Relative Importance Of Environmental Variables Research Articles

Habitat requirements of a critically endangered Ninox hawk‐owl: implications for island‐wide restoration

Small, isolated populations can be especially sensitive to poor habitat conditions due to their limited capacity to adapt or disperse. Management of such populations, with an objective to implement the most effective strategies, is a conservation priority. The Norfolk Island morepork (Ninox novaeseelandiae undulata) is a Critically Endangered hawk‐owl endemic to Norfolk Island with an estimated population of 25 individuals. This species faces compounding threats from habitat degradation and loss, as well as inbreeding depression. It presents an exemplar for the development of habitat management strategies for top‐order predators in small, isolated systems. We paired fine‐scale light detection and ranging‐derived habitat variables with tracking data to quantify habitat suitability and the relative importance of environmental variables. Maximum entropy modeling indicated vegetation patch size and canopy height were important habitat variables, with moreporks more likely to be found in larger patches and canopy heights above 7 m. Morepork presence was not strongly influenced by the type of wooded vegetation; rather, the species appears to be a generalist occupier of forested areas. We demonstrate that suitable habitat during the breeding season is currently constrained, with coverage limited to one‐third (34%) of the island and an estimated carrying capacity of 36 morepork pairs. Restoration of native forest in cleared areas adjacent to existing wooded patches, with a specific goal to increase patch size, was the optimized management strategy to increase in situ carrying capacity. This study has demonstrated how habitat suitability modeling can inform targeted habitat restoration to enhance threatened species recovery.

The relative importance of environmental variables at different spatial scales in structuring a stream fish metacommunity

The relative importance of environmental variables at different spatial scales in structuring a stream fish metacommunity

Elevational Patterns of Forest Evapotranspiration and Its Sensitivity to Climatic Variation in Dryland Mountains

Elevational climatic heterogeneity, complex terrains, and varying subsurface properties affect the sensitivity of evapotranspiration (ET) in dryland mountain forests to hydrometeorological changes. However, the elevational distribution of ET sensitivity and its major influencing factors remain poorly understood. This study focused on the mid-altitude zone (1000–3500 m) forests in the Chinese Western Tianshan Mountains and assessed ET sensitivity to multiple climate variables, including precipitation (P) and potential evapotranspiration (PET), from 2000 to 2020. To evaluate the multi-year mean and trends in ET sensitivity, multi-source remote sensing data and regional survey data were analyzed using Spearman’s correlation coefficient, the sliding window method, and Kendall’s test. Furthermore, the relative importance of environmental variables (topography, geology, soil, and vegetation) was investigated. P and PET showed no significant trends, while ET exhibited a significant increasing trend (5.81 mm/yr, p < 0.01), particularly at elevations above 2000 m. Most forests (93.5%) showed a positive sensitivity of ET to P, and 70.0% showed a positive sensitivity of ET to PET, mainly at elevations of 1500–2500 m. Additionally, the trend in ET sensitivity to P decreased with an increasing elevation, with 64.5% showing a positive trend. Meanwhile, the trend in ET sensitivity to PET increased with elevation, with 88.1% showing a positive trend. Notably, 53.2% of the forests showed increasing ET sensitivity trends to both P and PET, primarily at elevations of 2000–3000 m with a mean normalized difference vegetation index (NDVI) of 0.56. Geological factors, particularly the hydrological properties of weathered bedrock, contributed the most (~47%) to mean sensitivity. However, geological and vegetative factors, including the NDVI and root zone water availability, were the main contributors (35% each) to the sensitivity. This study highlights the elevation-dependent sensitivity of dryland mountain forests to hydrothermal changes, with higher-elevation forests (>2000 m) being more sensitive to global warming.

