Identification Of Habitats Research Articles

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

Identifying Gaps in the Protection of Mediterranean Seagrass Habitats Using Network‐Based Prioritisation

ABSTRACTAimSeagrass meadows represent a key marine ecosystem owing to the significant biodiversity they host. Protection actions are often implemented without considering connectivity between habitats. In this article, we project and prioritise Mediterranean seagrass habitats (Posidonia oceanica and Cymodocea nodosa) based on their potential as sources/retention and stepping stones for dispersal propagules of the associated biotic communities. We use this information to identify gaps in the protection of highly ranked habitats.LocationMediterranean Sea.MethodsWe related seagrass observations with marine environmental predictors to run species distribution models and infer the distribution of Mediterranean seagrasses. We then used a network‐based approach (CONEFOR) to rank patches of seagrass suitable areas based on their contribution to the seascape in terms of patch area, potential as source/retention of propagules and stepping stone. Finally, by overlaying our ranking with the spatial distribution of marine protected areas (MPAs), we identified potential gaps in the protection of important seagrass habitats across the Mediterranean and its basins.ResultsMost of the identified patches of seagrass suitable areas are not included in MPAs, only reaching a maximum protection coverage of ~50% in the Northwestern Mediterranean. Relatively few patches contribute disproportionately to connectivity, but top‐ranked habitat patches are not included within the existing MPAs network, both at the Mediterranean scale and for most basins. The largest gaps for the source/sink role are in the Aegean and Ionian Sea, and largest gaps for the stepping stone role are in the Adriatic, Ionian and Tyrrhenian Sea.Main ConclusionsOur results suggest that the current MPAs network fails to protect highly relevant patches of seagrass suitable areas in most of the Mediterranean basins. However, this gap could be filled by a few well‐placed MPAs. Overall, we provide novel insights for the identification of key habitats and planning novel coastal MPAs in the region.

Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Context Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas. Aims The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats. Methods We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods. Key results We found that the α-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The α-hull used a single default parameter and required no subjective input, making it a more objective, superior method. Conclusions We recommend that the α-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs. Implications This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped.

Assessing the vulnerability of Elasmobranch species in the Bay of Bengal: Insights from Lakkha gill net fishery of Bangladesh

The elasmobranch population is declining in the Bay of Bengal of Bangladesh due to large-mesh gill net fishing, locally known as the Lakkha net, which primarily targets Indian threadfin (Leptomelanosoma indicum). This study was the first attempt to identify megafaunal bycatch in Lakkha fishing and assess its vulnerability using Productivity Susceptibility Analysis. A total of 40 elasmobranch bycatch species were identified, with sharks comprising 13 species from three families, while 27 rays belonged to six families, with the majority belonging to the Myliobatiformes order (60 %). Productivity and susceptibility scores were assigned to all identified species, with values ranging from 1.27 to 2.73 and 1.50 to 2.63, respectively. The target Lakkha fish exhibited the highest susceptibility score, followed by several pelagic sharks and eagle rays. Vulnerability assessment revealed that 31.7 % (n = 13) of species were highly vulnerable, while 43.9 % (n = 18) were classified as moderate, and 24.4 % (n = 10) were considered to have low vulnerability. All the high-risk megafauna species (n = 13) are classified as threatened by the global IUCN Red List. Sensitivity analysis highlighted susceptibility as a major contributor to species’ vulnerability. Alterations in susceptibility scores led to significant changes in the vulnerability status of many species. The overall data quality assessment indicated moderate data quality across species, with variability observed between productivity (76 % of species received a poor data quality score) and susceptibility attributes. However, vulnerability of these species can be reduced through adequate gear modification, shorter net deployment periods, adoption of safe discharge techniques, identification of critical habitats, and establishment of marine protected areas within this region. This study provides valuable insights into the species composition and vulnerability of elasmobranchs in the Lakkha gill net fishery, emphasizing the need for conservation measures to mitigate bycatch impacts on threatened species.

