Assess Species Distribution Research Articles

Diversity and ecological niche model of malaria vector and non-vector mosquito species in Covè, Ouinhi, and Zangnanado, Southern Benin

The present study aimed to assess mosquito species diversity, distribution, and ecological preferences in the Covè, Ouinhi, and Zangnanado communes, Southern Benin. Such information is critical to understand mosquito bio-ecology and to focus control efforts in high-risk areas for vector-borne diseases. Mosquito collections occurred quarterly in 60 clusters between June 2020 and April 2021, using human landing catches. In addition to the seasonal mosquito abundance, Shannon's diversity, Simpson, and Pielou's equitability indices were also evaluated to assess mosquito diversity. Ecological niche models were developed with MaxEnt using environmental variables to assess species distribution. Overall, mosquito density was higher in the wet season than in the dry season in all communes. A significantly higher Shannon's diversity index was also observed in the wet season than in the dry seasons in all communes (p < 0.05). Habitat suitability of An. gambiae s.s., An. coluzzii, Cx. quinquefasciatus and Ma. africana was highly influenced by slope, isothermality, site aspect, elevation, and precipitation seasonality in both wet and dry seasons. Overall, depending on the season, the ecological preferences of the four main mosquito species were variable across study communes. This emphasizes the impact of environmental conditions on mosquito species distribution. Moreover, mosquito populations were found to be more diverse in the wet season compared to the dry season.

Assessment of species distribution and virulence factors of oral fungal carriage among hospitalized patients with COVID-19: a case-control study.

The COVID-19 pandemic highlighted the need to study oral fungal carriage and its potential impact. In oral fungal environments, factors like changes in respiratory epithelium, increased pathogen attachment, local inflammation, and virulence factors could influence COVID-19 severity. The authors conducted a study to explore oral fungal carriage in COVID-19 patients and compare it to a healthy control group. The authors executed a case-control investigation including 144 COVID-19 patients and an equivalent number of 144 healthy controls. The matching criteria encompassed age, sex, body mass index, and the history of antibiotic and antiviral medication intake. This research was performed over a span of 12 months from May 2021 to May 2022. The mouth area was sampled with a cotton-tipped swab. Subsequently, all the samples underwent fungal culture and PCR-sequencing procedures. In COVID-19 patients, oral fungal carriage was three times higher compared to healthy controls. Candida was the exclusive genus found in both groups, with Candida albicans being the most frequently isolated species (90.79%). Among COVID-19 patients, Candida species showed significantly higher esterase, proteinase, and hemolysin activity compared to healthy individuals. Both groups exhibited elevated levels of C. albicans virulence factors compared to non-albicans species. It is crucial to understand the way that virulence factors of oral fungal carriage act in COVID-19 patients in order to come up with novel antifungal medications, identify the contributing factors to drug resistance, and manage clinical outcomes.

A novel approach for improving the spatiotemporal distribution modeling of marine benthic species by coupling a new GIS procedure with machine learning

In this study we developed and validated a new integrative method for constructing and assessing Species Distribution Models (SDMs) for marine benthic species across spatial and temporal scales. This methodology synthesizes four dimensions-latitude, longitude, depth and time-into a unified predictive output, achieved by integrating a novel GIS tool, Bathymetric Projection (BP) with machine learning algorithms. BP can extract multi-dimensional data from remote sensing and satellite-derived datasets across the oceanic water column, including latitude, longitude, depth, and time. This data is then mapped onto the ocean floor using a reference bathymetric layer. The resulting seafloor layer is a predictive variable within the SDMs for benthic species. The model output quantifies the probability of species occurrence, or habitat suitability, as determined by environmental conditions specific to the ocean floor across the four dimensions. We validated this approach using two benthic species from the Colombian Pacific Ocean: the White shrimp (Litopenaeus occidentalis) and the Coliflor shrimp (Solenocera agassizii). The model's performance ranged from 87% to 98%, as indicated by 30-fold cross-validation on test datasets. Depth ranges and monthly seasonal patterns were accurately predicted when compared to independent literature data, confirming the model's capability to generate precise spatiotemporal distributions for benthic species. This research underscores the necessity of incorporating depth and time into SDMs, given their critical interactions with latitude and longitude in determining species distribution. The methodology offers a valuable tool for informing decision-making processes in fisheries and environmental management by enhancing the predictive accuracy of spatiotemporal distributions for benthic species, thereby facilitating more holistic interpretations of habitat suitability.