Local environmental and spatial factors are associated with multiple facets of riverine fish β‐diversity across spatial scales and seasons

Abstract Disentangling the factors governing community structure across various spatial and temporal scales is a core task in community ecology and biogeography, because it could provide valuable insights into biodiversity conservation and management. The relative role of environmental (a proxy for niche‐based mechanism) and spatial (a proxy for dispersal) effects in shaping ecological communities depends on diversity facets, network positions, seasonality and biological characteristics of the focal organisms. However, such patterns are not well understood for fish communities in large river systems, especially if all of these patterns are examined simultaneously. We investigated fish taxonomic, functional and phylogenetic total β‐diversity (and their turnover and nestedness phenomena) at different spatial scales (basin vs. section scales) and seasons (wet vs. dry seasons) in a subtropical river system (the Chishui River basin, China). We also quantified the relative importance of environmental variables and spatial factors in shaping fish β‐diversity by using generalised dissimilarity modelling. At the basin scale, fish taxonomic and phylogenetic β‐diversity across seasons mainly reflected turnover, yet turnover and nestedness contributed likewise to total functional β‐diversity. Taxonomic and phylogenetic β‐diversity were strongly correlated (r from 0.39 to 0.87), whereas functional and both taxonomic and phylogenetic facets were relatively weakly correlated (r from 0.27 to 0.65). Generalised dissimilarity modelling suggested that phylogenetic β‐diversity phenomena were consistently more influenced by environmental variables (wet 16.1%, dry 22.4%) than by spatial factors (wet 6.7%, dry 4.8%). However, for both taxonomic and functional facets, environmental variables (turnover 13.8%, nestedness 6.4%) and spatial factors (turnover 4.3%, nestedness 9.4%) accounted for a greater proportion of variations in turnover and nestedness, respectively. At the section scale, only phylogenetic total β‐diversity and nestedness, as well as taxonomic nestedness significantly differed between the headwater and downstream sections. Furthermore, headwater communities were primarily explained by spatial (15.7%) and environmental (12.7%) variables in the wet season, while environmental factors (25.9%; spatial 7.7%) dominated in the dry season. By contrast, downstream communities were more influenced by environmental factors (17.9%; spatial 8.3%) in the wet season, whereas environmental (11.0%) and spatial variables (11.5%) contributed likewise to β‐diversity in the dry season. Our results highlight that, compared with taxonomic β‐diversity, accounting for turnover and nestedness of functional and phylogenetic β‐diversity helps to reveal distinct biodiversity patterns, and potential environmental and spatial controls. Given that community–environment relationships and the relative contribution of environmental and spatial factors varied between seasons and between the basin and section scales, our study underlines the importance of long‐term dynamics and spatial scales in metacommunity studies. Our findings also suggest that maintaining environmental heterogeneity and adequate hydrological connectivity would be essential for the conservation of fish diversity. Overall, our study advances current knowledge of fish biodiversity patterns and their maintenance mechanisms by adding insights from multi‐faceted diversity and seasonality, which has rarely been done in large subtropical rivers.

Climate conditions are primary predictors of the regional‐scale spatial diversity patterns of ectomycorrhizal fungi

AbstractAimSpatial models are valuable for revealing biodiversity patterns but are less commonly applied to soil microbes than to aboveground macroorganisms. Ectomycorrhizal (EM) fungi are symbiotic microbes with high taxonomic and functional diversity that are associated with forest trees. We aimed to predict regional‐scale spatial patterns of EM fungal richness and community composition.LocationForests and subalpine ecosystems in Japan, from Hokkaido to Okinawa.TaxonEM fungi (Asco‐ and Basidiomycetes).MethodsWe used EM fungal DNA sequence data from 1507 soil cores at 39 sites covering a wide range of environmental conditions. The random forest machine learning approach was applied to determine the relative importance of environmental variables (i.e. climate, soil and ecosystem productivity) and to make spatial predictions. The spatial patterns of EM fungal richness and community composition were mapped at 1‐km2 grid resolution.ResultsTemperature generally had a strong influence on EM fungal richness and community composition dissimilarity. Our regional spatial analysis revealed that (1) EM fungal richness was higher in northeastern and montane regions than in southwestern regions and low‐elevation plains, (2) different EM fungal lineages exhibited contrasting spatial diversity patterns and (3) community composition dissimilarity shifted sharply from high to low elevations, and gradually from northeastern to southwestern regions, mainly in relation to climate gradients. Areas with low applicability for spatial modelling were identified based on multidimensional environmental spaces, which will help to prioritize data collection for future research.Main ConclusionsOur study provides a baseline of the potential spatial patterns of EM fungal communities, which were explained primarily by climate variables and secondarily by soil factors and ecosystem productivity. The predicted spatial patterns may be valuable for identifying diversity hotspots and advancing the assessment of climate change impacts on ecologically important root‐associated fungi.

