, namely dead coral sand (KMP), sea grass (LM), live coral sand (PKH) and sand (PS). The results showed that the application of the lyzenga correction using the MLH algorithm in mapping benthic habitats resulted in a significant accuracy rate (80,00%), while the application without lyzenga correction using the MLH algorithm resulted in an accuracy rate (76,00%). These findings support the use of the lyzenga correction method which can increase the level of accuracy by (4,00%) with the MLH algorithm as a guide in better mapping of benthic habitat cover in Pengudang Village.

Mapping the distribution of sea urchin (Echinoidea) and benthic habitat using drone in the waters of Lancang Island

Sea urchins are valuable marine creatures that live in the waters of Indonesia, and typically found in seagrass and coral reefs. This study aimed to determine the distribution of sea urchins and the benthic characteristics that serve as their habitat using high resolution drone images. Object-based image analysis (OBIA) with a support vector machine algorithm is used to identify benthic habitats, while the distribution of sea urchins is determined by thresholding pixel values. A total of 812 aerial photos with a spatial resolution of 3.98 cm/px were obtained and mapped into five classes of benthic habitat (sand, rubble, seagrass, living coral, and dead coral algae). The dominant habitat class obtained was dead coral algae, with an area of 234,035 m2 (29.63%), whereas seagrass was the smallest, with an area of 74,149 m2 (9.39%). The overall accuracy of the benthic classes was 65.44%, with kappa coefficient value of 0.57. The study found that the total number of sea urchins on Lancang Island was 61,551. The dominant distribution of sea urchins was found in the living coral class with 18,094 individuals, but the highest density was in the rubble class with 1358 individuals/ha.

Functional responses in American marten habitat selection indicate cumulative effects of progressive habitat change

AbstractShifts in habitat selection may serve as behavioral indicators of changing habitat quality and can provide an insight into the effects of chronic disturbance on wildlife populations before rates of occurrence and other metrics related to population viability are affected. Long‐term studies can provide unique opportunities to understand animals' behavioral responses to the cumulative effects of habitat disturbance that may be missed by short‐term studies or space‐for‐time substitution. Using the American marten (Martes americana) as a focal species, we evaluated the effects of progressive habitat disturbance on an animal population by identifying changes in habitat availability and selection over time. Marten are strongly associated with mature forest conditions and are thus sensitive to forest harvesting, which is a chronic and prevalent form of disturbance across the species' range. We developed resource selection functions that characterized both temporally consistent patterns and functional responses in the relative selection of habitats by marten at the patch scale. We used a combination of marten location data collected during three periods over 30 years of extensive forest change in northern Maine and a habitat classification scheme informed by species' behavior and designed to capture habitat changes over time. Marten increased their selection of tall (>12 m mean tree height) mature forest, including uncut forest and regenerated clearcuts, in response to decreased availability. Marten also increased avoidance of early‐successional forest (<9 m mean tree height) in response to increased availability. These functional responses in habitat selection appear to enable marten to maintain access to required resources in mature forest and limit exposure to risky or resource‐poor habitat within home ranges. However, a declining trend in the occurrence of resident adult females may indicate a limit to the functional response strategies for individuals unable to incur the energetic or prey‐related costs that may be associated with strong functional responses. Our results suggest that, despite adaptive habitat selection strategies, there are minimum thresholds of mature forest availability below which resident adults, particularly females, will struggle to persist. We recommend forest management strategies targeted at minimizing the strength of functional responses displayed by marten within occupied landscapes.

Estimating conservation value and natural capital value of land cover classes in the Irish National Land Cover Map and application to a case study area

