Anthropogenic Variables Research Articles

Biophysical factors control invasive annual grass hot spots in the Mojave Desert

Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.

Geographical trends of soil‐associated biodiversity changes due to tree plantations in South America: Biome and climate constraints revealed through meta‐analysis

AbstractAimTo evaluate the interaction between climate and biome structure when explaining changes in species richness of soil‐associated communities due to tree plantations developed in different biomes. Compare the response of plants, soil invertebrates and soil microorganisms, and to test whether they should be considered sensitive‐coupled biotas.LocationContinental South America.Time Period1996–2023.Major Taxa StudiedPlants, soil invertebrates and soil microorganisms.MethodsThrough a meta‐analysis, the change in species richness (i.e. response ratio) associated with tree plantations was evaluated in 127 points of study across South America, considering soil‐associated communities of plants, invertebrates and microorganisms. The influence of biome structure (open vs. closed habitats) on the response ratio, and its interaction with the actual evapotranspiration (AET) and temperature seasonality was evaluated. Differentiated responses of different taxa were tested by comparing models with and without an interaction term referring to the taxon studied. The regional agricultural cover and plantation age were considered as anthropogenic variables.ResultsModels containing the AET were better at explaining the trend of change in species richness than those with temperature seasonality. The response of the change in species richness was oppositely related to the AET in open and closed biomes. Plants presented a higher loss in species richness than soil invertebrates and microorganisms. The three taxa were positively associated with AET, while seasonality was not relevant in any case. Both anthropogenic variables significantly lessened the change in species richness in all models.Main ConclusionsThe structural contrast between the anthropogenic habitat and the biome where it is developed is a key factor influencing the response of soil‐associated communities to tree plantations. Nevertheless, its influence must be assessed together with climatic and anthropogenic variables given that their interaction can explain different geographical trends in the change in species richness across regions.

Impacts of Climate Change and Anthropogenic Activities on the Net Primary Productivity of Grassland in the Southeast Tibetan Plateau

Climate change and anthropogenic activities have had a profound effect on the variation in grassland productivity in the Tibetan Plateau in recent decades. Quantifying the impacts of climatic and anthropogenic variables on grassland productivity is a necessary step in making the management policies of a sustainable grassland ecosystem. Net primary productivity (NPP) is an important part of the terrestrial carbon cycle and can be used to assess vegetation growth. Based on the Carnegie–Ames–Stanford Approach model and statistical analysis method, in this study we estimated the variations in grassland potential NPP (PNPP), actual NPP (ANPP) and human-induced NPP (HNPP) in the Northwest Sichuan Plateau (NWSP) of the Southeast Tibetan Plateau from 2001 to 2020. Also, we assessed the contribution of climatic change and anthropogenic activities to grassland ANPP. The results showed that the average values of grassland ANPP, PNPP and HNPP in the whole NWSP increased at the rates of 3.81, 9.14 and 7.18 g C m−2 a−1, respectively. Grassland ANPP increased in 91.7% of the total area. Climate-oriented impacts led grassland ANPP to increase in 82.6% of the area, and temperature increase was the dominant factor. Additionally, anthropogenic activity was the major reason for the grassland ANPP’s decline (5.4% of the total area). Overall, our findings are beneficial for the formulation of practical countermeasures regarding climate change adaption and damaged grassland recovering in the plateau.

Identifying the drivers of silky shark distribution and an evaluation of protection measures

