Predictor Of Species Richness Research Articles

Global models and predictions of plant diversity based on advanced machine learning techniques.

Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation. Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions. Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity. Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology.

Predicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning

Spatial predictions of biomass production and biodiversity at regional scale in grasslands are critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can predict these grassland characteristics with varying accuracy. However, such studies frequently fail to cover a sufficiently broad range of environmental conditions, and their prediction models are often case-specific. To address this gap, we have modelled above-ground biomass and species richness in 150 spatially independent grassland plots of three geographical regions in Germany. These regions follow a North-South climate gradient and differ in soil types, topography, elevation, climatic conditions, historical contexts, and management intensities. The predictors tested in this study are Sentinel-1 backscatter, Sentinel-2 time series of surface reflectance along with derived vegetation indices and Rao's Q, and a set of topoedaphic variables. We compared the performance of a feed-forward deep neural network (DNN) with a random forest (RF) regression algorithm. The DNN achieved the best estimations of biomass (r2 = 0.45) when trained with Sentinel-2 surface reflectance only. Moreover, the DNN showed a higher generalizability than RF during spatial cross-validations (i.e., calibrating and validating in different regions, r2 = 0.38 vs. 0.26). Species richness predictions by both algorithms improved when the full time series of Sentinel-2 surface reflectance values were used (highest r2 = 0.42 achieved by the DNN), but both performed poorly during spatial cross-validations. Overall, the DNN-based models were more robust than RF models, showed a lower bias and lower systematic error, and required fewer inputs. Explainability analysis indicated that red-edge and near infrared information from May and October was the most relevant to predict species richness. This study presents an important step forward in generating robust spatially explicit predictions of grassland attributes and biodiversity variables across large areas, environmental gradients, and phenological stages.

Vegetation complexity and pool size predict species richness of forest birds

Disentangling regional and local drivers of species richness in communities is a long-term focus of ecology. Regional species pools affect local communities by providing their constituent species. Additionally, the amount and variety of resources enhance diversity locally. Here, we investigated whether the same ecological factor (vegetation complexity) shapes both regional and local species richness and thus drives local diversity both indirectly (via pool size) and directly (via facilitating the coexistence of species). We studied passerine birds of woodlands and forests in eastern Australia. We quantified regional species pool size and sampled local bird communities at 63 transects spanning 3,000 km. We estimated canopy height both regionally using satellite imagery and locally using vegetation sampling in the field. We studied how species pool size changed with regional canopy height and water availability, and how local species richness changed with pool size and local canopy height. Local species richness increased with both local canopy height and the size of the regional species pool. Pool size, in turn, increased with regional canopy height, which itself increased with water availability. Moreover, local species richness expressed as a proportion of the regional pool also increased with local canopy height. In sum, vegetation complexity indexed by canopy height had a doubly positive effect on local species richness: indirectly by promoting a large regional species pool and directly by facilitating the coexistence of disproportionately many species locally. Regional pools were larger in tall forests probably due to the legacy of extensive moist forests that once covered most of Australia, thus providing a sizeable potential for speciation, diversification, and species persistence. Local species richness was greater in tall, more productive forests with more vegetation layers likely due to more and varied resources (i.e., more potential niches), allowing the coexistence of more individuals and species of consumers.

Insights on biodiversity drivers to predict species richness in tropical forests at the local scale

Disentangling the relative importance of different biodiversity drivers (i.e., climate, edaphic, historical factors, or human impact) to predict plant species richness at the local scale is one of the most important challenges in ecology. Biodiversity modelling is a key tool for the integration of these drivers and the predictions generated are essential, for example, for climate change forecast and conservation planning. However, the reliability of biodiversity models at the local scale remains poorly understood, especially in tropical species-rich areas, where they are required. We inventoried all woody plants with stems ≥ 2.5 cm in 397 plots across the Andes-Amazon gradient. We generated and mapped 19 uncorrelated biodiversity drivers at 90 m resolution, grouped into four categories: microclimatic, microtopographic, anthropic, and edaphic. In order to evaluate the importance of the different categories, we grouped biodiversity drivers into four different clusters by categories. For each of the four clusters of biodiversity drivers, we modelled the observed species richness using two statistical techniques (random forest and Bayesian inference) and two modelling procedures (including or excluding a spatial component). All the biodiversity models produced were evaluated by cross-validation. Species richness was accurately predicted by random forest (Spearman correlation up to 0.85 and explained variance up to 67%). The results suggest that precipitation and temperature are important driving forces of species richness in the region. Nonetheless, a spatial component should be considered to properly predict biodiversity. This could reflect macroevolutionary underlying forces not considered here, such as colonization time, dispersal capacities, or speciation rates. However, the proposed biodiversity modelling approach can predict accurately species richness at the local scale and detailed resolution (90 m) in tropical areas, something that previous works had found extremely challenging. The innovative methodology presented here could be employed in other areas with conservation needs.