Identification of Key Habitats of Bowhead and Blue Whales in the OSPAR Area of the North-East Atlantic—A Modelling Approach towards Effective Conservation

Effective protection of highly mobile and data-poor species constitutes a great challenge amid growing ocean exploitation and use. Blue whales and bowhead whales in the North-East Atlantic face several threats, such as climate change, ship strikes, pollution, and entanglement in fishing gear. Consequently, the OSPAR (“Oslo-Paris Convention for the Protection of the Marine Environment of the North-East Atlantic”) Commission recognised their need for protection and included them on the OSPAR List of Threatened and/or Declining Species. Two actions to protect these endangered species that OSPAR Contracting Parties have agreed on are (i) identifying areas that play an essential role in the species’ life cycle—subsequently called key habitats, and (ii) ensuring that those key habitats are covered by OSPAR marine protected areas (MPAs). In addition, OSPAR has committed to expanding its network of MPAs and other effective area-based conservation measures by 2030 to cover at least 30% of the OSPAR maritime area (termed the 30 × 30 target) from today’s approx. 11%. In this paper, we present the results of modelling key habitats for the bowhead and blue whales in the North-East Atlantic, including Arctic waters. Due to the sparse data situation for these species, we apply presence-only modelling methods. As this method can, in principle, produce somewhat biased results, we recommend that systematic, regular surveys on these species in Arctic waters be significantly intensified for future analyses to overcome the data paucity and allow using presence–absence/count data modelling approaches. Key habitats of blue whales were identified between Iceland and Svalbard, extending to the Azores. For bowhead whales, key habitats were identified in the Fram Strait. However, our findings show that only 11.8% of blue whale key habitats and 4.1% of bowhead whale key habitats are currently covered by OSPAR MPAs. To protect 30% of key habitats for these species in the most efficient way, our analyses suggest that suitable areas to designate new OSPAR MPAs are located around the Azores for blue whales and in the Fram Strait for bowhead whales. With these additional MPAs, OSPAR would substantially improve the protection of the two endangered species and, at the same time, progress towards its 30 × 30 target.

Whole genome resequencing reveals the evolutionary history and geographic isolation of the eastern Asian Hickory (Carya)

Societal Impact StatementUnraveling the genetic intricacies and evolutionary history of Asian Hickories through advanced genome sequencing gives valuable insights into their ecological adaptations. Understanding the impact of historical dynamics, climate fluctuations, and geographical barriers on Asian hickories not only contributes to scientific knowledge but also empowers policymakers with evidence‐based decisions and guides conservationists in their efforts to protect vulnerable species. The identification of potential habitats, especially for the endangered Carya kweichowensis, offers a promising avenue for targeted conservation efforts, aligning with global initiatives to preserve Earth's precious biodiversity, but also ensures the conservation of a vital genetic reservoir for nut‐bearing economic tree species.SummaryBiogeographic characteristics of disjunct distribution play a vital role in plant geography and the endangered mechanism. The whole genome resequencing provides an opportunity to study the genetic relationship, population diffusion, and floristic evolution of disjunctive‐distribution flora. Based on the SNPs (single nucleotide polymorphisms) data generated by the whole genome deep resequencing of five EA (Eastern Asian) Carya species, we constructed the phylogenetic tree, genetic structure, and species distribution modeling to clarify the phylogenetic relationships and to predict the potential distribution area of EA Carya. Phylogenetic analysis of Carya revealed two distinct clades, separating EA Carya from NA (North America) Carya. C. kweichowensis, an endangered species, showed the lowest nucleotide diversity and the earliest divergence among studied EA Carya species. Species distribution modeling predicted suitable habitats for five EA Carya species, revealing the potential distribution of endangered C. kweichowensis. Importantly, minimal spatial overlap was observed among distribution regions of EA Carya species during different time periods. The uneven regional distribution of EA Carya is believed to be a consequence of Quaternary climate fluctuations, mountain barriers hindering species dispersal, and the limited cold tolerance of these trees. EA Carya highlights the significant role of climate and geological changes in their regional distribution and migration routes in Asia. Furthermore, the discovery of potential habitats offers a promising avenue for the conservation of C. kweichowensis.