Which metrics derived from airborne laser scanning are essential to measure the vertical profile of ecosystems?

AbstractIn a recent perspective (Diversity and Distributions, 29, 39–50), ‘10 variables’ were proposed to measure vegetation structure from airborne laser scanning (ALS) for assessing species distributions and habitat suitability. We worry about this list because the variables predominantly represent variation in vegetation height, the vertical variability of vegetation biomass is insufficiently captured, and variables of vegetation cover are ill‐defined or not ecosystem agnostic. We urge for a better defined, more comprehensive and more balanced list, and for assessing which information from ALS point clouds is truly essential to measure the major dimensions of 3D vegetation structure within and across ecosystems and animal habitats. We think that the currently proposed ‘list of 10 ALS metrics’ is premature and that researchers and stakeholders should be cautious in adopting this list.

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

AbstractEcosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up‐to‐date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.

Some Noteworthy Issues in Joint Species Distribution Modeling for Plant Data

Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables. These issues address the following: (i) how the observed presence/absence data is linked to the latent normal variables as well as the resulting implications with regard to modeling the data sites as independent or spatially dependent, (ii) the incompatibility of point referenced and areal referenced presence/absence data in spatial modeling of species distribution, (iii) the effect of modeling species independently/marginally rather than jointly within site, with regard to assessing species distribution, (iv) the interpretation of species dependence under the use of latent multivariate normal specification, and (v) the interpretation of clustering of species associated with specific joint species distribution modeling specifications. It is hoped that, by attempting to clarify these issues, ecological modelers and quantitative ecologists will be able to better appreciate some subtleties that are implicit in this growing collection of modeling ideas. In this regard, this paper can serve as a useful companion piece to the recent survey/comparison article by [33] in Methods in Ecology and Evolution.

P020 Risk factors, speciation ,and antifungal susceptibility in candidemia patients: An observational study

Poster session 1, September 21, 2022, 12:30 PM - 1:30 PMObjectivesTo assess species distribution, antifungal susceptibility pattern, and associated risk factors in cases of candidemia among admitted patients in a tertiary care hospital in New Delhi, India.MethodsAny positive blood culture bottle which revealed budding yeast cells on gram stain was included in the study. The samples were subcultured onto blood agar and HiCrome™ Candida Differential Agar (HiMedia Laboratories Pvt. Limited, Mumbai), after which the colonies were subjected to identification with VITEK MS (bioMerieux, France). The antifungal susceptibility of each isolate was assessed with the help of Vitek 2 AST (bioMerieux, France). Fluconazole, voriconazole, caspofungin, amphotericin B, flucytosine, and micafungin were the antifungals tested for resistance. Antifungal susceptibility by broth microdilution was performed for C. auris and for fluconazole in the case of C. glabrata. Patient demographics, as well as risk factors associated with Candida infections, were collected from case files and by interviewing patients and bystanders.ResultsWe isolated 171 fungal isolates from 160 patients admitted in the study during a period of 1 year from February 1, 2021 to January 31, 2022.Out of the 171 fungal isolates, 162 were Candida spp. Trichosporon spp. Saccharomyces cerevisiae and Fusarium spp. contributed 6, 2, and 1 isolate respectively. Among the Candida isolates, the commonest were C. auris (n = 37) followed by C. tropicalis (n = 34), C. albicans (n = 22), and C. glabrata (n = 22) (Fig. 1). The most common isolate from patients admitted to the ICU/HDU was C. auris (31%). Whereas in wards C. tropicalis (22%) and C. parapsilosis (22%) contributed the maximum number of isolates. Candida pelliculosa (n = 8) and C. tropicalis (n = 7) were the most common isolates among neonates.Antifungal susceptibility results were interpreted as per Clinical Laboratory Standards Institute M27 A2 document. Overall sensitivity was highest for Micafungin followed by amphotericin B. Micafungin was mostly sensitive for C. auris (94.11%), whereas in case of amphotericin B it was 47.22% (Table 1).The most common risk factor observed was the presence of IV line (n = 135), antimicrobial therapy (n = 126), and diabetes (n = 46). In neonates also the most common risk factor was the presence of an IV line (n = 23) followed by outborn status (babies delivered outside in other hospitals and transferred subsequently) (n = 20).A total of 30 days hospital mortality was observed to be 54.05% in patients with C auris isolates.Conclusion Candida BSI etiologies are shifting away from C. albicans and towards species that have a higher propensity for developing resistance, such as the multidrug-resistant C. auris, which is rapidly spreading throughout the world. This highlights the importance of stepping up hospital infection control practices and antimicrobial stewardship initiatives in order to counteract the rapidly increasing antifungal resistance threat.

