Habitat Monitoring Research Articles

An ecosystem-based system of variables to enhance marine species and habitat monitoring and conservation: The Adriatic Natura 2000 case study

Implementing effective marine monitoring to detect and track ecosystem shifts, biodiversity alteration, and habitat loss is one of the most crucial challenges to meet the objectives set out by the Post-2020 Biodiversity Framework and by the United Nations Sustainable Development Goals. The lack of coordinated and harmonized monitoring frameworks at different spatial scales and their weakness in accounting for ecological processes, due to incomplete sets of monitoring variables, strongly hinder the achievement of conservation objectives. Here, we propose an approach to build a coherent ecosystem-based system of monitoring variables for target marine species and habitats. The approach is designed to integrate the existing monitoring frameworks set up by the Water and the Marine Strategy Framework directives, and the Essential Ocean and Biodiversity Variables, with the aim to contribute to their harmonization and implementation. Furthermore, by embracing a holistic vision, it aims to incorporate ecological processes and socio-ecological aspects, considering the benefits of public engagement through citizen science, and of the ecosystem services approach for policies’ implementation. The study stems from the Ecological Observing System of the Adriatic Sea (ECOAdS), which was developed in the framework of the Interreg Italy-Croatia project ECOSS, using as exemplary monitoring test cases two relevant conservation targets for Natura 2000 sites of the Adriatic Sea, the common bottlenose dolphin and seagrass meadows. We test the potential of this approach in guiding the prioritization of monitoring variables under ecosystem-based criteria, and provide insights into the benefits delivered by an integrated system of observatories’ networks and monitoring frameworks to support marine conservation at both local and regional scales. The proposed approach can be transferred to other contexts and scales to help build a common knowledge and monitoring framework for conservation and management strategies, saving costs by relying on available resources and on consolidated and long-lasting approaches that might converge towards global initiatives.

Species characterization of soft bottom habitats by depth and sediment particle size on the Catalan coast (NW Mediterranean): unexpected species composition of the assemblages

The aim of the study is to describe the macrofaunal composition of marine soft bottom habitats on the Catalan coast between a depth of 3 and 35 meters, considering all groups of macroinvertebrates. The study also aims to compare our data with previous studies focused on the NW Mediterranean and to define the variables that drive assemblage composition. Depending on the species composition and the structure of the assemblages, six different habitats, mainly defined by depth and sediment granulometry, are described. Shallow (<20 m) and deep (>20 m) habitats are easily distinguished and are subdivided in three habitats each: (1) very coarse and coarse sediments (coarse sediments); (2) medium, fine and very fine sands (fine sediments); and (3) muddy sediments. Fine sediments in both shallow and deep waters have emerged as the most common and extensive habitats along the coast up to a depth of 35 meters. Neither coarse nor muddy habitats are frequent. The characterization of shallow habitats produced some unexpected results with respect to previous studies: Spisula subtruncata and Lucinella divaricata were dominant, while neither Ditrupa arietina nor Owenia fusiformis, previously mentioned as dominant polychaetes in shallow fine sand habitats, were common. Polychaetes were, in general, the dominant group (both in species richness and abundance) in the six habitats, except in shallow fine sediments, which were dominated by mollusks. The study is a benchmark for future monitoring of soft bottom habitats on the Catalan coast to a depth of 35 meters.

Evaluation of machine learning approaches for surface water monitoring using Sentinel-1 data

The monitoring of mosquito breeding habitats requires the production of surface water maps on a regular basis and at a high-resolution using mapping algorithms. To map surface water, several machine learning (ML) algorithms were evaluated, taking advantage of frequently available synthetic aperture radar imagery from the Sentinel-1 mission with a 10-m spatial resolution and a large dataset of field observations of the water state (inundated/dry) in rice paddies and wetlands. One-class support vector machine, one-class self-organizing map, and multilayer perceptron with automatic relevance determination (MLP-ARD) algorithms were trained and assessed to examine their accuracy in detecting surface water. Results show the robustness of the MLP-ARD algorithm, which provides an overall accuracy of 0.974 for a single date and 0.892 for a 5-month study period from May to September. The accuracy of water detection was found to be mainly affected by the presence of dense and high vegetation in inundated fields and the presence of floating vegetation or algae.