Developing machine learning models with multiple environmental data to predict stand biomass in natural coniferous-broad leaved mixed forests in Jilin Province of China

Forest biomass is influenced by multiple environmental factors. Multi-source data on site-specific soil, climate, and stand factors are essential for stand biomass prediction. While previous studies concerning planted forest biomass at stand level highlighted the importance of environmental factors, few have been conducted on the effects of climate and soil on stand biomass estimation, especially for natural mixed forest with complicated stand structure. Machine learning (ML) algorithms provide new tools for examining the effects, but the applications are still limited. Therefore, the objectives of this paper were to develop ML models for stand biomass estimation with stand and multiple environmental predictors, and quantify the relative importance of environmental variables. Tree above- and below-ground biomass at stand level was set as output variable with the random forest (RF) and boosted regression tree (BRT) regression methods. The data were from 286 sample plots from the National Forest Inventory (NFI) in Jilin Province, northeast China. The results showed that models with the inclusion of stand, soil, and climate variables explained > 89% of forest biomass variations. BA (stand basal area) and H (stand average height) were the most important stand variables, DD18 (degree-days above 18 °C) was the most important climate variable, and bdod (bulk density) and pH were the most important soil variables. Relative importance of climate and soil factors was 0.5% to 5.1% and 1.2% to 4.1%, respectively, which were weak but important predictors of stand biomass. In the RF model, climate variables were more important than soil variables, but the opposite in the BRT models. We concluded that above forest stand, climate and soil factors were significant input variables for regional forest biomass mapping using ML.

Spatial analysis of the occurrence of the western conifer seed bug Leptoglossus occidentalis (Heteroptera: Coreidae) in Europe based on multiple environmental variables.

The western conifer seed bug (WCSB) Leptoglossus occidentalis (Heidemann) (Heteroptera: Coreidae) is a pest insect that causes significant losses of coniferous trees worldwide. In this study, we sought to project the potential distribution of the WCSB based on dual CLIMEX modeling and random forest (RF) analysis to obtain basic data for WCSB monitoring strategies. The CLIMEX model, a semimechanistic niche model that responds to climate-based environmental parameters, is a species distribution model that focuses on regional climatic suitability. Given that this model can be used to select areas that are likely to reflect the climatically favorable spread of species, which we initially used CLIMEX to evaluate the potential distribution of the WCSB. The RF algorithm was used to predict the potential occurrence of WCSB and to evaluate the relative importance of environmental variables for WCSB occurrence. Using the RF model, land cover was found to be the most important variable for classifying the presence/pseudo-absence of the WCSB, with an accuracy of 77.1%. Climatic suitability for the WCSB was predicted to be 2.4-fold higher in Southern Europe than in Western Europe, and the WCSB was predicted to occur primarily near coniferous forests. Given that CLIMEX and RF analyses yielded different prediction results, using the findings of both models may compensate for the shortcomings of these models when used independently. Consequently, to ensure greater prediction reliability, we believe that it would be beneficial to base predictions on the combined potential distribution data obtained using both modeling approaches.