Fire severity has lasting effects on the distribution of arboreal mammals in a resprouting forest

AbstractWildfire severity is assumed to be an important driver of habitat availability and species' distributions in forest ecosystems. Many studies have focused on the immediate or short‐term effects of fire severity in fire‐prone ecosystems, with much less focus on how long the effect of fire severity may persist. We examined the effect of fire severity on the distribution of arboreal mammals at 3 and 10 years post‐fire, in a temperate forest ecosystem dominated by eucalypts that have strong resistance and resilience to low and high severity fire, respectively. The study took place within areas affected by the 2009 Kilmore East‐Murrindindi wildfires in south‐eastern Australia. Sites were established across a gradient of fire severity (unburnt through to canopy consumption) and surveyed at 3 and 10 years following fire. Owing to low detection rates for many species, analyses were limited to greater glider (Petauroides volans) occurrence and arboreal species richness. At both 3 and 10 years post‐fire, the greater glider was restricted to unburnt forest or sites affected only by understorey fire, which acted as fire refugia. Greater gliders were absent from sites affected by high severity, canopy‐consuming fires at both points in time. Greater glider occurrence was associated with high levels of canopy cover (&gt;50%), which were typically those areas that were unburnt or burnt at low severity. The richness of arboreal mammals increased over time and was greater in unburnt or minimally affected forest. Our findings emphasize: (i) that fire severity is an important driver of the distribution of arboreal mammals; and (ii) the importance of recognizing longer‐term effects of fire severity in assessing species distributions, even in highly resilient forest communities. Recent advances in mapping fire severity provide new opportunities to assess the effects of fire severity on the distributional patterns of fauna in eucalypt forests.

Native mass spectrometry for the investigation of protein structural (dis)order

A central challenge in structural biology is represented by dynamic and heterogeneous systems, as typically represented by proteins in solution, with the extreme case of intrinsically disordered proteins (IDPs) [1–3]. These proteins lack a specific three-dimensional structure and have poorly organized secondary structure. For these reasons, they escape structural characterization by conventional biophysical methods. The investigation of these systems requires description of conformational ensembles, rather than of unique, defined structures or bundles of largely superimposable structures. Mass spectrometry (MS) has become a central tool in this field, offering a variety of complementary approaches to generate structural information on either folded or disordered proteins [4–6]. Two main categories of methods can be recognized. On one side, conformation-dependent reactions (such as cross-linking, covalent labeling, H/D exchange) are exploited to label molecules in solution, followed by the characterization of the labeling products by denaturing MS [7–11]. On the other side, non-denaturing (“native”) MS can be used to directly explore the different conformational components in terms of geometry and structural compactness [12–16]. All these approaches have in common the capability to conjugate protein structure investigation with the peculiar analytical power of MS measurements, offering the possibility of assessing species distributions for folding and binding equilibria and the combination of both. These methods can be combined with characterization of noncovalent complexes [17, 18] and post-translational modifications [19–23]. This review focuses on the application of native MS to protein structure and dynamics investigation, with a general methodological section, followed by examples on specific proteins from our laboratory.

SpOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models

Abstract Occupancy modelling is a common approach to assess species distribution patterns, while explicitly accounting for false absences in detection–nondetection data. Numerous extensions of the basic single‐species occupancy model exist to model multiple species, spatial autocorrelation and to integrate multiple data types. However, development of specialized and computationally efficient software to incorporate such extensions, especially for large datasets, is scarce or absent. We introduce the spOccupancy R package designed to fit single‐species and multi‐species spatially explicit occupancy models. We fit all models within a Bayesian framework using Pólya‐Gamma data augmentation, which results in fast and efficient inference. spOccupancy provides functionality for data integration of multiple single‐species detection–nondetection datasets via a joint likelihood framework. The package leverages Nearest Neighbour Gaussian Processes to account for spatial autocorrelation, which enables spatially explicit occupancy modelling for potentially massive datasets (e.g. 1,000s–100,000s of sites). spOccupancy provides user‐friendly functions for data simulation, model fitting, model validation (by posterior predictive checks), model comparison (using information criteria and k‐fold cross‐validation) and out‐of‐sample prediction. We illustrate the package's functionality via a vignette, simulated data analysis and two bird case studies. The spOccupancy package provides a user‐friendly platform to fit a variety of single and multi‐species occupancy models, making it straightforward to address detection biases and spatial autocorrelation in species distribution models even for large datasets.

Bee foraging preferences, microbiota and pathogens revealed by direct shotgun metagenomics of honey.

Honeybees (Apis mellifera) continue to succumb to human and environmental pressures despite their crucial role in providing essential ecosystem services. Owing to their foraging and honey production activities, honeybees form complex relationships with species across all domains, such as plants, viruses, bacteria and other hive pests, making honey a valuable biomonitoring tool for assessing their ecological niche. Thus, the application of honey shotgun metagenomics (SM) has paved the way for a detailed description of the species honeybees interact with. Nevertheless, SM bioinformatics tools and DNA extraction methods rely on resources not necessarily optimized for honey. In this study, we compared five widely used taxonomic classifiers using simulated species communities commonly found in honey. We found that Kraken 2 with a threshold of 0.5 performs best in assessing species distribution. We also optimized a simple NaOH-based honey DNA extraction methodology (Direct-SM), which profiled species seasonal variability similarly to an established column-based DNA extraction approach (SM). Both approaches produce results consistent with melissopalinology analysis describing the botanical landscape surrounding the apiary. Interestingly, we detected a strong stability of the bacteria constituting the core and noncore gut microbiome across seasons, pointing to the potential utility of honey for noninvasive assessment of bee microbiota. Finally, the Direct-SM approach to detect Varroa correlates well with the biomonitoring of mite infestation observed in hives. These observations suggest that Direct-SM methodology has the potential to comprehensively describe honeybee ecological niches and can be tested as a building block for large-scale studies to assess bee health in the field.