DEDG: Cluster-Based Delay and Energy-Aware Data Gathering in 3D-UWSN with Optimal Movement of Multi-AUV

The monitoring of underwater aquatic habitats and pipeline leakages and disaster prevention are assisted by the construction of an underwater wireless sensor network (UWSN). The deployment of underwater sensors consumes energy and causes delay when transferring the gathered sensed data via multiple hops. The consumption of energy and delays are minimized by means of an autonomous unmanned vehicle (AUV). This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D-UWSN. Energy in the sensor nodes is saved by clustering and scheduling; on the other hand, the delay is minimized by the movement of the AUV and inter-cluster routing. In clustering, multi-objective spotted hyena optimization (MO-SHO) is applied for the selection of the best sensor for the cluster head, which is responsible for assigning sleep schedules for members. According to the total number of members, an equal half of the members is provided with sleep slots based on the energy and hop counts. The redundancy in the gathered data is eliminated by measuring the Hassanat distance. Then, the moving AUV is able to predict its movement by the di-factor actor–critic path prediction method. The mid-point among the four heads is determined so that the AUV can collect data from four heads at a time. In cases where the waiting time of the CH is exceeded, three-step, inter-cluster routing is executed. The three steps are the discovery of possible routes, ignoring the longest paths and validating the filtered path with a fuzzy–LeNet method. In this 3D-UWSN, the sensed data are not always normal, and, hence, a weighted method is presented to transfer emergency events by selecting forwarders. This work is implemented on Network Simulator version 3.26 to test the results. It achieves better efficiency in terms of data collection delay, end-to-end delay, AUV tour length, network lifetime, number of alive nodes and energy consumption.

A Metaheuristic Algorithm for Vertex Cover based Link Monitoring and Backbone Formation in Wireless Ad hoc Networks

Wireless ad hoc networks (WANETs) are infrastructureless networks and are used in various applications such as habitat monitoring, military surveillance, and disaster relief. Data transmission is achieved through radio packet transfer, thus it is prone to various attacks such as eavesdropping, spoofing, and etc. Monitoring the communication links by secure points (monitors) is an essential precaution against these attacks. Also, deploying monitors provides a virtual backbone for multi-hop data transmission. However, adding secure points to a WANET can be costly in terms of price and time, so minimizing the number of secure points is of utmost importance. Graph theory provides a great foundation to tackle the emerging problems in WANETs. A vertex cover (VC) is a set of vertices where every edge is incident to at least one vertex. The minimum weighted connected VC (MWCVC) problem can be defined as finding the VC of connected nodes having the minimum total weight. MWCVC is a very suitable infrastructure for energy-efficient link monitoring and virtual backbone formation. In this paper, we propose a novel metaheuristic algorithm for MWCVC construction in WANETs. Our algorithm is a population-based iterated greedy (PBIG) approach that is very effective against graph theoretical problems. We explain the idea of the algorithm and illustrate its operation through sample examples. We implement the proposed algorithm with its competitors on a widely used dataset. From extensive measurements, we obtain that the algorithm produces WCVC with less weight at the same time its monitor count and time performances are reasonable.