Multi-factor biotopes as a method for detailed site characterization in diverse benthic megafaunal communities and habitats in deep-water off Morro Bay, California

Here we describe the methods and results for biological characterization of the benthos on a previously unexplored area of central California, USA seafloor. We conducted 40 remotely operated vehicle dives from 371 to 1173 m water depth. Seafloor habitats and megafauna (fish and invertebrates) were documented from 46.8 km of seafloor video footage. Our expanded development and analysis of biotopes from quantitative data allowed us to describe detailed biological communities, along with the physical characteristics and habitat associations within the study area. This method provides a framework for potential monitoring, detection of future environmental change (natural and anthropogenic) and comprehensive comparison to other deep water regions. From 185 h of observational video at 25 sites, nearly 120,000 annotations of organisms, habitat characters, biological detritus and anthropogenic debris were recorded and analyzed. We identified a total of 228 taxa, with 173 of them present on linear quantitative transects. Species richness for transects ranged from 0.04 to 0.28 m-2 (8–55 taxa), with densities ranging from 0.07 to 5.20 ind. m−2. Both were highest on hard substrate with greatest surface area. Densities decreased with depth. Within soft substratum zones was a large field of pockmarks, which are seafloor depressions averaging 175 m in diameter and 5 m in depth. Pockmarks have sometimes been associated with seafloor gas seepage, but here we found no biological evidence of chemosynthetic organisms. No significant differences were found in either density nor species richness at pockmark sites vs. non-pockmark sites. Mud draped greenish-black coarse sand occurred only in low oxygen areas, while hummocky, rugose mud supported somewhat different species than flat mud plains. Seventy percent of the transects occurred inside the oxygen minimum zone. We conclude that high rugosity, slope, and the presence of hard substratum were better predictors of species richness and density than oxygen concentration in this specific study. Abundant biological detritus, in the form of dead and dying pelagic pyrosomes and salps, created a large, presumably ephemeral flux of carbon to the seafloor during the study period.

Simple Assessment of Temperate Grassland Suitability as Habitat for Three Insect Taxa

We propose a set of indicators to evaluate how grazing by livestock and grassland management can help preserve biodiversity in the insect communities of grassland ecosystems. These indicators can be calculated for any grassland for which management and botanical composition are known, and they do not require advanced knowledge on conservation biology and entomology. Based on the scientific literature and expert interviews, each indicator combines pressure (i.e., 16 management classes) and state variables (i.e., sward botanical composition and several of the plant functional characteristics) to assess the effects on butterfly, bumblebee, and grasshopper abundance and species richness. Each indicator was based on a multicriteria decision tree with fuzzy partitioning to account for uncertainty in the threshold values between different alternatives. The output validation of indicators was performed by comparing decision tree output with standardized measurements from 10 surveys across France, Germany, Switzerland, Wales, and Italy. The prediction of grassland suitability for butterflies was more accurate for butterfly species richness (P < 0.05) than for butterfly abundance (P = 0.10), as even under standardized transect counts, recorders will observe disproportionately more individuals when it is sunny and warm. The sensitivity of the butterfly species richness indicator was then tested using an independent dataset of 395 French grasslands. There were significant differences in predicted species richness (χ215 = 121.16; P < 0.05) among the 16 management classes, and they were ranked consistently in spite of very parsimonious assumptions in the decision tree. The prediction of grassland suitability was good for bumblebee abundance (P < 0.05) and species richness (P < 0.01). This prediction was also successful for grasshopper abundance (P < 0.05) and species richness (P = 0.05), all outliers coming from a highly diverse grassland community located on shallow soil in the Italian survey. This set of indicators can thus provide a cost-effective evaluation of temperate grassland suitability as habitat for three insect taxa. These indicators have more relevance than general insect species richness, and their application does not require any entomological skill. The indicators can help stakeholders to make adequate decisions for insect conservation in grassland ecosystems, but do not have direct applicability to higher conservation value taxa or species.