Conservation science and planning, by measuring proxies of biodiversity and ecosystem services provision, aim to identify priority areas for nature conservation and ecosystem services. In Ireland, fine-scale data on ecosystems functioning and biodiversity are limited, making it challenging to map conservation value (CV) and natural capital value (NCV) accurately. We elicited expert knowledge to rank habitat classes mapped in the recently published National Land Cover Map (NLCM) (EPA and Tailte Éireann, 2023). A scoring system from 0 to 10 was used to score habitats based on their estimated provision of biodiversity (CV) and ecosystem services (NCV). As a case study, we applied this scoring system to a catchment in the south-east of Ireland (>2,000 km2) with land cover information available from the draft NLCM. The expert elicitation showed little overall difference between the scores assigned by the team and the experts invited to validate the CV and NCV scores. However, some scores were revised based on experts’ contributions. Results of the mapping exercise indicated a high correlation between monads with high CV and high NCV scores. Future work should focus on differentiating the weighting assigned to each ecosystem service associated with each land cover class. This could result in changes in the overall NCV scores assigned to each habitat (and monads). Nevertheless, the approach developed here has the potential to identify areas in the landscape that should be targeted for conservation. For reproducibility, we provide the R code for analysis at polygon scale.

Towards interpretable learned representations for ecoacoustics using variational auto-encoding

Ecoacoustics is an emerging science that seeks to understand the role of sound in ecological processes. Passive acoustic monitoring is being used to collect vast quantities of soundscape audio recordings to study variations in acoustic community and monitor biodiversity. However, extracting relevant information from soundscape recordings is non-trivial. Recent approaches to machine-learned acoustic features appear promising but are limited by at least three issues: inductive biases, lack of interpretability and crude temporal integration. In this paper we introduce a novel self-supervised representation learning algorithm for ecoacoustics - a convolutional Variational Auto-encoder (VAE) - and directly address these shortcomings. Firstly, we train the network on soundscape recordings from temperate and tropical field sites along a gradient of ecological degradation to provide a more relevant inductive bias than prior approaches. Secondly, we present a new method that allows interpretation of the latent space for the first time, giving insight into the basis of classification. Thirdly, we advance existing methods for temporal aggregation of learned embeddings by encoding latent features as a distribution over time. Under our approach to increase interpretability, we provide insight into how learned features drive habitat classification for the first time: inspection of latent space confirms that varying combinations of biophony, geophony and anthrophony are used to infer sites along a degradation gradient. Our novel temporal encoding method increases sensitivity to periodic signals and improves on previous research that uses time-averaged representations for site classification. This approach also reveals the contribution of hardware-specific frequency response that create a potential bias; we demonstrate how a simple linear transformation can be used to mitigate the effect of hardware variance on the learned representation under our approach. Our novel approach paves the way for development of a new class of deep neural networks that afford more interpretable learned ecoacoustic representations to advance both fundamental and applied science and support global conservation efforts.

Exploring the spatial configuration of tourism resources through ecosystem services and ethnic minority villages

The rampant and unregulated development of natural landscapes has depleted vital environmental resources, degraded ecosystems, and diminished the allure of ecotourism. Identifying natural spaces suitable for tourism development using a holistic approach that evaluates surrounding ecosystem structure and function is therefore imperative to safeguarding ecological integrity and fostering sustainable economic progress. Leveraging the InVEST model and Kernel Density Estimation (KDE), we assessed the spatial distribution of natural landscape resources and the density of ethnic minority villages to identify potential areas suitable for ecotourism development within the fragile Karst Region of Qiandongnan Miao and Dong Autonomous Prefecture (QMDA), southeastern Guizhou Province, China. Results showed that habitat quality in QMDA was relatively high, with high-quality habitat (Class V) covering 19,554.76 km2, accounting for 65% of the total study area. Optimal natural landscape resources gravitated predominantly towards Liping and Rongjiang, collectively occupying 16% and 12% of the total optimal natural landscape resources, respectively. A total of 3439.95 km2 of potential natural resource development space was identified, with a discernible spatial distribution where the western region of the study area surpassed the eastern counterpart, and the southern region exceeded the northern expanse. The potential development space of tourism resources spanned a cumulative area of 1245.84 km2, located predominately in Kaili, and Danzhai, accounting for 26% and 16% of the total potential development space of tourism resources, respectively. To ensure sustainable use of ecological and cultural assets in QMDA, it is recommended that zoning and regulatory measures that encompass both ecological and touristic domains be enacted. Our findings contribute to a scientific foundation for the pursuits of ecological preservation and the administration of tourism planning for QMDA.

Don't worry, be happy: Habitat selection of Blanding's Turtles (Emydoidea blandingii) living in a reference condition in Georgian Bay.