Flow alterations in rivers due to unconventional oil and gas development in the Ohio River basin

Unconventional oil and gas (UOG) exploration and development in the Ohio River basin has been a controversial issue for the past decade. The process of extracting gas and oil from shale formations utilizes significant amounts of water with little to no recycling in the region. The environmental risks to date have largely focused on air and water quality, with some attention paid to noise pollution. This study examines the risk for excessive water withdrawals and potential for negative impacts of low streamflow, which have largely not been addressed, as basin-wide studies have indicated that UOG will not significantly alter flow at large scales. The smallest watersheds, however, are often not monitored and therefore no historical record of flow is present. Using modelled estimates of historic flow, the impacts of UOG-related water withdrawals in HUC12 watersheds (approximately 1st to 3rd order streams) across the eastern Ohio River basin were estimated. Modelling of the effect of well operations showed that >10 % and >20 % reductions in streamflow occurred at least episodically in 53 % and 42 % of the HUC12 watersheds analyzed, respectively, amounting to 8.8 % and 2.4 % of active days. Although such severe reductions were usually infrequent in a particular stream, they could have lasting negative impacts on the stream biota. These flow reductions have the potential to affect downstream users, including regionally-endangered species. The legal framework surrounding water withdrawal permitting should have a substantial impact on flow reduction, but a lack of sufficient monitoring and clear reporting of water withdrawal sources hinders effective monitoring and protection of stream and riparian habitats.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.

Raking over the ashes: assessing the impact of fire on native fauna in the aftermath of Australia’s 2019–2020 fires

ABSTRACT The 2021 annual forum of the Royal Zoological Society of NSW raked over the ashes of the unprecedented “Black Summer” bushfires of 2019–2020 in eastern and southern Australia to assess how forest ecosystems and their constituent fauna had fared. This paper provides an overview of the 21 studies that were presented at the forum, now as papers in this theme edition of Australian Zoologist. All the authors were unanimous in their agreement about the unparalleled extent and severity of the fires and the magnitude of their ecological impacts. Whereas much of the focus of the 2019–2020 fires was on vertebrates, significant research was also carried out on a diverse range of invertebrate taxa. The studies of the invertebrate groups found that different taxa respond variably to fire and also emphasised the difficulties in judging the full impact of the fires due to taxonomic impediments. An underlying theme in almost all studies was that long-term and broad-scale monitoring of fauna and faunal habitats is essential if we are to build a robust understanding of how animals respond to fire, and in turn how managers can mitigate the impacts of fire in future. Such monitoring will need to incorporate the effects of other disturbance factors, such as habitat fragmentation, drought, salvage logging and longwall mining, that interact with fire, and also trial new methods to track and assist fauna to cope with the changing fire regimes. Several studies advocated the use of novel and emerging technologies to achieve better monitoring of fauna, while others proposed mapping of large scale, as well as micro-refuges, to maximise fire resilience, or the use of supplementary resources such as nest boxes and artificial roosts to replace those lost in fires. We concur with all the authors that a critically important way to protect and manage our native fauna is through expanded and sustained research and monitoring programs, and by making the key results available to managers and policy makers via peer-reviewed publication.

Considerations and tradeoffs of UAS-based coastal wetland monitoring in the Southeastern United States

Coastal wetlands of the Southeastern United States host a high abundance and diversity of critical species and provide essential ecosystem services. A rise in threats to these vulnerable habitats has led to an increased focus on research and monitoring in these areas, which is traditionally performed using manual measurements of vegetative characteristics. As these methods require substantial time and effort, they are often limited in scale and infeasible in areas of dense or impassable habitat. Unoccupied Aircraft Systems (UAS) provide an advantage over traditional ground-based methods by serving as a non-invasive alternative that expands the scale at which we can understand these ecosystems. While recent interest in UAS-based monitoring of coastal wetland habitats has grown, methods and parameters for UAS-based mapping lack standardization. This study addresses variability introduced by common UAS study techniques and forms recommendations for optimal survey designs in vegetated coastal habitats. Applying these parameters, we assess alignment of computed estimations with manually collected measurements by comparing UAS-SfM mapping products to ground-based data. This study demonstrates that, with careful consideration in study design and analysis, there exists great potential for UAS to provide accurate, large-scale estimates of common vegetative characteristics in coastal salt marshes.