Estimating Alpha, Beta, and Gamma Diversity Through Deep Learning.

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

Geographic context is a key driver of spatial variation of bird species richness during migration

AbstractAimDetermine whether primary productivity or location (distance to the coast) is more important to migrating bird habitat selection.LocationYucatan Peninsula, Mexico.Time periodAugust–December and February–May 2009–2016.TaxaMigratory birds.MethodsUsing eBird data, we modelled spatial variation in species richness as a function of primary productivity and distance to the coast and how the relationships vary with time during spring and autumn migration. We compared the standardized regression coefficients of linear models with a Poisson error distribution.ResultsWe found that three primary productivity indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), heterogeneity] performed similarly in predicting species richness. However, species richness was more strongly associated with distance to the coast. In autumn, richness was higher closer to the coast through most of the season and then shifted inland late in autumn. Richness was also higher in more productive habitats. In spring, richness was higher inland early in the season but then increased closer to the coast. Species richness was relatively similar across the primary productivity gradient, even declining slightly with increasing NDVI.Main conclusionsWe did not find convincing evidence for a strong relationship between species richness and primary productivity. Instead, migration constrains birds such that they concentrate in coastal environments. In autumn, migrants may favour habitats that are both coastal and productive, such as those that occur along the Caribbean coast. In spring, migrants may also use the less productive Gulf of Mexico coast, possibly due to high arthropod production occurring during an otherwise dry time of year.

Avifaunal feeding guilds’ response to landscape compositional heterogeneity and their drivers in forest mosaic, Uttarakhand, India

AbstractThe positive association between landscape compositional heterogeneity and avian species richness is widely accepted, however, birds of different feeding guilds are expected to respond differently because of diverse resource utilisation patterns and ecological tolerance. In the present study, we assessed the response of avian species and avifaunal feeding guilds to landscape compositional heterogeneity and factors associated with their richness. Bird richness and landscape variables, edge density, landscape diversity, and area of land cover types were evaluated at 30 sampling sites in the Terai-Arc landscape of Uttarakhand, India. Univariate regression was performed to investigate the response of birds and various feeding guilds to landscape compositional heterogeneity. Average weighted models of most parsimonious generalised linear regression models (<Δ 2AICc) were developed for various feeding guilds to identify significant predictors of species richness. The richness of overall birds and most feeding guilds, except piscivores and frugivores, responded positively to landscape compositional heterogeneity at variable spatial scales. The scale of effect was largest for carnivore (1.5 km), followed by granivore (1 km), insectivore (0.75 km), and frugi-insectivore, nectarivore, and omnivore (0.5 km). Overall bird species richness was positively associated with landscape diversity, teak plantation, and Sal-mixed forest. The average-weighted models identified edge density and dry riverine forest for frugi-insectivore, barren land and water body for the carnivore, teak plantation, Sal-mixed forest and dry-riverine forest for insectivore, edge density, human habitation/agriculture, teak plantation, barren land and scrubland for granivore, human habitation/agriculture for omnivore and waterbodies for piscivore and frugivore guilds as the significant drivers of species richness. The study concludes that the response to landscape compositional heterogeneity differs among feeding guilds and varies with the spatial scale of analysis. The results of our study are expected to serve as a reference for future studies, exploring the landscape relationship to the avian community in similar environmental conditions.

On the efficiency of indicator species for broad-scale monitoring of bird diversity across climate conditions

Multiple constraints (e.g., time, funding, expertise) can impede the broad-scale monitoring of human-induced loss of biodiversity. Non-random species co-occurrence provides an opportunity to estimate local species richness by tracking only a few species that are indicators of more global assemblages. Despite promising fine-scale studies, it remains unclear whether such indicator species remain effective over broad spatial extents. We assessed the value of indicator species to consistently predict species richness despite variation in short-term (5–9 years) climate conditions. Our study involves 22,041 point-count stations distributed over 1.75 M km2 of Canadian boreal forest, where 197,000 birds from 216 species were detected. Using null model analysis of species co-occurrence followed by regression analysis, we identified the minimum set of indicator species that can predict 70% of spatial variation in local bird richness in each of eight short-term climate regions. We found that indicators were non-random subsets of the species pool, as they were species whose presence explained a relatively high percentage of variations in species richness within the short-term climate region, and those sharing a relatively high number of significant pairwise associations. Although only 11 to 26 indicator species were needed to predict species richness at the regional scale, 57 of the 216 species pool (26%) were necessary to make predictions over the entire study area. This large number reflects regional variations in the best indicator species, and those that remained indicators in several regions were representative of different species assemblages. Our observations thus cast doubts on the use of indicator species as an effective and efficient tool for biodiversity monitoring under changing climate conditions. Broad-scale (spatial or temporal) use of indicators thus comes with the colossal challenge of having to determine under which new conditions a given set of indicators must be replaced by another, and by which one.

Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands

AbstractSpecies occur in sympatric assemblages, bound together by ecological relationships and interspecific interactions. Borneo and Sumatra host some of the richest assemblages of biota worldwide. The region, however, faces the highest global deforestation rates, which seriously threaten its unique biodiversity. We used a large camera trap dataset that recorded data for 70 terrestrial species of mammals and birds, to explore the drivers of regional species richness patterns. Using a multi‐scale, multivariate modelling framework which quantified the main environmental factors associated with patterns of biodiversity, while simultaneously assessing individual relationships of each species, we determined the ecological drivers of sampled biodiversity, and their contributions to community assemblages. We then mapped predicted species richness, evaluated the effectiveness of protected areas in securing biodiversity hotspots, performed gap analysis to highlight biodiverse areas lacking protection and compared our predictions with species richness maps produced by using IUCN range layers. Finally, we investigated the performance of each species as an indicator of sampled biodiversity. We demonstrate that biodiversity in Borneo and Sumatra is primarily affected by gradients of ecological and anthropogenic factors, and only marginally by topographic and spatial factors. In both islands, species are primarily associated with elevational gradients in vegetation and climate, leading to altitudinal zonation in niche separation as a major factor characterizing the islands' biodiversity. Species richness was highest in north‐eastern Borneo and in western Sumatra. We found that most predicted biodiversity hotspots are not formally protected in either island; only 9.2 and 18.2% of the modelled species richness occurred within protected areas in Borneo and Sumatra, respectively. We highlighted that our prediction for Borneo performed better than, and differed drastically from, the IUCN species richness layer, while for Sumatra our modelled species richness layer and the IUCN one were similar, and both showed low predictive power. Our analysis suggests that common and generalist carnivores are the most effective indicators of sampled biodiversity and have high potential as focal, umbrella or indicator species to assist multi‐species vertebrate conservation planning. Understanding existing drivers and patterns of biodiversity is critical to support the development of effective community conservation strategies in this rapidly changing region.

Effects of Environmental Changes on Gerromorpha (Heteroptera: Hemiptera) Communities from Amazonian Streams

The presence of environmental conditions that are suitable for the development of agricultural and cattle-raising activities has favored the expansion of land use in the Brazilian Amazon. This expansion changes the environmental conditions of aquatic ecosystems by altering the structure of the streambeds and the physicochemical conditions of the water. Therefore, this manuscript aimed to assess the effect of changes in environmental conditions along a gradient of different land uses on the richness and species composition of Gerromorpha (Heteroptera: Hemiptera) communities in streams from the Brazilian Amazon. We sampled Gerromorpha communities and environmental variables (physical structure of the habitat and physicochemical characteristics of the water) in 45 streams in the municipality of Paragominas in the state of Pará, Brazil. We then performed variation partitioning analyses to assess the relative contribution of habitat structure and water physicochemical variables on the richness and species composition. The Habitat Integrity Index (HII), which measures the habitat physical structure, was the best predictor of species richness. The HII and physicochemical variables were jointly the best predictors of species composition. Our results show that Gerromorpha communities respond to gradients of environmental anthropization in the Amazon region, which reinforces the importance of habitat integrity and water quality for the maintenance of Gerromorpha communities.

The use of highly diverse clades as a surrogate for habitat integrity analysis: Argia damselflies as a practical tool for rapid assessments.