Documentation of a probable spawning run of cisco Coregonus artedi in the Spanish River, Ontario, Canada

Coregonines were historically diverse and abundant in the Laurentian Great Lakes but declined throughout the 19th and 20th centuries due to multiple factors, including overfishing, habitat degradation, and interactions with invasive species. Great Lakes fishery managers are now working to restore coregonines across the basin. An important aspect of restoration efforts is the identification of key habitats, such as spawning habitats. Our understanding of the extent and diversity of coregonine spawning habitats in the Great Lakes is limited. Historical accounts suggest some cisco Coregonus artedi migrated to tributaries to spawn, but contemporary runs have not been reported. Moreover, cisco display considerable phenotypic variation, with different “forms” often occupying specific habitats or regions. We provide evidence for a previously undocumented spawning run of cisco in the Spanish River, a tributary to the North Channel of Lake Huron. We captured 31 mature and ripe fish (25 males, 6 females) approximately 52 km upstream of the river mouth on 15–16 November 2022. The fish ranged in age from 3-9 yr and total length from 287-394 mm. Of these 31 individuals, 27 were morphologically similar to C. artedi manitoulinus, a subspecies of cisco found in the North Channel; however, four individuals more closely resembled cisco captured in other areas of Lake Huron. These findings represent the best evidence of tributary spawning by cisco since the 1880s, and they have important implications for coregonine conservation and restoration efforts. Ongoing work aims to augment our understanding of the ecological and evolutionary importance of this population.

Features of Consistent Powassan Virus Lineage II Focus in Southern Maine, United States.

Powassan virus lineage II or deer tick virus (DTV) is a rare but increasingly reported human infection in the United States transmitted by Ixodes scapularis ticks. The virus is thought to be maintained in environmental foci that are optimal for tick and vertebrate reservoirs, but details on DTV ecology are poorly understood. We investigated DTV tick infection rates and reservoir host abundance in a focus of consistent DTV activity in Maine, USA. Host and tick abundance, vegetation, and microclimate conditions were measured in three forest sites representing increasing invasive understory infestation. Sites were selected representing native understory, mixed vegetation with some invasive Japanese barberry (Berberis thunbergii), and a highly invasive site dominated by Japanese barberry. Japanese barberry in the mixed vegetation site averaged 1 m in height with space between plants, whereas the highly invasive site had impenetrable Japanese barberry over 1.5 m. The DTV infection rate was greater in the highly invasive site. Density of I. scapularis ticks were significantly lower in the native forest site, and no DTV was found. Another feature of the DTV focus was more stable humid microclimate throughout the year compared with the other sites and a nearby continuous section of forest, consistent with reports from Connecticut, USA. We conclude that invasive Japanese barberry stands provide favorable and consistent microclimate conditions to maintain high DTV infection rates annually among questing I. scapularis ticks. Understanding environmental and landscape features that support high infection rates could lead to the identification of high-risk habitats for contracting DTV.

Modeling Proboscis monkey conservation sites on Borneo using ensemble machine learning