Ubms: An R package for fitting hierarchical occupancy and N‐mixture abundance models in a Bayesian framework

Abstract Obtaining unbiased estimates of wildlife distribution and abundance is an important objective in research and management. Occupancy and N‐mixture abundance models, which correct for imperfect detection, are commonly used for this purpose. Fitting these models in a Bayesian framework has advantages but doing so can be challenging and time‐consuming for many researchers. We developed an R package, ubms, which provides an easy‐to‐use, formula‐based interface for fitting occupancy, N‐mixture abundance and other models in a Bayesian framework using Stan. The package also provides tools for visualizing parameter effects, calculating residuals, assessing goodness‐of‐fit and comparing models. We demonstrate the use of ubms by fitting an N‐mixture model to ruffed grouse Bonasa umbellus count data from drumming surveys conducted at roadside points sampled on five occasions annually during 2013–2015. To demonstrate the functionality of ubms, we used survey site as a random effect, and occasion date and per cent aspen cover at each site as covariates of detection and abundance respectively. The top‐ranked model included a positive effect of per cent aspen on grouse abundance. ubms has the potential to greatly increase the range of users who will be able to rigorously assess species distribution and abundance while correcting for imperfect detection in a Bayesian framework.

Methodological analysis of bioclimatic variable selection in species distribution modeling with application to agricultural pests (Metcalfa pruinosa and Spodoptera litura)

MaxEnt is a machine-learning-based species distribution modeling tool that is widely used to evaluate the occurrence possibility of a species. The characteristics of the algorithm requires that bioclimatic variables are appropriately selected in order for the model to predict species occurrence as precisely as possible without overfitting to the training data. In this study, we developed MaxEnt models with six different variable selection methods for two major pests, Metcalfa pruinosa and Spodoptera litura, which are differentially distributed worldwide. It was found that when evaluated by model performance indices that consider both the actual distribution and size of the prediction area, correlation-based screening, and principal component analysis-based variable reconstruction generally returned a better prediction accuracy. However, the optimal method was different for each species, indicating that the variable selection might depend on the target species and its distribution pattern, i.e., the presence data. In addition, we combined the two best MaxEnt models with a previously developed CLIMEX model, demonstrating the effectiveness of ensemble modeling in assessing species distribution. Although it was impossible to identify a single method for MaxEnt variable selection, this study provides insights on how to statistically select the bioclimatic variables and evaluate the performance between different models.

Eyes, ears, or nose? Comparison of three non-invasive methods to survey wolf recolonisation

The development and use of cost-effective and appropriate survey methods to assess species distribution and to monitor range expansion and contraction of wild populations is crucial due to the limited financial resources for conservation. Of particular importance, yet little studied, is the ability to collect information before a wild population is well established, i.e. at the early stages of recolonisation. During 2018 and 2019, we used camera traps, audio recorders, and scat detection dogs simultaneously to investigate composition, detection probability, and territorial extent of a pack of wolves in the Swiss Alps. We compared the efficacy of these survey methods by assessing sampling effort, data obtained, and costs. We show that, under the presented setup, camera traps and scat detection dogs substantially outperformed audio recorders in detecting wolves, representing the packs’ territorial extent, and revealing the number of adult wolves. The detection dogs did not detect pups but, unlike the other methods, allowed the identification of single individuals. The use of four camera traps during 13 weeks, a 24-km-long transect walked with the detection dog, or the use of one audio recorder during 148 weeks were necessary to obtain a comparable wolf detection probability. Our results show that no single method was able to return all information that we hoped to collect. Comprehensive and cost-effective information was best obtained by combining data from camera traps and detection dogs. We suggest both methods to be simultaneously used to successfully investigate wolf recolonisation into historical range.

No time to rest: How the effects of climate change on nest decay threaten the conservation of apes in the wild.