Modeling impacts of climate change on the distribution of invasive Opuntia ficus-indica (L.) Mill. in Ethiopia: Implications on biodiversity conservation

The main danger to biological diversity is the introduction of exotic species. Opuntia ficus-indica (O. ficus-indica) is a dangerous invasive species that has seriously harmed Ethiopia's ecology and economy. To properly inform decision-making about the control of this invasive species, it is crucial to investigate the projected invasion dynamics of O. ficus-indica in the country under the current climate change scenarios. Thus, the objective of this research was to evaluate the current distribution and relative importance of environmental variables for O. ficus-indica distribution, map the habitat's future suitability under scenarios of climate change and assess how habitat change would affect the species' future expected suitability in Ethiopia. The SDM R program was used to perform species distribution modeling (SDM) using 311 georeferenced presence records along with climatic variables. Predictive models were developed as an agreement model from six modeling methodologies to investigate the climatic suitability of target species for the years 2050 and 2070 under two shared socio-economic pathway (SSP) scenarios (SSP2-4.5 and 5–8.5) in order to estimate the risks of climate change to the species. Under the current climatic scenario, only 9.26% (104939.3 km2) and 4.05% (45850.6 km2) of the country were moderately and highly suitable for species dispersion and invasion respectively. The remaining 86.69% (980648 km2) was suitable for the distribution and invasion of the species. In 2050, under the SSP2-4.5 and 5–8.5, the highly suitable range of O. ficus-indica is anticipated to expand by 2.30% and 1.76%, whereas the moderately suitable area is predicted to decrease by 1.66% and 2.69%, respectively. Under the SSP2-4.5 and 5–8.5 scenarios, the highly suitable region for the species is expected to grow by 1.47% and 0.65%, respectively, in 2070 compared to the current climatic conditions. This invasive species had already had a considerable negative influence on rangelands in a significant portion of the country with the current cover. Its continuing growth would exacerbate the issue, cause significant economic and environmental harm, and endanger the community's way of living. If preventive and efficient management methods are not taken seriously, the species will have considerable negative environmental impacts, which would be one of the biggest difficulties for pastoralism and their livelihoods.

Microbial activity and habitat heterogeneity in surface soil in a high-mountain grassland landscape (Central Apennines, Italy)

A spatially intensive sampling of surface soil layers (81 stratified-randomized sampling points across a 14 km2 study area) was performed in a high-mountain grassland landscape (Central Apennines, Italy) in July 2017, in order to describe the heterogeneity of microbial habitats and measure their microbial enzymatic activity. Three different microbial habitat types were identified via Hierarchical Cluster Analysis on the basis of 14 (measured or remote-sensed) environmental variables (including chemical and physical soil properties, topographic and geomorphological features and vegetation cover): (i) sub-acidic, at high elevation, with high vegetation cover (of mostly acidophytic/mesophytic species) and high silt content, and the lowest clay content; (ii) sub-acidic, at low elevation, with a high vegetation cover and a very low stone cover, with the highest sand and the lowest silt content, and the highest available phosphorus; (iii) very shallow soil, mainly stone-covered, at high elevation, with the highest pH values, on steep slopes, with the highest content of organic matter and the highest water holding capacity. The third habitat showed the highest enzymatic activity (b-glucosidase, β-cellobiohydrolase and leucine-arylamidase) involved in C and N cycling, while the more acidic and deeper soils, typical of sinkhole or slope areas, favoured the acid phosphomonoesterase activity involved in P cycle thanks to the establishment of the rhizospheric activity of the herbaceous plant species. Moreover, we analysed the relative importance of environmental variables on the total enzyme activities via Boosted Regression Trees (BRT): the results highlighted - for the first time as far as we know - the importance of topographic factors such as slope inclination in predicting the microbial functional capacity in a mountain grassland ecosystem. We conclude that the different enzymatic activity patterns found in the three habitats suggest diverse microbial functions with respect to nutrient cycling, within a small landscape and a relatively homogeneous land-cover.