Human activities have impacted many environments on earth, and thus several species are facing an increased risk of extinction. The environmental crisis requires rapid tools to assess the ecosystem health accurately. Studies have been conducted with visual indices that quantify habitat integrity by predicting species richness and diversity. However, whether a diverse clade can predict habitat integrity has not been used. The genus Argia (Rambur, 1842) is one of the most locally diverse groups in southeastern Mexico. In this context, we hypothesized that the occurrence, species richness, and diversity of adults Argia spp. could be a better predictor of the Visual-Based Habitat Assessment Score (VBHAS) than the other taxonomic levels or less diverse clades. We found that the richness and diversity of Argia spp. are positively correlated with VBHA scores, as same as taxonomic ratios. Simultaneously, VBHA scores increase to 23.51 times when Argia spp. diversity increases. We discuss the possible use of a diverse Odonata clade, as Argia spp. could surrogate habitat integrity for local long-term biomonitoring programs. This approach requires testing with other indices and verifying a reliable and consistent relationship between diverse clades and environmental assessment scores.

Priority maps for pollinator habitat enhancement schemes in semi-natural grasslands

Conserving semi-natural grasslands, a threatened habitat type in European landscapes, is increasingly recognized as a measure to conserve pollinators. Our aim was to test if (a) prediction maps of solitary bee species richness could be used to rank semi-natural grasslands in terms of their potential for supporting wild bees, and (b) if such predictions extend current assessment criteria that determine which grasslands are eligible for being listed under habitat conservation schemes. We sampled wild bee communities in 52 semi-natural grasslands in southeast Norway. We conducted an across-year validation, using data from 2019 (32 sites) to model bee species richness, and used data from 2020 (20 sites) to validate predictions. We then conducted a leave-one-out cross-validation, iteratively using data from 51 sites to parameterize our model, and validating predictions on the withheld site. Finally, we used data from all 52 sites to update the model and tested if predicted species richness within the 1075 grasslands in our region was reflected in current assessment criteria scores assigned to those grasslands. Models from across-year, and leave-one-out cross-validations, predicted 39%, and 43% of bee species richness in semi-natural grasslands, respectively. Model predictions and current criteria of semi-natural grassland quality were not strongly related (R2adjusted = 0.01), suggesting that prediction models can add a valuable extra dimension when prioritizing between semi-natural grassland for pollinator habitat conservation. Our findings illustrate how spatial prediction models can provide management authorities with a valuable tool for prioritizing where to direct habitat enhancement schemes in order to improve conservation effectiveness.

Jointly modeling marine species to inform the effects of environmental change on an ecological community in the Northwest Atlantic

Single species distribution models (SSDMs) are typically used to understand and predict the distribution and abundance of marine fish by fitting distribution models for each species independently to a combination of abiotic environmental variables. However, species abundances and distributions are influenced by abiotic environmental preferences as well as biotic dependencies such as interspecific competition and predation. When species interact, a joint species distribution model (JSDM) will allow for valid inference of environmental effects. We built a joint species distribution model of marine fish and invertebrates of the Northeast US Continental Shelf, providing evidence on species relationships with the environment as well as the likelihood of species to covary. Predictive performance is similar to SSDMs but the Bayesian joint modeling approach provides two main advantages over single species modeling: (1) the JSDM directly estimates the significance of environmental effects; and (2) predicted species richness accounts for species dependencies. An additional value of JSDMs is that the conditional prediction of species distributions can use not only the environmental associations of species, but also the presence and abundance of other species when forecasting future climatic associations.

Abundance and Diversity of Woody Undergrowth Reservoir as Indicator of Suitable Vegetation Patch for Natural Regeneration

The diversity, abundance and height structure of naturally regenerated woody species were analyzed and compared for undergrowth reservoir associated with vegetation patches as bowal, shrubland and woodland for understanding the effect of vegetation conditions on spatial distribution of woody plants in Sudanian ecosystem. All undergrowth individuals (height 2) laid out in each vegetation patch during rainy season. In total 52 undergrowth species and 2224 stems arranged in 38 genera and 21 families out of which 25, 36 and 38 species were found in bowal, shrubland and woodland respectively and 15 shared species. Combretaceae, Caesalpiniaceae and Mimosaceae families were the most abundant and species density was significantly higher (P < 0.05) in shrubland and woodland than bowal, and increase diversity in only shrubland (P = 0.002) comparing with bowal. Undergrowth was abundantly concentrated in small height class as canopy benefit and indicate a specific-site distribution. Results also showed that bowal had a particular vegetation which vary in function of tree community characters in place (P < 0.05) while woodland and shrubland are similar vegetation patches. As conclusion, the study vegetation patches form suitable regeneration niches for the undergrowth reservoir of limited number of species which in return can be predictor of species richness and abundance and thus the structure of tree community. Woody undergrowth community could be considered in biodiversity management and degraded land restoration. Supplementary works could be undertaken to understand the characteristics of seed bank for bowal, shrubland and woodland regarding the undergrowth and tree communities.