Since 1994, IUCN Red List assessments apply globally acknowledged standards to assess species distribution, abundance and trends. The extinction risk of a species has a major impact on conservation science and international funding mechanisms. Great ape species are listed as Endangered or Critically Endangered. Their populations are often assessed using their unique habit of constructing sleeping platforms, called nests. As nests rather than apes are counted, it is necessary to know the time it takes for nests to disappear to convert nest counts into ape numbers. However, nest decomposition is highly variable across sites and time and the factors involved are poorly understood. Here, we used 1,511 bonobo (Pan paniscus) nests and 15 years of climatic data (2003–2018) from the research site LuiKotale, Democratic Republic of the Congo, to investigate the effects of climate change and behavioural factors on nest decay time, using a Bayesian gamma survival model. We also tested the logistic regression method, a recommended time-efficient option for estimating nest decay time. Our climatic data showed a decreasing trend in precipitation across the 15 years of study. We found bonobo nests to have longer decay times in recent years. While the number of storms was the main factor driving nest decay time, nest construction type and tree species used were also important. We also found evidence for bonobo nesting behaviour being adapted to climatic conditions, namely strengthening the nest structure in response to unpredictable, harsh precipitation. By highlighting methodological caveats, we show that logistic regression is effective in estimating nest decay time under certain conditions. Our study reveals the impact of climate change on nest decay time in a tropical remote area. Failure to account for these changes would invalidate biomonitoring estimates of global significance, and subsequently jeopardize the conservation of great apes in the wild.

TaxonTableTools - A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data

DNA-based identification methods, such as DNA metabarcoding, are increasingly used as biodiversity assessment tools in research and environmental management. Although powerful analysis software exists to process raw data, the translation of sequence read data into biological information and downstream analyses may be difficult for end users with limited expertise in bioinformatics. Thus, the need for easy-to-use, graphical user interface (GUI) software to analyze and visualize DNA metabarcoding data is growing. Here we present TaxonTableTools (TTT), a new platform-independent GUI that aims to fill this gap by providing simple, reproducible analysis and visualization workflows. The input format of TTT is a so-called "TaXon table". This data format can easily be generated within TTT from two common file formats that can be obtained using various published DNA metabarcoding pipelines: a read table and a taxonomy table. TTT offers a wide range of processing, filtering and analysis modules. The user can analyze and visualize basic statistics, such as read proportion per taxon, as well as more sophisticated visualizations such as interactive Krona charts for taxonomic data exploration, or complex parallel category diagrams to assess species distribution patterns. Venn diagrams can be calculated to compare taxon overlap among replicates, samples, or analysis methods. Various ecological analyses can be produced directly, including alpha or beta diversity estimates, rarefaction analyses, and principal coordinate or non-metric multidimensional scaling plots. The taxonomy of a data set can be validated via the Global Biodiversity Information Facility (GBIF) API to check for synonyms and spelling mistakes. Furthermore, geographical distribution data can be automatically downloaded from GBIF. Additionally, TTT offers a conversion tool for DNA metabarcoding data into formats required for traditional, taxonomy-based analyses performed by regulatory bioassessment programs. Beyond that, TTT is able to produce fully interactive html-based graphics that can be analyzed in any web browser. The software comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) or the Python package index (https://pypi.org/project/taxontabletools/).

Exploring the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae), Using Multiple Species Distribution Model Approaches.

The American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae), is a vector for several human disease-causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using maximum entropy (MaxEnt), refining bioclimatic data inputs, and including soil variables. We then compared geospatial predictions from five species distribution modeling frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soil layers improved the accuracy of the MaxEnt model; 2) the predicted ‘infected niche’ was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.

Plant diversity patterns along environmental gradients in Nanda Devi Biosphere Reserve, West Himalaya

Mid to high-altitude vegetation in part of west Himalaya, India has been investigated and analyzed for assessing species distribution in relation to environmental variables. The buffer zone site of Nanda Devi Biosphere Reserve (NDBR) in Pindari–Sunderdhunga–Kafni (PSK) area (30 forest stands in 11 forest communities) of Kumaun and Lata–Tolma–Phagti (LTP) area (30 forest stands in 8 forest communities) of Garhwal region having 11 forest communities along different elevation gradients was identified for detailed surveys. Species composition showed a total of 451, distributed in NDBR along Pindari–Sunderdhunga–Kafni site and Lata–Tolma–Phagti transects. Among communities, Quercus semecarpifolia and Mixed Abies pindrow-Quercus spp. communities in Pindari–Sunderdhunga–Kafni site with 23 species each and Pinus wallichiana community in Lata–Tolma–Phagti site with 18 species showed highest species composition. The interrelationship between environmental variables and species distribution was analysed using Simple Linear Correlation analysis and Canonical Correspondence Analysis (CCA). Simple Linear Correlation and CCA indicated compositional differences in forest types that perhaps was caused by differences in species richness and forest composition. In both the sites, among all the environmental variables, elevation, slope and organic carbon were found to be the most controlling factor for species distribution. Different forest structural components like species richness, total basal area, canopy density was found to be varying greatly with environmental factors and human disturbance, however, more datasets of vegetation dynamics and responses are required to further strengthen this premise.