The silky shark (Carcharhinus falciformis) has experienced a significant population decline associated with intense targeted and incidental fishing pressure. Large marine protected areas (MPAs) are increasingly advocated for the conservation of oceanic species like silky sharks, recognising that the benefits of MPAs to such species depend on a comprehensive understanding of their distribution, abundance and life history. We combined mid-water stereo-baited remote underwater video system (BRUVS) records with environmental, geographic and anthropogenic variables to document the distribution and abundance of silky shark populations, identify the most important predictors of their presence, abundance and body size, and determine if their abundance is greater within MPAs than in locations not designated as MPAs. From 1418 deployments of mid-water BRUVS across three ocean basins, 945 silky sharks were identified at 18 locations, with young-of-year (< 87 cm TL) observed at four of these. Our study revealed generally low abundances of silky sharks as recorded on mid-water BRUVS across their cosmopolitan distribution, although our models identified seamounts as hotspots of abundance. Human pressure was a significant variable within our models, with proximity to human populations and ports being key drivers of silky shark abundance and body size. We did not observe a higher abundance of silky sharks inside MPAs compared to locations not designated as MPAs, suggesting that these MPAs have not been placed in areas where silky sharks remain relatively abundant. We therefore recommend expanding the current MPA network in line with the 30 × 30 initiative to more effectively protect key habitats such as seamounts.

Where do we manage? Uncovering the hidden impact of human presence on mammal conservation in northern Myanmar

Species spatiotemporal responses to human presence and prioritizing conservation areas are essential for effective management and conservation planning. To address this information, we conducted a long-term large mammal survey in both protected areas (PAs) and outside of PAs (non-PA) in Northern Mountain Forest Complex (NMFC), Myanmar by using 174 camera traps from 2015 to 2019. We selected 14 common species for the single species occupancy model and activity pattern analysis to evaluate their spatiotemporal responses to different anthropogenic and environmental variables. Results showed that human presence did not have a significant impact on mammal occupancy, whereas at the community level, it had a significant influence on mammal abundance. Some variables, including distance to settlements, proximity to water sources and forest cover, shaped the habitat occupancy of mammals in NMFC. Regarding the temporal axis, only three out of 14 species had higher temporal activity overlap with humans, while the remaining species showed lower to medium overlap in both PAs and non-PA, confirming that most mammals avoided human presence through temporal rather than spatial scale. Moreover, our priority analysis revealed that non-PA supported a significant proportion of medium- to high-priority areas, particularly in the south-eastern part of NMFC. We concluded that preserving both PAs and non-PA are crucial and human activities have an implicit influence on large mammal conservation in NMFC. The government should prioritize the south-eastern region of outside of the current PAs network and adopt community-based conservation strategies to ensure the efficacy of PA networks and biodiversity conservation in NMFC.

Indicator species reveal the physical and biological singularity of esker ecosystems

Eskers are complex geological formations shaped with a linear accumulation of sand and gravel under the glaciers during the last ice age and that provide crucial resources such as drinking water, sand/gravel, outdoor recreational sites, and productive forests. Surrounding sand and gravel and connection with the groundwater influences the physicochemical properties of lakes on esker which can benefit different biotic communities in the food web. The sustainable management of resources provided by eskers requires baseline ecological knowledge of these ecosystems. However, very little information exists about the ecology of freshwater ecosystems on eskers. This study uses a food web approach to identify the environmental variables, biological diversity, and indicator species associated with esker lakes to better understand their ecological functioning and biodiversity patterns to benefit their sustainable management and conservation. Fifty lakes were sampled in the Abitibi-Témiscamingue region (Canada), half on eskers and half on the surrounding boreal clay belt to include the most abundant lake ecosystems of the region. Physicochemical, environmental, and anthropogenic variables measured in the two lake types showed that esker lakes differed markedly from clay lakes. Nutrient concentrations, conductivity, and macrophyte cover were significantly lower in esker lakes than in clay lakes, whereas dissolved oxygen saturation and concentration showed the opposite trend. Three interconnected trophic levels of the esker lake food webs—waterbird, fish, and macroinvertebrate communities—were characterized for biological diversity and the associated species. We found a significantly lower Shannon diversity index for waterbirds (mean ± standard deviation; 0.7 ± 0.2), fish (0.4 ± 0.3), and a tendency for a lower value for macroinvertebrates (0.9 ± 0.3) in esker lakes than the clay lakes (1.1 ± 0.4, 0.9 ± 0.3, and 1.3 ± 0.5, respectively). Common goldeneye (Bucephala clangula) and Canada goose (Bucephala clangula) were associated significantly with esker lakes and identified as indicator species for esker lakes. In contrast, ring-necked duck (Aythya collaris) and hooded merganser (Lophodytes cucullatus) were associated significantly with clay lakes. Perlidae was similarly associated with esker lakes as an indicator for macroinvertebrates. Anthropogenic activities such as forest harvesting have altered the waterbird community, and recreational activities around the lakes have modified the fish and macroinvertebrate communities. We conclude that esker lakes differ from other regional lakes and are associated with specific environmental and biological variables and indicator species. The biological diversity in esker lakes is lower than that of clay lakes for all studied trophic levels of the food web, but these waterbodies provide preferential habitats for some species. This research provides the first baseline ecological information necessary to establish sustainable management and conservation strategies for this vulnerable ecosystem.