This study aimed to analyze the habitat suitability of the endangered Proboscis monkey (Nasalis larvatus) on Borneo using a multi-machine-learning approach. This study integrated physical, vegetational, meteorological, and human activity data to develop a comprehensive habitat suitability model. Four machine-learning algorithms, namely, maximum entropy (MaxEnt), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and classification and regression trees (CART), were employed to model the habitat suitability index. A total of 1943 sample points were divided into training (70 %) and validation (30 %) sets for the analysis. This study included three main stages: geospatial database creation, spatial habitat modeling using multi-machine-learning algorithms, and habitat suitability evaluation. In addition, the pressure from human development on the habitat suitability index model was analyzed. This study identified a high level of suitability for Proboscis monkey habitats in nearshore areas. The maximum habitat suitability for Proboscis monkeys was observed to be 11.54 %, as evidenced by the consensus of the MaxEnt value and four machine-learning algorithms. Conversely, the minimum habitat suitability was recorded at 13.27 %, as indicated by disagreement among all algorithms. The AUC values for the machine-learning models ranged from 74 % to 90 %, indicating moderate to high predictive performance. This study provides valuable insights for the formulation of well-planned development programs for Proboscis monkeys. The results of this study will contribute to the accurate identification of potential Proboscis monkey habitats, thereby providing support for conservation efforts aimed at safeguarding this endangered species.

A deep‐learning framework for enhancing habitat identification based on species composition

AbstractAimsThe accurate classification of habitats is essential for effective biodiversity conservation. The goal of this study was to harness the potential of deep learning to advance habitat identification in Europe. We aimed to develop and evaluate models capable of assigning vegetation‐plot records to the habitats of the European Nature Information System (EUNIS), a widely used reference framework for European habitat types.LocationThe framework was designed for use in Europe and adjacent areas (e.g., Anatolia, Caucasus).MethodsWe leveraged deep‐learning techniques, such as transformers (i.e., models with attention components able to learn contextual relations between categorical and numerical features) that we trained using spatial k‐fold cross‐validation (CV) on vegetation plots sourced from the European Vegetation Archive (EVA), to show that they have great potential for classifying vegetation‐plot records. We tested different network architectures, feature encodings, hyperparameter tuning and noise addition strategies to identify the optimal model. We used an independent test set from the National Plant Monitoring Scheme (NPMS) to evaluate its performance and compare its results against the traditional expert systems.ResultsExploration of the use of deep learning applied to species composition and plot‐location criteria for habitat classification led to the development of a framework containing a wide range of models. Our selected algorithm, applied to European habitat types, significantly improved habitat classification accuracy, achieving a more than twofold improvement compared to the previous state‐of‐the‐art (SOTA) method on an external data set, clearly outperforming expert systems. The framework is shared and maintained through a GitHub repository.ConclusionsOur results demonstrate the potential benefits of the adoption of deep learning for improving the accuracy of vegetation classification. They highlight the importance of incorporating advanced technologies into habitat monitoring. These algorithms have shown to be better suited for habitat type prediction than expert systems. They push the accuracy score on a database containing hundreds of thousands of standardized presence/absence European surveys to 88.74%, as assessed by expert judgment. Finally, our results showcase that species dominance is a strong marker of ecosystems and that the exact cover abundance of the flora is not required to train neural networks with predictive performances. The framework we developed can be used by researchers and practitioners to accurately classify habitats.

Distribution models of baleen whale species in the Irish Exclusive Economic Zone to inform management and conservation

Irish waters are under increasing pressure from anthropogenic sources including the development of offshore renewable energy, vessel traffic and fishing activity. Spatial planning requires robust datasets on species distribution and the identification of important habitats to inform the planning process. Despite limited survey effort, long-term citizen science data on whale presence are available and provide an opportunity to fill information gaps. Using presence-only data as well as a variety of environmental variables, we constructed seasonal ensemble species distribution models based on five different algorithms for minke whales, fin whales, humpback whales, sei whales, and blue whales. The models predicted that the coastal waters off the south and west of Ireland are particularly suitable for minke, fin and humpback whales. Offshore waters in the Porcupine Seabight area were identified as a relevant habitat for fin whales, sei whales and blue whales. We combined model outputs with data on maritime traffic, fishing activity and offshore wind farms to measure the exposure of all the species to these pressures, identifying areas of concern. This study serves as a baseline for the species presence in Irish waters over the last two decades to help develop appropriate marine spatial plans in the future.