Few areas within the Great Lakes basin are currently free from impact of human activities, and it is important to study these reference conditions for comparison with degraded sites in those regions. Here, we use radio telemetry to investigate habitat use, movement, and habitat selection of a population of the endangered (Federally in Canada) Blanding's turtle (Emydoidea blandingii, BLTU) inhabiting a mostly undisturbed archipelago located at the northern shore of Mnidoo gamii (Georgian Bay), Ontario over two active seasons (May to September 2021 and 2022). We found a mean home range of 16.21 ha for females (n = 7) and 15.10 ha for males (n = 7). Of the five habitat classes (Marsh, Open Water, Rock, Peatland, and Forest), females used all except Peatland during the nesting season, and both sexes used all five habitat classes throughout both active seasons in 2021 and 2022. Disproportionate habitat use was detected at the landscape scale but not at the home range scale which was consistent with the hypothesis that adult Blanding's turtles residing in relatively undisturbed sites with abundant habitat types use all habitat types according to their availability. We also observed the use of open, deep water by Blanding's Turtles as travel corridors for nesting and mating. Effective future conservation strategies should prioritize the protection and connectivity of relatively undisturbed wetlands, forests, and rock barrens in this region and use this study as a reference condition to compare BLTU habitat use and movement across disturbance gradients within Georgian Bay.

Biodiversity conservation in Myanmar's coastal wetlands: Focusing on saltwater crocodile habitats and connectivity

Landscape-level conservation strategies are needed to protect the saltwater crocodile population and habitats in Myanmar. Identifying the remaining habitats and movement corridors is essential due to population decline across coastal regions and insufficient habitat coverage, even within protected areas. This study predicts the distribution of habitats and creates low-resistance corridors that facilitate movement across the landscape. Occurrence dataset encompassing a 20-year period (1999–2019) was used incorporating records gathered from the Global Biodiversity Information Facility—GBIF, crocodile counting reports from the Meinmahla Kyun wildlife sanctuary of Myanmar, and recent spotlight and camera-trap surveys. Using landscape connectivity tools, structural compositions of habitat classes were determined, and habitat patches were delineated to simulate the least-cost corridor and dispersal pathways. Two patches in Rakhine, two in Ayeyarwady and Yangon, one in Mon, and two in Tanintharyi, were identified that include 1247 km2 of core suitable habitat areas. The Ayeyarwady Delta exhibits a plethora of suitable habitats, while the Rakhine and Tanintharyi regions have higher marginal habitats that are largely unprotected. Only 12 % of the extent of occurrence of saltwater crocodiles are suitable habitats with a high potential for occupancy. Habitats are highly fragmented and four bottlenecks are identified to assist population connectivity in those fragmented patches. Despite notable challenges in ensuring connectivity to restore populations across the habitat patches, we highlighted connectivity as the foundation for establishing an ecological network of Myanmar's coastal habitats, leveraging the saltwater crocodile as an umbrella species for the region's coastal wetlands, and identifying key areas for enhanced protection.

Optimizing Seagrass Detection in Lampung Selatan: Leveraging Machine Learning Algorithms and Sentinel-2A Imagery

Seagrass beds are one of the coastal ecosystems that play an important role in maintaining the stability of blue carbon. However, high community activities threaten the existence of seagrass beds themselves. South Lampung Regency is one of the areas with considerable seagrass potential but the availability of distribution and density data is still minimal. This research aims to identify and map seagrass density as a first step for seagrass management. The data used in this research is Sentinel 2A multispectral image. Machine learning-based classification methods used are random forest (RF) and support vector machine (SVM) because these algorithms have a good ability to distinguish objects based on their features. This study uses a 2-level classification scheme, where level 1 consists of land, shallow sea, and deep-sea classes. Level 2 is the shallow marine bottom benthic habitat class. The type of seagrass found in this area is Enhalus acoroides. Based on the results of the analysis, low, medium, and high-density classes were obtained with an area of low around 20.12 ha, medium around 34.67 ha and high around 320.12ha with a total area of 374.91ha. RF has a higher overall accuracy of 88.00% while SVM accuracy is 84.00% so it can be concluded that Sentinel 2A images can be used to detect seagrass meadows.