Mapping essential habitat of estuarine fishery species with a mechanistic SDM and Landsat data

The identification and monitoring of essential fishery habitat (EFH) is primary and important for the conservation of estuarine fishery habitat and sustainable fisheries management. However, mapping EFH of estuarine fishery species with generally statistical species distribution model (SDM) remains difficult due to their complex life histories and the extensive spatiotemporal variability of habitat variables. Thus, we proposed a mechanistic SDM by analyzing a more direct and certain relationship at a pre-selected suitable life stage with prior knowledge of species life history, not a rough statistical analysis of overlapping cause–effect relationships operating across multiple life history stages in reverse. Landsat data was used to describe the extensive spatiotemporal variability of estuarine habitat variables. A window filtration was contributed most to select the suitable life stage with a score scheme. As mapping EFH needs to feasibly locate the representative and essential habitats over large estuarine scale, we constructed two indicators to represent “habitat importance” and “descriptor predictability” in the score scheme, which was semi-quantified with three primary factors (habitat function, duration of life history stage, inversion limitation of critical descriptors from Landsat data). Through selecting the suitable life stage for the mechanistic SDM, the usage of Landsat archives could be extended, which is beneficial from a few optically / thermally active determinants. The applicability of the mechanistic SDM was tested by mapping EFH of Eriocheir sinensis in the Yangtze River Estuary (YRE), China. The window filtration of E. sinensis’ life history stages in the YRE showed that the berry stage was filtered as the suitable stage with the highest scores both in habitat importance and descriptors predictability. Then the distribution of E. sinensis was modelled, predicated, and assessed at berry stage by comparing outward in a generally statistical way and inward with other filter window. The mechanistic model at berry stage showed the best explanatory and predictive power in both occurrence and abundance models, validating that the optimized SDM had much stronger capability in model fit and prediction. The score scheme was also verified to set reasonably and effectively through the model comparisons at different filter window stages. With the predicated distribution of E. sinensis, the core ranges of EFH were mapped at berry stage, which were mostly distributed in sub-tidal areas neighbored to wetlands, showing the habitat requirement of shallow water for hiding. Meantime, the determinants’ variance partitioning, in which dissolved oxygen (DO) and total suspended sediment (TSS) exceeded over 65%, complementally illustrated that core EFH may also require high water quality. The identification of EFH locations with our mechanistic SDM would provide spatial-explicit information and guidance to fishery habitat protection and fishery resources maintenance. Additionally, this study also explored a new way to utilize a few predictors from the extensively used Landsat data in an alternative way. This study could extend the limitations of SDM in EFH mapping and bridge gaps between spatiotemporal explicit modelling and remote sensing data accommodations, which is meaningful for fishery habitat conservation.

Moth-Flame Optimization Algorithm for Efficient Cluster Head Selection in Wireless Sensor Networks

Network lifetime and energy consumption are the important requirement of wireless sensor networks. The sensor network is mainly used for the military and civil applications, habitat monitoring. These tasks consume more energy for the data processing and directly affect the network lifetime. Clustering methodology provides a better solution for prolonging the network lifetime and reducing the energy consumption. In this paper, moth flame optimization algorithm is proposed in LEACH-C algorithm for identifying the suitable cluster head in wireless sensor networks. The proposed methodology uses the navigation method of moths for balancing the exploration and exploitation phases in the optimization process. The residual energy of the node and distance between the cluster head and sensor node are utilized to calculate the fitness function. The proposed methodology is evaluated with the help of performance metrics of network lifetime, energy consumption and number of alive nodes. The proposed methodology prolongs the network lifetime and reduces the energy consumption.