Integration of physically-based numerical tools and socio-hydrological variables in urban flood modeling

Floods are considered the most destructive natural catastrophes on the planet; therefore, studying their dynamics in the context of each region is a fundamental pillar of society. A very popular tool for studying the degree of threat is physics-based modeling using 2D models. It is imperative to include physical, hydrological, and anthropogenic variables in flood modeling approaches. The reviewed literature did not develop a methodology or strategy to facilitate this process. Based on this reason, it was considered for Riohacha, a coastal city in northern Colombia. A survey was conducted to determine social, economic, and flood depth variables during extreme events. Twenty socio-hydrological variables were obtained from the survey analysis using Kruskal–Wallis and multiple correspondence analysis (MCA) tests. Flood depth was the response variable used to select the socio-hydrological variables, among which the following stood out: age of the house, internal slope, toilet overflow, proximity to a wetland, paved road, raft area, and slope of the soil in front of the house. An iterative process supported by a Support Vector Machine (SVM) dummy was used to determine the appropriate combination of parameters and optimize the calibration process of the TELEMAC-2D hydrodynamic model. The curve number (CN) and Manning friction coefficient were used as calibration parameters. The optimization process was performed by entering the dummy SVM into the socio-hydrological variables CN and Manning's n to test up to 20,000 possible combinations of the parameters and evaluate the mean absolute error (MAE), mean error (ME), absolute relative error (RAE), root mean square error (RMSE), and inertia root mean squared error (IRMSE). The best combination of parameters to simulate an extreme flood event and determine the metrics with the observed and simulated flood depths was entered into TELEMAC-2D. This paper presents a methodological proposal for optimizing the calibration process of physics-based flood models (traditional method), considering socio-hydrological variables, to articulate it as a new step in the modeling protocol. In the traditional simulation, an RMSE of 0.48 m, MAE of 0.37 m, and IRMSE of 1.37 m are obtained, which decrease to 0.33 m, 0.27 m, and 0.92 m, respectively, when the socio-hydrological variables are included. Contrary to the previous metrics, the ME improves slightly from 0.15 m with the traditional method to 0.17 m when the socio-hydrological variables are included. These results can be improved using a digital terrain model (DTM) that more realistically represents the complexity of the urban fabric. With only 7 iterative processes, the model calibration was optimized using only seven iterative processes. Therefore, the proposed methodology was established as a tool for optimizing the flood modeling processes.

Dominance of Topography on Vegetation Dynamics in the Mt. Qomolangma National Nature Reserve: A UMAP and PLS-SEM Analysis

The southern portion of the Qinghai–Tibet Plateau (QTP) and the central Himalayan region are home to the Mt. Qomolangma (Everest) National Nature Reserve (QNNR), which is the world’s highest nature reserve and is distinguished by delicate natural ecosystems and unique geographic features. Analyzing regional vegetation trends, as well as the impacts of natural and anthropogenic variables on vegetation coverage, is crucial for local environmental protection and sustainable development. In this study, the variation patterns of the MOD13Q1 Normalized Difference Vegetation Index (NDVI) data were explored, and the responses of vegetation development to both natural and anthropogenic parameters were investigated by applying trend analysis and partial correlation analysis, as well as the partial least squares-structural equation model (PLS-SEM). To better comprehend the spatial characteristics and interrelationships between NDVI and various parameters under different vegetation types, the Uniform Manifold Approximation and Projection (UMAP) was employed for dimensionality reduction and visualization. The results illustrated that between 2000 and 2018, the reserve greened up at a rate of 0.00073/a (p &lt; 0.05), with vegetation improvement areas accounting for 49.46%. The major climatic driver for the greening trend of vegetation was temperature. Topography (especially elevation) remains dominant in regulating vegetation development in the QNNR, despite a progressively growing impact of hydrothermal conditions on vegetation development. Additionally, the implementation of environmental initiatives has stifled the adverse impacts of human activity.