Analysis of the evolution of a fisheries management plan based on environmental governance: Living laboratory in the Olo River, Portugal

Environmental governance aims to develop and understand the lessons, achievements and challenges of planning at both local and global levels. In particular, integrated river management is key to the sustainable development of ecosystems, and the collaborative and inclusive approach among stakeholders supports the decision-making process. The objective of this study was to assess the current management model for recreational fisheries in the Olo River Reserved Fishing Zone (RFZ). This management model aims to better rationalise the use of fishery resources by allowing only recreational fishing, ensuring the sustainability of this activity and of the environment, and guaranteeing that this activity contributes to the development of the basin. To this end, the state of this activity was evaluated before and after the implementation of the First Management Plan and the Second Management Plan. Samples of ichthyofauna were taken and an analysis of the data on the age, growth and physical condition of the endemic species was carried out. Multivariate analysis techniques applied to the biological and physical data sets allowed the determination of distribution patterns of Olo Basin and the relative importance of environmental variables in the discrimination of the fish assemblages.The presence of the species Salmo trutta fario improved in the stations sampled throughout the study. The abundance of fish throughout the study showed a remarkable improvement, from 1229 (year 2008), 1142 (year 2013) and 1781 (year 2018), the drop in 2013 was being due to a wildfire in the area. This fishing area has been a true living laboratory for the development of new and more sustainable inland fisheries management practices. The development of this planning and management model was successfully exported to other river basins in the area.

Spatiotemporal variability and controlling factors of indirect N2O emission in a typical complex watershed

The mechanisms of N2O emissions from inland rivers remain poorly understood because of the high variability of dissolved N2O concentration and the complexity of influencing factors. Thus, it is necessary to research the spatiotemporal patterns and influencing factors to understand the driving mechanisms of riverine N2O emissions. We combine the Soil and Water Assessment Tool (SWAT) outputs with established empirical equations to identify the spatiotemporal fluctuations of riverine N2O emissions and evaluate model performance through field measurements in a typical watershed. The spatiotemporal hotspots of N2O emissions are then determined, and the relative importance of environmental variables is further determined by correlation and attribution analysis. The results indicate that the riverine N2O emissions are relatively high from August to October, which accounted for 35.38% of the annual emissions. Temporal changes are attributed to agricultural activities and meteorological factors. Agricultural activities such as planting and fertilization lead to increased diffuse nitrogen loads on the land surface. Meantime, heavy precipitation events enhance the transport of nutrients, resulting in changes in nitrogen levels in the river. Spatial analysis shows that the urban watersheds (191.22 ± 156.19 μmol m−2 d−1) are the hotspots of riverine N2O emission, which are 1.55–3.03 times that of non-urban rivers. Spatial variations are mainly affected by riverine physicochemical indicators for different watersheds. Sewage from various sources received by urban rivers provides appropriate environmental conditions for N2O production, and transports large exogenous dissolved N2O. Furthermore, salinity (r = 0.80; p < 0.001) and nitrogen nutrients in riverine physicochemical indicators show a significant correlation with N2O fluxes. It emphasizes that N-related (TN, NH4+, NO3−) indicators are important reactants for N2O generation, which can promote nitrification and denitrification. Meanwhile, the results of structural equation modeling (SEM) also demonstrate that N2O emissions follow a similar pattern to riverine dissolved N2O concentration (r = 0.841, p < 0.001), and non-point source (r = 0.678, p < 0.001) play an important role in the changes of dissolved N2O concentrations. Our results highlight that certain hot moments and hot spots of rivers play a disproportionate role in year-round and basin-wide N2O emissions, respectively. It is necessary to implement more effective management measures by controlling key environmental factors to reduce N2O emissions.