Outbreak Mechanism of Locust Plagues under Dynamic Drought and Flood Environments Based on Time Series Remote Sensing Data: Implication for Identifying Potential High-Risk Locust Areas

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture.

Microbiota and Nutrient Portraits of European Roe Deer (Capreolus capreolus) Rumen Contents in Characteristic Southern German Habitats

Roe deer (Capreolus capreolus) are found in various habitats, from pure forest cultures to agricultural areas and mountains. In adapting to the geographically and seasonally differentiating food supply, they depend, above all, on an adapted microbiome. However, knowledge about the microbiome of wild ruminants still needs to be improved. There are only a few publications for individual species with a low number of samples. This study aims to identify a core microbiota for Bavarian roe deer and present nutrient and microbiota portraits of the individual habitat types. This study investigated the roe deer’s rumen (reticulorumen) content from seven different characteristic Bavarian habitat types. The focus was on the composition of nutrients, fermentation products, and the rumen bacterial community. A total of 311 roe deer samples were analysed, with the most even possible distribution per habitat, season, age class, and gender. Significant differences in nutrient concentrations and microbial composition were identified for the factors habitat, season, and age class. The highest crude protein content (plant protein and microbial) in the rumen was determined in the purely agricultural habitat (AG), the highest value of non-fibre carbohydrates in the alpine mountain forest, and the highest fibre content (neutral detergent fibre, NDF) in the pine forest habitat. Maximum values for fibre content go up to 70% NDF. The proportion of metabolites (ammonia, lactate, total volatile fatty acids) was highest in the Agriculture-Beech-Forest habitat (ABF). Correlations can be identified between adaptations in the microbiota and specific nutrient concentrations, as well as in strong fluctuations in ingested forage. In addition, a core bacterial community comprising five genera could be identified across all habitats, up to 44% of total relative abundance. As with all wild ruminants, many microbial genera remain largely unclassified at various taxonomic levels. This study provides a more in-depth insight into the diversity and complexity of the roe deer rumen microbiota. It highlights the key microorganisms responsible for converting naturally available nutrients of different botanical origins.

The impact of habitat and migration on plumage colour in Cardinalidae

Abstract Plumage coloration arises from a complex concert of selection pressures incorporating regional ecology, the light environment and genomic architecture. Here, we analyse the evolution of coloration in cardinals and grosbeaks (Cardinalidae) from the avian visual perspective and test the relative roles of life-history traits in shaping plumage evolution. We incorporate life-history data from three separate sources to analyse the correlated evolution of plumage coloration and ecological and habitat classifications for males and females. Our results show that males and females evolve under different axes of selection and that correlations with life-history traits differ between the sexes. We find that preferences for semi-open, fragmented habitat are correlated with male, but not female, plumage complexity. We also find that migration, long heralded as a driver of sexual dichromatism, is correlated with reduced plumage complexity in females, but not significantly correlated with male plumage coloration. Finally, our results showcase that user-defined categorical metrics can influence or, potentially, even mislead the interpretation of results, highlighting the need to measure habitat quantitatively rather than with subjective, categorical metrics.

LCM2021 – the UK Land Cover Map 2021

Abstract. Land cover is a key environmental variable, underpinning widespread environmental research and decision making. The UK Centre for Ecology and Hydrology (UKCEH) has provided reliable land cover information since the early 1990s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here, we introduce the latest, annual UK Land Cover Map representing 2021 (LCM2021), and we describe its production and validation. LCM2021 methods replicate those of LCM2017 to LCM2020 with minor deviations in cloud-masking processes and training data sourcing to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover or habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created based on data harvested from historic UKCEH land cover maps. The training data were used to construct a random forest classifier, which yielded classified images. Compiled results were validated against 35 182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10-aggregated-classes level. The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km), and land–parcel or vector products. The data are provided in 21-class (all configurations) and aggregated 10-class (1 km raster products only) versions. All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (EPSG: 27700) and the Northern Ireland (NI) data are in the TM75 Irish Grid (EPSG: 29903). Information on how to access the data is given in the “Data availability” section of the paper.