Macroecology of rocky intertidal benthic communities along the southwestern Atlantic: Patterns of spatial variation and associations with natural and anthropogenic variables

Assessing spatial variability in biodiversity and its relationships with potential drivers is necessary for understanding and predicting changes in ecosystems. Here, we evaluated spatial patterns in sessile macrobenthic communities in rocky intertidal habitats along the southwestern Atlantic (SE Brazil), spanning over 500 km of coastline. We applied a rapid-survey approach focusing on the main space occupiers and habitat-forming taxa. We partitioned community variance into spatial scales ranging from metres to hundreds of kilometres and assessed whether community patterns were associated with variation in shore topography, nearshore ocean, and human influence. The communities from the mid-midlittoral level exhibited equivalent variation (31–35%) at the scales of quadrats (metres), sites (kilometres), and sub-regions (tens of kilometres). For the communities from the low-midlittoral and infralittoral fringe levels, most variability occurred at the scales of quadrats and sites (30–42%), followed by sub-regions (22%). Wave fetch, sea surface temperature (SST), and shore inclination were the variables that best explained community structure at the mid-midlittoral. At the low-midlittoral and infralittoral fringe, the most influential variables were related to oceanic forcing (SST, total suspended solids, particulate organic carbon, chlorophyll-a concentration) and human influence. Univariate analyses also revealed strong associations between the abundance of the main components of the communities and the predictor variables evaluated. Our results suggest that urbanised estuarine bays and coastal upwelling regimes have a strong influence on adjacent benthic communities, driving macroecological patterns in the study area. This study advances the knowledge in macroecology and biogeography of rocky shores in an understudied coastline and globally and provides valuable insights for future assessments of ecological changes resulting from unfolding human impacts.

Is there a consistency in basin morphometry and hydrodynamic modelling results in terms of the flood generation potential of basins? A case study from the Ulus River Basin (Türkiye)

Floods are natural phenomena that increase their impact every year due to increasing natural and anthropogenic variables, which are effective individually or together. Many research studies have focused on understanding the causative factors. This study aims to reveal whether there is a consistency in the results of the basin morphometry and hydrodynamic model used to understand the flood generation potential of river basins. Both analyses have been discussed individually in many papers but are not comprehensively compared. The Ulus settlement (Türkiye), where Ulus upstream, Suleyman, Alpi, and Eldes tributaries converge, was chosen as a study area. The results of the two analyses were evaluated in terms of their potential ability to produce floods over the subbasins and the Ulus settlement. Eight basin morphometric parameters were applied to the 10 m resolution TopoDEM data, and the results were evaluated according to the Normalised Morphometric Flood Index (NMFI) method, which helps decrease subjectivity in ranking the results. Flow data of the ungauged subbasins were produced using the SWAT rainfall-runoff model, and 2, 5, 10, 20, 50, 100, 200, 500, and 1000 years maximum flow data were generated using flood frequency analysis, and the different flow data were modelled based on 1 m LiDAR DEM using the LISFLOOD-FP 2D-hydrodynamic model. According to the results, the Suleyman subbasin has the highest flood generation potential in basin morphometry and hydrodynamic modelling. It is followed by the Alpi subbasin at high potential, Ulus upstream and Eldes subbasins at medium potential. As a result, we determine there is a consistency in basin morphometry and hydrodynamic modelling results. The basin morphometry and the developed NMFI can be used to determine and understand the flood-generating potential of basins or subbasins in data-scarce regions regarding flood modelling.