Anuran assemblages in western Philippines: Unraveling the effects of habitat types, water availability, and elevation

Understanding the patterns and drivers of species assemblages is the central tenet of community ecology and this may inform effective conservation and management strategies. Such information is scarce in tropical regions, particularly the Palawan faunal region and island group in the western Philippines. Here we examined the patterns of anuran richness and identified correlations in species co-occurrences and their shared responses to habitat types, water availability, and elevation. We sampled anuran populations using standardized transects established across Palawan's major islands between 2013 and 2019. We generated interpolated and extrapolated richness using Hill numbers. To investigate species co-occurrences and the relative importance of environmental variables, we used Joint Species Distribution Model (JSDM). A total of 18 anuran species were recorded, including seven non-endemic and 11 species restricted to Palawan. Anuran richness was highest in forests habitats than in cultivated land areas. Sites with water sources had higher anuran richness than those without water. Species richness observed along elevational gradient exhibited a hump-shaped pattern. Significant positive correlations in species co-occurrences were found among aquatic and semi-aquatic species. Anuran species showed tight associations with elevation and water availability, indicating that these variables are strong predictors of anuran co-occurrences. Finally, this study broadens our understanding of the differences and similarities in habitat requirements among anuran species, highlighting the need to consider such information when developing conservation strategies other than the traditional vulnerability and irreplaceability measures.

Spatial heterogeneity of environmental factors related to the invasion of Hypochaeris radicata in South Korea

Early detection of the spatial expansion pattern of alien plants and the associated environmental factors are essential for their management. In this study, we applied regression kriging and geographically weighted regression, which can incorporate spatial autocorrelation into the model, to estimate the critical environmental factors related to the invasion of Hypochaeris radicata into new habitats. We spatially interpolated the occurrence probabilities of H. radicata based on field survey data from 2000–2001 and 2018–2020 to compare the distribution pattern of this species over 20 years. Further, we evaluated the relative importance of environmental variables for the invasion of H. radicata using geographically weighted regression. Based on the results of our model, the expansion of H. radicata was found to be significantly associated with anthropogenic activities, such as human footprinting (HFP) and residential area, and the transportation system area. These results suggest that the primary dispersal forces of H. radicata are human activities, and that roads may be the main corridor of its expansion. Additionally, H. radicata did not invade the forest area over the 20 years. We can predict that this plant may flourish better around big cities because of human activities. Furthermore, deforestation and sporadic mowing can accelerate the expansion of H. radicata because of its preference for sunny spots.

Dynamic calibration of phytoplankton blooms using the modified SWAT model

Phytoplankton blooms in natural waters are critical for managing drinking water quality. Thus, estimating and understanding algal systems is important to supply safe drinking water and protect public health. The objectives of this study were to evaluate a modified algal module and calibrate algae-associated parameters annually (i.e., dynamic calibration) with identification of the relationship between parameters and environmental variables. The modified algal module in the Soil and Water Assessment Tool (SWAT) model incorporates temperature rate multipliers to correct the temperature effect on phytoplankton. Three different modules (i.e., original module, modified module, and dynamic calibration) were calibrated using 15-year observations in a watershed that provides drinking water resources. The original module resulted in a Nash-Sutcliffe model efficiency (NSE) of 0.334 and a root mean squared error (RMSE) of 0.578, but the modified module increased up to 0.374 and decreased to 0.561. In addition, the dynamic calibration of the modified module further improved the model performance with an NSE of 0.486 and RMSE of 0.508. The random forest method was used to investigate the relative importance of environmental variables to dynamic parameters. The maximum temperature was a relatively important variable for maximum algal growth, and the nutrient concentration was important for temperature multiplier factors. Dynamic calibration not only improved the simulation accuracy but also provided the relationship between environmental variables and algal parameters. This calibration method will be useful for simulating chlorophyll-a (Chl-a) dynamics and preparing watershed management policies for algal blooms.

Prediction of Geosmin at Different Depths of Lake Using Machine Learning Techniques.