Identification of suitable habitats and priority conservation areas under climate change scenarios for the Chinese alligator (Alligator sinensis).

Amphibians and reptiles, especially the critically endangered Chinese alligators, are vulnerable to climate change. Historically, the decline in suitable habitats and fragmentation has restricted the distribution of Chinese alligators to a small area in southeast Anhui Province in China. However, the effects of climate change on range-restricted Chinese alligator habitats are largely unknown. We aimed to predict current and future (2050s and 2070s) Chinese alligator distribution and identify priority conservation areas under climate change. We employed species distribution models, barycenter migration analyses, and the Marxian model to assess current and future Chinese alligator distribution and identify priority conservation areas under climate change. The results showed that the lowest temperature and rainfall seasonality in the coldest month were the two most important factors affecting the distribution of Chinese alligators. Future predictions indicate a reduction (3.39%-98.41%) in suitable habitats and a westward shift in their distribution. Further, the study emphasizes that suitable habitats for Chinese alligators are threatened by climate change. Despite the impact of the Anhui Chinese Alligator National Nature Reserve, protection gaps persist, with 78.27% of the area lacking priority protected area. Our study provides crucial data for Chinese alligator adaptation to climate change and underscores the need for improved conservation strategies. Future research should refine conservation efforts, consider individual plasticity, and address identified limitations to enhance the resilience of Chinese alligator populations in the face of ongoing climate change.

Diversity In N.W. Himalayan Macrofungi (Basidiomycetes) and their Conservation

The diversity and distribution of macrofungi belonging to Jelly fungi, Agaricales, Aphyllophorales and Gasteromycetes in N. W. Himalayas, based on extensive survey and collections of these fungi from this region have been discussed in the present communication. An emphasis is laid to intensify the survey and identification of taxa and habitats worthy of conservation, leading to ascertain the exact status of species. The paper discusses the various threats to which not only the fungi but their habitats are also constantly exposed, and suggests the strategies for their effective conservation. The problems faced in evolving the parameters towards the preparation of Red Data lists/Books on fungi have also been discussed and possible remedies to overcome them are outlined.

Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification

Automating identification of benthic habitats from imagery, with Machine Learning (ML), is necessary to contribute efficiently and effectively to marine spatial planning. A promising method is to adapt pre-trained general convolutional neural networks (CNNs) to a new classification task (transfer learning). However, this is often inaccessible to a non-specialist, requiring large investments in computational resources and time (for user comprehension and model training). In this paper, we demonstrate a simpler transfer learning framework for classifying broad deep-sea benthic habitats. Specifically, we take an ‘off-the-shelf’ CNN (VGG16) and use it to extract features (pixel patterns) from benthic images (without further training). The default outputs of VGG16 are then fed in to a Support Vector Machine (SVM), a classical and simpler method than deep networks. For comparison, we also train the remaining classification layers of VGG16 using stochastic gradient descent. The discriminative power of these approaches is demonstrated on three benthic datasets (574–8353 images) from Norwegian waters; each using a unique imaging platform. Benthic habitats are broadly classified as Soft Substrate (sands, muds), Hard Substrate (gravels, cobbles and boulders) and Reef (Desmophyllum pertusum). We found that the relatively simplicity of the SVM classifier did not compromise performance. Results were competitive with the CNN classifier and consistently high, with test accuracy ranging from 0.87 to 0.95 (average = 0.9 (±0.04)) across datasets, somewhat increasing with dataset size. Impressively, these results were achieved 2.4–5× faster than CNN training and had significantly less dependency on high-specification hardware. Our suggested approach maximises conceptual and practical simplicity, representing a realistic baseline for novice users when approaching benthic habitat classification. This method has wide potential. It allows automated image grouping to aid annotation or further model selection, as well as screening of old-datasets. It is especially suited to offshore scenarios as it can provide quick, albeit crude, insights into habitat presence, allowing adaptation of sampling protocols in near real-time.