Predicting Species Occurrence of Litsea leytensis Merr. in the Provinces of Laguna and Quezon, Philippines

Litsea leytensis Merr. is an endemic and premium hardwood species in the Philippines used for wood carving. It is a threatened species that survival in the wild is impossible with persistent causal factors. The study estimated the species probability of occurrence in Laguna and Quezon based on local knowledge, published records of species distribution, and environmental variables using the Maximum Entropy (MaxEnt) model. Six pre-models were generated: one climatic model, four partial models of variable groups combined with climatic variables, and one full model with 31 original variables. The Final model had 19 highly correlated variables after variable selection and reduction using Jackknife and multi-collinearity tests. Analysis of variable importance revealed that L. leytensis’ occurrence was mostly determined by climatic (62.0%), edaphic (21.76%), and anthropogenic variables (9.53%), while topographic (5.15%) and vegetation-related variables (1.34%) had lesser contributions. The Area Under the Receiver Operating Characteristic Curve (AUC) and True Skill Statistics (TSS) measured model accuracy; and the Final model performed best at AUC = 0.9489 and TSS = 0.7175. Modeling species probability of occurrence could help key sectors in Laguna and Quezon to formulate appropriate conservation strategies for L. leytensis as a reforestation and industrial tree plantation species.

CORRECTION: Influences of natural and anthropogenic habitat variables on Indo-Pacific humpback dolphins Sousa chinensis in Hong Kong

CORRECTION: Influences of natural and anthropogenic habitat variables on Indo-Pacific humpback dolphins Sousa chinensis in Hong Kong

The impact of anthropogenic pollutants on the distribution of a marine top predator within a coastal estuarine system.

Due to anthropogenic pressures, estuarine systems are among the most broadly impacted areas for marine top predator species. Given this, it is crucial to study the interaction between the vulnerable marine species that inhabit these regions with environmental and anthropogenic variables. This study aims to determine whether nutrient pollution is related to the presence of bottlenose dolphins in a coastal environment. Using a multi-year dataset and GAMs, we studied the relationship between marine pollutants and the presence of bottlenose dolphins in this highly impacted coastal marine environment. We observed that urban fertilizers were linked to the spatial distribution of bottlenose dolphins. There was a higher presence of bottlenose dolphins in areas with high levels of phosphoric acid. In contrast, at higher concentrations of nitrate, the presence of bottlenose dolphins decreased.

Projecting shifts in the distributions of Chinese endemic vertebrate species under climate and land-use change

Human-induced climate and land-use change impact species’ habitats and survival ability. A growing body of research uses species distribution models (SDMs) to predict potential changes in species ranges under global change. We constructed SDMs for 411 Chinese endemic vertebrates using Maximum Entropy (MaxEnt) modeling and four shared socioeconomic pathways (SSPs) spanning to 2100. We compared four different approaches: (1) using only climatic and geographic factors, (2) adding anthropogenic factors (land-use types and human population densities), but only using current data to project into the future, (3) incorporating future estimates of the anthropogenic variables, and (4) processing species occurrence data extracted from IUCN range maps to remove unsuitable areas and reflect each species’ area of habitat (AOH). The results showed that the performance of the models (as measured by the Boyce index) improved with the inclusion of anthropogenic data. Additionally, the predicted future suitable area was most restricted and diminished compared to the current area, when using the fourth approach. Overall, the results are consistent with other studies showing that species distributions will shift to higher elevations and latitudes under global change, especially under higher emission scenarios. Species threatened currently, as listed by the IUCN, will have their range decrease more than others. Additionally, higher emission scenarios forecast more threatened species in the future. Our findings show that approaches to optimizing SDM modeling can improve accuracy, predicting more direct global change consequences, which need to be anticipated. We also show that global change poses a significant threat to endemic species even in regions with extensive protected land at higher latitudes and elevations, such as China.

Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda

Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017. A geostatistical model of CCHF seroprevalence among livestock was developed to incorporate environmental and anthropogenic variables associated with elevated CCHF seroprevalence to predict CCHF seroprevalence on a map of Uganda and estimate the probability that CCHF seroprevalence exceeded 30% at each prediction location. Environmental and anthropogenic variables were also analyzed in separate models to determine the spatially varying drivers of prediction and determine which covariate class resulted in best prediction certainty. Covariates used in the full model included distance to the nearest croplands, average annual change in night-time light index, percent sand soil content, land surface temperature, and enhanced vegetation index. Elevated CCHF seroprevalence occurred in patches throughout the country, being highest in northern Uganda. Environmental covariates drove predicted seroprevalence in the full model more than anthropogenic covariates. Combination of environmental and anthropogenic variables resulted in the best prediction certainty. An understanding of the spatial distribution of CCHF across Uganda and the variables that drove predictions can be used to prioritize specific locations and activities to reduce the risk of future CCHF transmission.

Using Multiscale Environmental and Spatial Analyses to Understand Natural and Anthropogenic Influence on Fish Communities in Four Canadian Rivers

Science-based conservation of riverine fishes can be best targeted with specific information about spatial-ecological controls on the community, including anthropogenic stressors. Because anthropogenic stressors can originate at multiple spatial scales, we investigated the influence of natural and anthropogenic variables summarized within the reach, valley, and catchment on fish community composition along four river mainstems in Ontario, Canada. We used Redundancy Analyses (RDA) to explore models with multi- and single-scale variables on fish community composition. We used partial RDAs to differentiate the relative effects of variable types in multiscale models and to determine if spatial variables explained additional variation in fish community composition. Catchment variables accounted for the majority of explained variation in fish community composition in three of the four rivers, but instream habitat variables accounted for considerable variability in fish community composition in the two rivers that are highly fragmented by dams or naturally occurring rapids. Natural and human-derived fragmentation in rivers may reduce the influence of catchment controls, disrupt longitudinal gradients, and increase the influence of local instream habitat. Environmental variables that explained fish distribution had longitudinal or patchy spatial pattern within rivers, but spatial variables representing impediments to fish dispersal and proximity to receiving waterbodies failed to explain additional variation in fish community composition.

Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

Fromforest floor to tree top: Partitioning of biomass and carbon stock in multiple strata of forest vegetation in Western Himalaya.

The foremost role of forest vegetation in storing biomass and carbon (C) stock constitutes one of the main nature-based solutions to mitigate climate change. In this study, we aimed to quantify biomass and C stock partitioning in multiple vegetation strata (tree, shrub, herb, and ground floor layers) of major forest types in Jammu and Kashmir,Western Himalaya, India. We used a stratified random cluster sampling strategy to collect field data in 96 forest stands of 12 forest types (altitudinal range: 350 to 3450m) in the study region. We evaluated the degree to which the carbon stock of the entire ecosystem was dependent on the multiple vegetation strata using the Pearson method. Across all the forest types, the average total ecosystem-level biomass was estimated to be 181.95 Mgha-1 (range: 60.64-528.98). Forest strata-wise, the maximum biomass of 172.92 Mgha-1 (range: 50.64-514.97) was found in the tree vegetation, followed by 5.58 Mgha-1 (range: 2.59-8.93) in understory vegetation (shrubs and herbaceous), and 3.44 Mgha-1 (range: 0.97 and 9.14) in the forest floor. The total ecosystem-level biomass showed a peak at mid-elevation coniferous forest types, whereas the lowest was observed in low-elevation broad leaved forest types. At the ecosystem-level, on average, the understory contributed 3% and the forest floor 2% to the total C stock across the forest types. The shrub layer contributed up to 80% of total understory C, with the herbaceous layer accounting for the remaining 20%. The ordination analysis clearly shows that anthropogenic and environmental variables significantly (p ≤ 0.002) influence the forest types' C stock in the region. Our findings have significant implications for conserving natural forest ecosystems and restoring degraded forest landscapes in this Himalayan region, which in turn can lead to better carbon sequestration and climate mitigation outcomes.