Geosmin is a major concern in the management of water sources worldwide. Thus, we predicted concentration categories of geosmin at three different depths of lakes (i.e., surface, middle, and bottom), and analyzed relationships between geosmin concentration and factors such as phytoplankton abundance and environmental variables. Data were collected monthly from three major lakes (Uiam, Cheongpyeong, and Paldang lakes) in Korea from May 2014 to December 2015. Before predicting geosmin concentration, we categorized it into four groups based on the boxplot method, and multivariate adaptive regression splines, classification and regression trees, and random forest (RF) were applied to identify the most appropriate modelling to predict geosmin concentration. Overall, using environmental variables was more accurate than using phytoplankton abundance to predict the four categories of geosmin concentration based on AUC and accuracy in all three models as well as each layer. The RF model had the highest predictive power among the three SDMs. When predicting geosmin in the three water layers, the relative importance of environmental variables and phytoplankton abundance in the sensitivity analysis was different for each layer. Water temperature and abundance of Cyanophyceae were the most important factors for predicting geosmin concentration categories in the surface layer, whereas total abundance of phytoplankton exhibited relatively higher importance in the bottom layer.

Factors determining the structure of fish assemblages in Tarumã River, Jaru Biological Reserve, Machado River drainage, northern Brazil

ABSTRACT Few studies on fish assemblages and relations with environmental factors in aquatic systems in southeastern Amazonia have been carried out when compared to other areas in the Amazon. Therefore, which are the main environmental variables and processes responsible for structuring them remains unknown. We hypothesized that fish assemblages respond the variation in the physical-chemistry variables between seasons of the hydrological cycle in a pristine river in the Amazon. The study was performed on fish assemblages of the Tarumã River, Jaru Biological Reserve, Rondônia. Samplings were carried out in five sites along the river in March and September, 2015, which included fish collection and environmental data measurements. Principal component analysis was performed to ordinate the sites in high water and low water seasons, according to environmental variables. We used a similarity analysis in order to identify the individual contribution of species in hydrological period and a partial redundancy analysis for quantify the relative importance of environmental variables in the species composition. As predicted by our hypothesis, the species composition was influenced by dissolved oxygen and temperature. Myloplus rubripinnis, Serrasalmus compressus, and S. rhombeus were the most abundant during high water, while S. rhombeus, Myloplus lobatus, Prochilodus nigricans, and Hydrolycus armatus were the dominant species during the low water.

Genetic structure and life history are key factors in species distribution models of spiny lobsters.

AimWe incorporated genetic structure and life history phase in species distribution models (SDMs) constructed for a widespread spiny lobster, to reveal local adaptations specific to individual subspecies and predict future range shifts under the RCP 8.5 climate change scenario.LocationIndo‐West Pacific.MethodsMaxEnt was used to construct present‐day SDMs for the spiny lobster Panulirus homarus and individually for the three genetically distinct subspecies of which it comprises. SDMs incorporated both sea surface and benthic (seafloor) climate layers to recreate discrete influences of these habitats during the drifting larval and benthic juvenile and adult life history phases. Principle component analysis (PCA) was used to infer environmental variables to which individual subspecies were adapted. SDM projections of present‐day habitat suitability were compared with predictions for the year 2,100, under the RCP 8.5 climate change scenario.ResultsIn the PCA, salinity best explained P. h. megasculptus habitat suitability, compared with current velocity in P. h. rubellus and sea surface temperature in P. h. homarus. Drifting and benthic life history phases were adapted to different combinations of sea surface and benthic environmental variables considered. Highly suitable habitats for benthic phases were spatially enveloped within more extensive sea surface habitats suitable for drifting larvae. SDMs predicted that present‐day highly suitable habitats for P. homarus will decrease by the year 2,100.Main conclusionsIncorporating genetic structure in SDMs showed that individual spiny lobster subspecies had unique adaptations, which could not be resolved in species‐level models. The use of sea surface and benthic climate layers revealed the relative importance of environmental variables during drifting and benthic life history phases. SDMs that included genetic structure and life history were more informative in predictive models of climate change effects.