Salmon shark seasonal site fidelity demonstrates the influence of scale on identifying potential high-use areas and vulnerabilities

Considering habitat use throughout the whole range of a highly mobile marine species is necessary to understand life history, identify vulnerabilities, and inform effective management. We used satellite tagging data from 128 adult female salmon sharks Lamna ditropis to identify seasonal hotspots of activity in an extended California Current region (ECCR; encompassing the California Current Large Marine Ecosystem), an area far away from their well-described primary habitat in the Alaska Downwelling Region where they have been documented, but whose utility has been poorly understood. Tag track durations had a mean of 447.7 ± 381 d, and 88 sharks (68.8%) visited the ECCR, comprising 33.6% of 28019 total daily Argos detections. Tracking data revealed that the timing and duration of migrations to the ECCR varied, but salmon shark distribution within the ECCR displayed consistent latitudinal shifts in accordance with regional oceanographic seasons. High site fidelity across multi-year tracks to high-productivity features, such as sea banks, and previously published knowledge of salmon shark life history suggest that the ECCR provides important foraging habitat which may be linked to reproductive success. The data reveal high overlap of salmon shark distribution with cumulative fishing effort collected by Global Fishing Watch for 2012-2019, particularly around seasonal hotspots, suggesting that female salmon sharks might be at risk of fisheries encounters. Collectively, our findings emphasize the importance of the ECCR in salmon shark life history and demonstrate the influence of spatial and temporal scale on interpretation of large movement data sets and identification of critical habitat outside of well-studied regions.

Tracking aquatic animals for fisheries management in European waters

AbstractAcoustic telemetry (AT) has emerged as a valuable tool for monitoring aquatic animals in both European inland and marine waters over the past two decades. The European Tracking Network (ETN) initiative has played a pivotal role in promoting collaboration among AT researchers in Europe and has led to a significant increase in the number of tagged and observed aquatic animals in transboundary European waters. While AT benefits decision‐making and delivers essential data to management bodies, its potential for management decision‐making mechanisms has yet to be fully harnessed. We reviewed existing research, studies, and organisational initiatives related to aquatic animal tracking and their utility in fisheries management in European waters. We found that AT has already contributed to many aspects of fisheries management, such as improved understanding of stock dynamics, identification of critical habitats, assessment of migration routes, and evaluation of the effectiveness of conservation measures. However, broader utilisation of tracking technologies is needed. By leveraging the full potential of AT, managers can make more informed decisions to protect, restore, and sustainably manage European waters and creatures that live therein.

Identification of key habitats and conservation threats of the Ganges River dolphin (Platanista gangetica) of Bangladesh

Ganges River Dolphins (Platanista gangetica) are known to inhabit all major river systems in Bangladesh. However, comprehensive spatial records for this globally endangered aquatic mammal are notably scarce nationwide. Furthermore, their habitats are increasingly threatened due to excessive utilization of aquatic resources and their habitats by the growing human population. To allocate limited resources in safeguarding high-priority dolphin habitats, we conducted field surveys and arranged stakeholder consultations to identify key dolphin habitats and the conservation threats they face across Bangladesh. Through this process, we have identified 14 river segments as high-priority winter habitats for Ganges River Dolphins. These dolphins encounter both direct and indirect threats within their habitats. Direct threats include entanglement and occasional intentional killing for traditional medicinal purposes and as fish attractants. Indirect threats leading to habitat degradation comprise the construction of dams and barrages for upstream water extraction, increasing industrial pollution in river waters, overfishing, and alterations to river morphology caused by siltation and sedimentation. The heavy reliance of local communities on dolphin habitats, coupled with a lack of widespread awareness and insufficient conservation efforts, pose significant challenges to the future survival of Ganges River Dolphins in Bangladesh. Bangladesh J. Zool. 51(3): 253-266, 2023