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Plecoptera (Insecta) Diversity in Indiana: A Watershed-Based Analysis

Plecoptera, an environmentally sensitive order of aquatic insects commonly used in water quality monitoring is experiencing decline across the globe. This study addresses the landscape factors that impact the species richness of stoneflies using the US Geological Survey Hierarchical Unit Code 8 drainage scale (HUC8) in the state of Indiana. Over 6300 specimen records from regional museums, literature, and recent efforts were assigned to HUC8 drainages. A total of 93 species were recorded from the state. The three richest of 38 HUC8s were the Lower East Fork White (66 species), the Blue-Sinking (58), and the Lower White (51) drainages, all concentrated in the southern unglaciated part of the state. Richness was predicted using nine variables, reduced from 116 and subjected to AICc importance and hierarchical partitioning. AICc importance revealed four variables associated with Plecoptera species richness, topographic wetness index, HUC8 area, % soil hydrolgroup C/D, and % historic wetland ecosystem. Hierarchical partitioning indicated topographic wetness index, HUC8 area, and % cherty red clay surface geology as significantly important to predicting species richness. This analysis highlights the importance of hydrology and glacial history in species richness of Plecoptera. The accumulated data are primed to be used for monograph production, niche modeling, and conservation status assessment for an entire assemblage in a large geographic area.

Impacts of vegetation properties and temperature characteristics on species richness patterns in drylands: Case study from Xinjiang

Energy availability at trophic and hydrologic level dominates species richness gradients by constraining food resources, and regulating population sizes and extinction rates. Remote sensing datasets have mapped vegetation productivities as a proxy for energy availability, for example, using Dynamic Habitat Indices (DHIs). Considering the sparse vegetation across drylands, we developed indices of Land surface temperature (ILST) based on daytime land surface temperature from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), which has three components: (1) annual mean temperature (LSTmean), (2) annual maximum temperature (LSTmax), and (3) standard deviation of temperature (LSTstd). We hypothesized that the temperature variables, such as ILST, would predict species richness better than productivity proxies across drylands. Thus, our objective was to determine how well they would predict the richness of plants, mammals and birds across the Xinjiang Uygur Autonomous Region. We calculated the DHIs and ILST from the MODIS vegetation and temperature products from 2001 to 2015. We found that: (1) ILST could capture more additive information compared with DHIs in terms of the relatively high variance explanation of species richness and high variable importance, and the combination of ILST and DHIs gave better predictions than single metrics for species richness patterns. (2) Plants and birds were more sensitive to temperature than vegetation productivity, probably due to physiological tolerance and evolutionary processes. (3) LSTstd was the most important variable affecting species richness, except on mammals. High LSTstd was related to more food resources and habitats, and low LSTstd represented extreme environment and environmental stress. Combined vegetation properties and temperature variabilities are good determinants of species richness, and should be carefully considered in future research.

Climate change effects on multi-taxa pollinator diversity and distribution along the elevation gradient of Mount Olympus, Greece

Climate change is predicted to dramatically affect mountain biodiversity and especially mountain pollination systems due to the mutual dependence between plants and pollinators. In this work, we investigate climate change effects on pollinator distribution and diversity along the altitudinal gradient of Mt. Olympus, a biodiversity hotspot.We used a species distribution modelling framework and predicted species richness hotspots, potential distribution and altitude change for 114 pollinator species, comprising bees, butterflies, and hoverflies along the altitudinal gradient (327–2596 m a.s.l.).We projected potential loss of suitable habitat and upward shift for most pollinator groups, with the exception of bumblebees and hoverflies which were predicted to descend. Pollinator extinctions were not forecasted; instead, we observed a pronounced species-specific response to climate change. Species richness hotspots will be relocated to higher altitudes and to the north-eastern mountain side.Projections for substantial but not detrimental climate change effects on pollinator fauna may be due to species differential resilience to climate change along with the existence of microrefugia on Mt. Olympus. Divergent response to global warming by bumblebees and hoverflies is probably due to species distribution modelling limitations, resulting in exclusion of the rarest species. We conclude that the predicted climate change impact stresses for the need of urgent conservation measures, including the expansion of the protection status over the whole mountain.