Boron availability in top- and sub-soils as affected by topography and climate

Available boron (B) is essential to the normal growth of crops. Previous studies on available B have focused on topsoil; hence, information about available B variation and its relationships with environmental variables (topography, climate, vegetation, soil property and parent material) in subsoil is limited. The current study collected 132, 124, and 87 soil samples, respectively, from A, B, and C horizons of arable land in purple hilly areas of southwestern China. Classical statistics, semivariogram analysis, and boosted regression trees (BRT) were applied to investigate available B variation and its affecting factors in various horizons. Samples of each soil horizon were randomly divided into calibration (80%) and validation (20%) sets. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were employed for evaluating model performance. Results indicated that available B content decreased with soil depth and showed the strongest spatial autocorrelation in the A horizon. Approximately 50%, 58%, and 51% of available B variability in the three horizons could be explained by the BRT models. Values of MAE varied between 0.038 (C horizon) and 0.053 mg kg−1 (A horizon), and RMSE changed between 0.048 (C horizon) and 0.069 mg kg−1 (A horizon). The relative importance of environmental variables to available B variability varied with soil horizons. Precipitation, flow path length, and topographical aspect were the most critical factors for the A, B, and C horizons, respectively. The importance of valley depth, elevation, and temperature enhanced, whereas precipitation and normalized difference vegetation index decreased in subsoil.

Prediction of the spatial distribution of soil arthropods using a random forest model: A case study in Changtu County, Northeast China

Soil arthropods with a body size of more than 2 mm are a significant component of terrestrial ecosystems and play an important role in biogeochemical cycles. Understanding the spatial distribution of soil arthropods is crucial for biodiversity conservation and land remediation. Studies on the spatial distribution of soil arthropods have mostly been performed at a small scale. However, due to the difficulties in obtaining soil arthropods, research on their spatial distribution patterns at a large scale is scarce and the details remain unclear. Therefore, it is important to clarify the spatial distribution pattern of soil arthropods at a large scale and the influence of environmental variables on them to develop targeted protection measures. In this study, we used a random forest model combined with environmental variables to map the spatial distribution of soil arthropods in cultivated land in Changtu County, China, and evaluated the prediction accuracy of the model. In addition, we ranked the relative importance of environmental variables to understand their influence on the spatial distribution of soil arthropods. The results showed the following: 1) The mean absolute prediction error was 13.9, the root mean square error was 21.5, R2 was 0.53, and Lin’s concordance correlation coefficient of the model was 0.48, which indicated that random forest can be used to predict the spatial distribution of soil arthropods at a large scale. 2) The nature index of agricultural land, which represents the comprehensive quality of cultivated lands, and landscape division index were the two most important environmental variables affecting the spatial distribution of soil arthropods, with relative importance values of 19.3 % and 12.9 %, respectively. 3) The number of soil arthropods was low in the study area and the soil arthropods were aggregated in their distribution. We believe that accurately predicting the distribution of soil arthropods can aid in the rational distribution and management of farmland landscape and the protection of soil arthropods.

Influence of Anthropogenic Noise for Predicting Cinereous Vulture Nest Distribution

Natural landscapes are increasingly under anthropogenic pressures, and concern about human impacts on wildlife populations is becoming particularly relevant in the case of natural areas affected by roads. The expansion of road networks is considered among the main factors threatening biodiversity due to their potential for disturbing natural ecosystems on large scales. Indeed, traffic noise pollution reduces the quantity and the quality of natural habitats, and umbrella species are frequently used as indicators of natural ecosystem health. In this sense, there is a variety of GIS-based ecological modeling tools that allow evaluation of the factors that influence species distributions in order to accurately predict habitat selection. In this study, we have combined the use of noise modeling tools and maximum entropy modeling (MaxEnt) to evaluate the relative importance of environmental variables for Cinereous vulture (Aegypius monachus) nesting habitat selection within a mountainous forest in Spain. As a result, we found that spatial negative influence of roads on wildlife due to road traffic disturbance may have been traditionally overestimated when it has been inferred from distance measurements of wildlife behavior in road surroundings instead of from considering road traffic noise level exposure. In addition, we found a potential risk threshold for cinereous vulture breeding around roads, which ties in with a Leq24h level of 40 dB(A). This may be a useful indicator for assessing the potential impact of human activities on an umbrella species such as, for instance, the cinereous vulture, whose breeding does not take place where road traffic Leq24h levels are higher than 40 dB(A).