Evaluation and Selection of Multi-Spectral Indices to Classify Vegetation Using Multivariate Functional Principal Component Analysis

The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral bands. In the case of supervised mapping, time series based on classical vegetation indices (e.g., NDVI, GNDVI, …) are usually input characteristics, but the selection of the best index or set of indices (which guarantees the best performance) is still based on human experience and is also influenced by the study area. In this work, several different time series, based on Sentinel-2 images, were created exploring new combinations of bands that extend the classic basic formulas as the normalized difference index. Multivariate Functional Principal Component Analysis (MFPCA) was used to contemporarily decompose the multiple time series. The principal multivariate seasonal spectral variations identified (MFPCA scores) were classified by using a Random Forest (RF) model. The MFPCA and RF classifications were nested into a forward selection strategy to identify the proper and minimum set of indices’ (dense) time series that produced the most accurate supervised classification of plant communities and habitat. The results we obtained can be summarized as follows: (i) the selection of the best set of time series is specific to the study area and the habitats involved; (ii) well-known and widely used indices such as the NDVI are not selected as the indices with the best performance; instead, time series based on original indices (in terms of formula or combination of bands) or underused indices (such as those derivable with the visible bands) are selected; (iii) MFPCA efficiently reduces the dimensionality of the data (multiple dense time series) providing ecologically interpretable results representing an important tool for habitat modelling outperforming conventional approaches that consider only discrete time series.

Critical areas for sea turtles in Northeast Brazil: a participatory approach for a data-poor context.

Fishing is one of the main threats to sea turtles due to the risk of entanglement in lost nets, vessel collision and mortality due to incidental catches. In Brazil, most of the studies regarding fishing interactions with sea turtles are focused on pelagic longline fisheries in the South and Southeast regions. However, their main reproductive areas in Southwest Atlantic RMU occur in Northeast Brazil, which overlaps small-scale coastal gillnet fisheries. Here, we aimed to use ethnobiology and participatory approaches as simple and cost-effective methods to identify areas for sea turtle conservation where impacts from small-scale fisheries are most likely. Expert captains were trained to recording sea turtle sightings during navigations from the landing port to the fishing grounds, informing their folk nomenclatures. By interpolation of environmental data (chlorophyll and bathymetry) and ecological data, we predicted habitats of 3,459.96 km² for Caretta caretta, Chelonia mydas, and Eretmochelys imbricata and fishing zones of 1,087 km² for management in 20 m and 50 m depths. Our results contributes to ongoing discussions of bycatch mitigation for sea turtle species and identification of habitats. We highlights the importance of considering particularities of overlapped areas in marine spatial planning and co-management arrangements.

Evaluating the context dependency of large river nursery habitats for fishes in the lower Red River catchment

AbstractBoth fine‐ and coarse‐scale physicochemical conditions affect the quantity and quality of nursery habitats within riverine ecosystems. Nursery habitats in large, braided, and sandbed streams such as the lower Red River of Oklahoma, Texas, and Arkansas are not well described and likely vary among species. Identification of nursery habitats is important for developing proper conservation and management actions. We used an occupancy model framework to determine how hierarchical habitat factors related to the occupancy of 38 juvenile fish species. Our findings indicate that large river nursery habitats can generally be defined by reaches with off‐channel slackwater habitat, having deep pools but shallow thalweg depths, typically located further away from dams, and with low percentages of limestone lithology. Species within the same genera often exhibited variable relationships with river slope, amount of large woody debris, channel shape, discharge, and position of reaches within the stream network. Our results indicate important species‐specific relationships that define nursery habitats, indicating an important context dependency of nursery habitats even within fishes that are taxonomically similar. If the goal is to improve recruitment by native fishes, then consideration of the important species‐specific differences would be beneficial if improvements are made to nursery habitats. Moreover, careful consideration of the effects of dam operations will help maintain proper connectivity to off‐channel habitats important in downriver portions of the river network.