Shallow sea waters habitat mapping in 2018 and 2022 using Sentinel-2 Imagery with OBIA Method at Kelapa Dua Island, Seribu Island, Jakarta Province, Indonesia

Shallow sea habitat has a very important role as a place to live and shelter for various types of organisms. Mapping this area needs to be done regularly considering the many benefits it has and the tendency for this area to change, one of them is by using remote sensing technology with the object-based image analysis (OBIA) method. OBIA classification includes the process of image segmentation and classification of all segmentation results. In the segmentation process using scale 100 at level 1 and scale 10 at level 2. Furthermore, at the classification process, 6 (six) habitat classes are obtained, namely sand, coral, macroalgae, sea grass, rubble and dead coral algae. The overall accuracy value in 2018 showed a value of 70%, and in 2022, it showed a value of 70.77%. The kappa coefficient value is 0.617 in the 2018 image acquisition and 0.612 in the 2022 image acquisition. The analysis carried out showed that there was a change in each habitat with the habitat that experienced the greatest changes are sand and dead coral algae class.

Mapping reef terrain roughness and benthic habitat complexity in the Pamegaran Island, Kepulauan Seribu National Marine Park

The formation of coral cays in the Kepulauan Seribu Marine Parks, Jakarta Special Capital Region, is the result of patch reef evolution which linked to the geology of sediment accretion, including the ecology of coral growth and competition of benthic community. This study aims to map the level of reef complexity in Pamegaran Island using Benthic Terrain Modeler. Field observation to monitor benthic communities in four stations at 3- and 5- meter depth was combined with SPOT-7 image processing to evaluate in situ rugosity, 3-dimensional bathymetry and benthic habitat classification. The results of this study revealed low degree of terrain complexity as results of habitat degradation and dominant benthic class of sand. Classification of reef benthic habitat resulted in five classes, namely sand (Sd= 126.65 ha), seagrass (Sg= 35.29 ha), bare rubble (Rb= 32.68 ha), reefs dominated by dead corals (DC=3.16 ha) and dead coral with algae (DA= 20.50 ha); with 67.21% score of overall accuracy (OA). In situ rugosity at 5 m was higher than 3 m, as result of higher coverage of live corals particularly in the east site. Similar profile was shown for rugosity index, which reflects ratio between surface to planar area and seafloor gradient along the reef slope. Finally, positive correlation between reef surface and in situ rugosity implies the importance of live coral communities in supporting complex terrain and benthic habitat in the patch reef environment.

Shell shape-habitat correlations in extant turtles: A global-scale analysis

Animal morphological structure can influence behaviour and interaction with the environment. Owing to their wide range of habitats, from aquatic to terrestrial, and their variation in shell shapes, turtles are highly suitable for ecomorphological studies. However, previous studied have encountered several limitations, including small sample sizes, simplified habitat classification, and no consideration of sexual dimorphism, that may prevent the identification of the ecological mechanisms responsible for divergent morphologies of turtles on a global scale. In this study, we collected the carapace length (CL), carapace width (CW), and carapace height (CH), as an indicators of body size, from 6664 adult individuals of 115 species, 61 genera, and 11 families and calculated the ratio between the CH and CW to delineate shell shape. These turtle species were divided into four habitat types (marine, freshwater, mainland, and island) and eight subtypes (freshwater: large lentic, large lotic, small lentic, small lotic, all water bodies; mainland: desert, flatland, highland) to better understand the ecomorphological alteration of their shell shape. The results of the linear regression showed that shell shape became flat with increasing body size in aquatic turtles, whereas the opposite trend was observed for male mainland tortoises. According to the results of the general linear model, there was a gradual increase in the domed shape of turtle shells from sea to freshwater habitats, from freshwater to mainland terrestrial habitats, and from mainland to island terrestrial habitats. There were no significant differences between the shell shape and habitat subtype for freshwater turtles and mainland tortoises. To the best of our knowledge, this study represents the first investigation into the variation pattern of shell shape in turtles living in habitats ranging from marine to island and its relationship to body size. The study results suggest that conservation strategies for turtles should consider both their shell shape and habitat characteristics.