Characterising an interaction between coastal bottlenose dolphins (Tursiops truncatus) and the spot gillnet fishery in southeastern North Carolina, USA

The aim of this study was to characterise interactions between coastal bottlenose dolphins (Tursiops truncatus Montagu, 1821), and the autumn gillnet fishery in southeastern North Carolina, USA that targets spot (Leiostomus xanthurus). Beach based (conducted from April 1997 - January 1998) and aerial surveys (conducted from July 1998 - May 1999) were used to estimate the abundance of dolphins and gillnets in nearshore waters. Commercial spot landings records from Brunswick County, North Carolina were used as an index of prey abundance. Stranded bottlenose dolphins were evaluated using protocols developed to describe diagnostic evidence of human-induced fisheries mortality. During both survey periods, dolphin numbers, gillnet numbers and spot landings all peaked in October-November. Simultaneously, an increase in dolphin strandings bearing evidence of entanglement in gillnets (cuts, lacerations, or wrapping marks on their appendages) was observed. Four stranded dolphins were determined to have been killed in gillnets, and one dolphin was removed alive from a gillnet in October 1997. Six stranded dolphins were killed in gillnets in October and November 1998. Thus, 20-24% of the annual allowable human-induced mortality for all USA Atlantic coastal bottlenose dolphins could be attributed to monofilament gillnets targeting spot in October and November in southeastern North Carolina. Both recreational and commercial fishermen target spot in the autumn using gillnets and dolphin mortality may be attributable to both aspects of the fishery. Results of this study are cause for alarm because interactions between dolphins and coastal gillnets may be occurring at much larger spatial and temporal scales along the USA Atlantic coast. Understanding the relationships between the biological and anthropogenic variables leading to these interactions can facilitate conservative, pro-active, management ensuring that human-induced mortality is not negatively impacting populations of marine mammals, such as Atlantic coastal bottlenose dolphins.

Host, environment, and anthropogenic factors drive landscape dynamics of an environmentally transmitted pathogen: Sarcoptic mange in the bare-nosed wombat.

Understanding the spatial dynamics and drivers of wildlife pathogens is constrained by sampling logistics, with implications for advancing the field of landscape epidemiology and targeted allocation of management resources. However, visually apparent wildlife diseases, when combined with remote-surveillance and distribution modelling technologies, present an opportunity to overcome this landscape-scale problem. Here, we investigated dynamics and drivers of landscape-scale wildlife disease, using clinical signs of sarcoptic mange (caused by Sarcoptes scabiei) in its bare-nosed wombat (BNW; Vombatus ursinus) host. We used 53,089 camera-trap observations from over 3261 locations across the 68,401 km2 area of Tasmania, Australia, combined with landscape data and ensemble species distribution modelling (SDM). We investigated: (1) landscape variables predicted to drive habitat suitability of the host; (2) host and landscape variables associated with clinical signs of disease in the host; and (3) predicted locations and environmental conditions at greatest risk of disease occurrence, including some Bass Strait islands where BNW translocations are proposed. We showed that the Tasmanian landscape, and ecosystems therein, are nearly ubiquitously suited to BNWs. Only high mean annual precipitation reduced habitat suitability for the host. In contrast, clinical signs of sarcoptic mange disease in BNWs were widespread, but heterogeneously distributed across the landscape. Mange (which is environmentally transmitted in BNWs) was most likely to be observed in areas of increased host habitat suitability, lower annual precipitation, near sources of freshwater and where topographic roughness was minimal (e.g. human modified landscapes, such as farmland and intensive land-use areas, shrub and grass lands). Thus, a confluence of host, environmental and anthropogenic variables appear to influence the risk of environmental transmission of S. scabiei. We identified that the Bass Strait Islands are highly suitable for BNWs and predicted a mix of high and low suitability for the pathogen. This study is the largest spatial assessment of sarcoptic mange in any host species, and advances understanding of the landscape epidemiology of environmentally transmitted S. scabiei. This research illustrates how host-pathogen co-suitability can be useful for allocating management resources in the landscape.