Relationships Between Community Structure and Environmental Factors in Xixiakou Artificial Reef Area

The construction of artificial reefs has unparallelly developed for a few decades in China. Artificial reefs can be used to manage and conserve commercially exploited fish and crustacea. However, their suitability as ecological niche is poorly characterized. Therefore, in this study, we detected the seasonal variation of community biodiversity and the corresponding driving environmental factors. We also explored the relationships between dominant species and environmental factors to identify appropriate ecological niche areas. Different statistical analysis methods were used to assess species distribution within an artificial reef area in Xixiakou during nine sampling events in four seasons between 2017 and 2018. Non-metric multidimensional scaling (NMDS) and cluster analysis results indicated that the components of community can be divided into two clusters. Complexity of community, which is exhibited by species number, biodiversity, and catch per unit effort (CPUE), was significantly higher in summer than in other seasons. Generalized additive model (GAMs) results revealed the significant effects of temperature and chlorophyll a on the community structure. Sebastes schlegelii, Hexagrammos otakii, Conger myriaster and Charybdis japonica were the dominant species in four seasons. GAMs results indicated that temperature, dissolved oxygen (DO), pH and chlorophyll a affect the CPUE of dominant species significantly. The distinct suitable ecological niche for each dominant species was found in this study. For example, Charybdis japonica preferred to live in the area with 20.7–22.1 °C, dissolved oxygen 7.07–7.15 mg L−1 and salinity 31.8–31.9. The results of this study are beneficial to resource conservation and fishery management.

Assessment of carbon stocks in oak forests along the altitudinal gradient: A case study in the Panchase Conservation Area in Nepal

Little study was done to understand the role of high-altitude oak forests in climate change mitigation. Here, we analyzed data from 30 sample plots collected along the four altitudinal gradients to assess species distribution and carbon stocks in the Panchase Conservation Area in Nepal. Three carbon pools were considered, namely aboveground carbon (AGC), belowground carbon (BGC), and carbon in litters (leaf litters, grass, and herb biomass, CiL). Seven species were found, of which two are oak tree species (Quercus semecarpifolia and Quercus lamellosa). Quercus semecarpifolia is the most dominant species in terms of stem density and carbon stocks, followed by Quercus lamellose. Average tree diameter, height, and total carbon stocks were 27.7 cm, 8.4 m, and 127.6 MgC ha−1, respectively. Our study found higher carbon stocks at the higher altitudinal gradient. Pearson’s correlation analysis shows a strong effect of altitudinal gradients on carbon stocks (r = 0.7124), moderate effect on tree height (r = 0.5263), and less effects on diameter (r = 0.1733). A stepwise multiple regression analysis shows strong relationship between carbon stocks with height (P = 0.0059) and carbon stocks with tree diameter (P = 0.0148). Our ground observations and random interviews indicate that the low carbon stocks at the low altitude were due to human disturbance. Oak forests at lower altitudes are sparse forest stands with poor regeneration capacity, overgrazing, and extensive lopping. If such disturbance is prevented, oak forest can increase carbon stocks to 361.7 MgC ha−1 as found at the higher altitude. Conservation of oak forests in the Panchase Conservation Area could result in emission reductions of 633.0 MgCO2 ha−1. These reductions could be eligible for financial incentives under the REDD + scheme.