Monitoring of bats in key wintering shelters of the Northern Black Sea region (Ukraine)

Based on the results of winter monitoring of bats conducted in 2020–2022 in key wintering sites—limestone mines located within the continental part of the Back Sea area and the steppe area of Ukraine (Mykolayiv and Odesa ob-lasts)—we have recorded 7 species of bats: Myotis daubentonii, M. dasycneme, M. aurascens, M. mystacinus, Plecotus auritus, P. austriacus, and Eptesicus serotinus. Compared to results of monitoring conducted between 2008 and 2011, the number of bats that winter in the mentioned mines has doubled, from 702 to 1421 spe¬cimens. One individual of M. aurascens, which was ringed in the mine KVL-K in 2008–2011, was recorded there for the sec-ond time. The ring number is VТ 03492, Kyiv, Ukraine. Five species of bats were recorded in each of the three moni-tored sites. The results of the two monitoring sessions appear to be almost identical both by species composition and abundance. The dominating species in the inspected sites and, apparently, generally for winter aggregations of the same type in the region is M. daubentonii. The species share in the total sample was 77%. In Kovalivka (KVL-K) and Illyinka (ILN-K) mines, the co-dominance of two species—M. daubentonii and M. aurascens—was observed (23.3% and 26.8%, respectively). The species M. mystacinus was recorded only in the Kovalivsky mines (9.6%), whereas P. auritus was found only in the Nova Odesa mines (0.1%). The Nova Odesa mines play a key role in pre-serving the rare M. dasycneme in Ukraine harbouring the most abundant and widely known accumulation of this species. All investigated caves are particularly important for the preservation of bat populations in the region and their support during critical stages of their life. Granting the Kuyalnik Estuary the status of nature reserve will con-tribute to the conservation of habitats important for bats. Monitoring of key habitats for conservation of bat species is an important task for the implementation of EUROBATS agreement, which was signed by Ukraine. The obtained data are especially crucial considering the invasion of the russian federation to Ukraine since it is unclear when there will be a chance to continue the monitoring of bats in these key wintering sites and what is their current condition.

Evaluation of a diatom eDNA-based technique for assessing water quality variations in tropical lakes and reservoirs

Freshwater bodies, both lotic (canals, rivers) and lentic (lakes and reservoirs), in the tropics have only rarely been subjected to intense monitoring regimes owing to limited expertise and resources, despite tropical habitats being among the most dynamic and threatened globally. Diatoms are an important part of assessing aquatic environmental changes in parts of the world where monitoring is well-established because of their sensitivity to a range of indicators of water quality. In such cases, diatoms have traditionally been identified based on morphology. Morphological information on tropical diatom taxa and the expertise to assemble it are sparse. However, DNA-based assessment is less reliant on taxonomic expertise and has the potential to be more cost-efficient. The application of DNA-based technology could thus expedite the incorporation of diatoms into routine monitoring in the tropics. Here we evaluate an environmental DNA (eDNA) metabarcoding workflow for diatom identifications based on 109 water samples from six freshwater reservoirs in Singapore, a highly urbanised part of tropical Southeast Asia. The six reservoirs span a range of sizes and catchment settings. We generate baseline information for lentic systems by targeting planktonic assemblages and including two commonly used barcodes, rbcL and 18S, to assess detection differences. Congruence with morphological identifications is generally low and each technique reveals distinct species lists, but overall congruence between methods improves when comparisons are at the genus level. We discuss the probable methodological and analytical causes for this incongruence and conclude that both approaches be used concurrently for generating reference databases of relatively poorly-known diatom diversity in tropical freshwater habitats. More promisingly, we find that eDNA datasets were sufficient to reveal both spatial and temporal patterns in diatom assemblages across the six study sites. We also show that regardless of the detection method used, diatom assemblages correlated with similar environmental variables. Results highlight the potential feasibility of eDNA-based diatom monitoring of freshwater habitats in Southeast Asia and in the tropics more broadly.

Analysis of mangrove distribution using satellite images in Rembau River, Negeri Sembilan, Malaysia

Malaysia has been recognizedas a country with the greatest diversity of mangrove species. In this study, the mangrove forest at Rembau River, Negeri Sembilan, Malaysia was classified using multispectral SPOT 5 image. The mangrove vegetation species was identified using texture analysis, Normalized Difference Vegetation Index was also utilized to identify vegetation densities in the mangrove area. Classification approach was implemented using Maximum Likelihood Classifier. Eight classes of mangrove species were found within the study site which includes Sonneratia caseolaris, Nypa fruticans Bruguiera gymnorhiza, Xylocarpus granatum, Cerbera odollam, Sonneratia alba, Thespesia populnea and Rhizophora apiculata. The results showed that the highest per pixel classification accuracy (84.81%) for SPOT 5 image was using NIR band with a 9 × 9 window size. The upstream riverside area was dominated by Sonneratia caseolaris whereas Rhizhopora apiculata were dominant at downstream of the river. Dense vegetation dominates the overall mangrove forest of this study area. However, Sonneratia caseolaris and Nypa fruticans were found in low dense vegetation area. This endangers the firefly habitat. The results suggest preserving mangrove ecosystems to support firefly tourist operations. The findings of this research will benefit in monitoring and conservation of the mangrove habitats for the firefly population.

Warming and marine heatwaves tropicalize rocky reefs communities in the Gulf of California

Globally, marine communities are experiencing gradual warming and extreme heatwaves causing species to shift in geographic range. As a result, the biological assemblages outside tropical latitudes are being reorganized or “tropicalized” as warm-affinity species become increasingly dominant and cool water species recede, with impacts on our economies, food supply, and health. In the Gulf of California, existing oceanographic discontinuities shape marine communities by creating different assemblages according to environmental affinities. In this study, we show how a known ecological boundary underwent a northward shift of 1.5° latitude because of an average 1 °C gradual warming over the last decade (2010–2020) and extreme marine heatwaves threefold more frequent. Such shift homogenized environmental conditions and reconfigured rocky reefs communities. Fish biomass decreased of 43%, whereas invertebrates, which recorded a 35% decrease in overall abundance, showed different community configurations depending on the climate regime. Stony coral species with warm water affinities increased with a reduction of cold-water species during the last El Niño. The long-term consequences of the tropicalization of these rocky reefs’ communities are still uncertain. This study underlines the importance of long-term monitoring of key habitats to quantify, predict, and adapt to future climatic stresses.

Detection of Virulence-Associated Genes and in vitro Gene Transfer From Aeromonas sp. Isolated From Aquatic Environments of Sub-himalayan West Bengal.

Aeromonas is omnipresent in aquatic environments and cause disease within a wide host range. A total of thirty-four isolates from water samples of small fish farms were identified as Aeromonas based on biochemical characteristics and 16S rRNA gene sequence. A total of six virulent factors were analyzed which indicated 100% of isolates as beta-haemolytic and proteolytic, whereas 44.1, 38.2, and 70.6% of isolates produced DNAse, siderophore, and amylase, respectively. Studies on the occurrence of four genetic determinants of virulence factors revealed that aer/haem (haemolytic toxin) and flaA (polar flagella) genes were present in 44.1% of strains whereas ascV (type 3 secretion system) and aspA (serine protease) genes were detected in 21.5 and 8.82% of strains, respectively. Fish (Anabas testudineus) challenge studies showed that the isolate GP3 (Aeromonas veronii) bearing five virulent factors with the combination of aer/haem+/ascV+/fla+ genes induced severe lesions leading to 100% of mortality. In contrast, RB7 possessing four virulence factors and three genes (aer/haem+/ascV+/aspA+) could not produce severe lesions and any mortality indicating the absence of correlation between the virulence factors, its genes, and the pathogenicity in fishes. GP3 was cytotoxic to human liver cell line (WRL-68) in trypan blue dye exclusion assay. The 431 bp aer/haem gene of GP3 was transferable to E. coli Dh5α with a conjugational efficiency of 0.394 × 10–4 transconjugants per recipient cell. The transfer was confirmed by PCR and by the presence of 23-kb plasmids in both donor and transconjugants. Therefore, the occurrence of mobile genetic elements bearing virulence-associated genes in Aeromonas indicates the need for periodic monitoring of the aquatic habitat to prevent disease outbreaks.

Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis

Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.

The Status and Swot Analysis of Roe Deer Population Management in the Hunting Grounds of Central Serbia

Summary The purpose of this study is to examine the status of roe deer population in the hunting grounds of Central Serbia, and perform a SWOT analysis of its management. The data used herein on the number of roe deer individuals and culling events were obtained from the Ministry of Agriculture, Forestry and Water Management - Forest Administration and the Statistical Office of the Republic of Serbia. Moreover, the Law on Game and Hunting was used for analysis purposes. The status of roe deer population in the hunting grounds of Serbia is quite unsatisfactory, depending on the geographical area and wildlife management practices. In 2021, a total of 86,755 individuals were recorded in the area of Central Serbia, which accounts for 97.54% of the optimal population. However, the number of roe deer in the individual hunting grounds of the Central and Eastern hunting areas was above the optimal density, whereas the number of roe deer accounted for 88.58% and 89.12% of the optimal density in the individual hunting grounds of the Southern and Western hunting areas, respectively. The largest number of individuals per unit area (km2) was recorded in the Eastern (2.06) and Belgrade (2.02) hunting areas, whereas the smallest number of individuals per unit area (km2) was recorded in the Southern (1.02) and Western (1.34) hunting areas. The following issues were identified as major impediments to the management of roe deer population in the hunting grounds considered: legislative and regulatory instruments, non-compliance and non-enforcement of a large number of regulations, roe deer monitoring issues, poor hunting ground control, inapplicability of certain regulations in the field of veterinary medicine, environmental protection, unresolved issues of trophy and game meat export, lack of strategic planning (namely the hunting development strategies of the Republic of Serbia), lack of deer habitat monitoring methodology and rating methodology, and unprofessional planning and development of hunting ground management plans. The SWOT analysis performed emphasized the strengths, weaknesses, opportunities and threats of roe deer population management in Serbia.

Diversity, distribution and conservation status of raw edible plant resources of the Madgram Watershed, Lahaul Valley, Himachal Pradesh, India

The paper records the occurrence of 34 raw edible plants collected from the Madgram Watershed in the Lahaul Valley, Himachal Pradesh, India. Of the total 34 raw edible plants recorded, 12 species were native to the Himalayan Region and 7 species were native to the Himalayan and other biogeographical regions together. Ephedra gerardiana, Rheum australe and Sinopodophyllum hexandrum are endangered. Bunium persicum, Hippophae rhamnoides ssp. turkestanica and Rheum webbianum are vulnerable. Leaves and fruits were utilized in majority cases. Due to habitat degradation, overexploitation and changing environmental conditions, the populations of these wild edibles are facing high pressure. Therefore, frequent monitoring of habitats, populations, mass multiplication, education and awareness for the local inhabitants and people’s participation in conservation of wild edibles have been suggested.

Cases of fatal electrocution of the endangered Javan Gibbons (Mammalia: Primates: Hylobatidae) by power lines

Human-made structures are often deadly to wildlife. Power lines from electric poles can cause serious injury and even death of wildlife via electrocution, especially of arboreal primate species that can easily access power lines. Here, we reported three cases of wild ‘Endangered’ Javan Gibbons Hylobates moloch electrocuted by power lines along a road between a tea plantation and a village adjacent to Gunung Halimun-Salak National Park area, West Java, Indonesia. In all cases, the adult male Javan Gibbons jumped and grabbed a power line hanging at the edge of the forests and immediately died. Our case reports highlight that power lines can have a critically adverse effect on the survival of wild animals, particularly on highly arboreal species such as Javan Gibbons. We argue the need for mitigation plans (e.g., cable insulation) for power lines in areas at risk. Such areas should be determined based on wildlife habitat monitoring and the study of ranging behaviors, focusing on areas with high risks of electrocution.