Predictor Of Species Richness Research Articles

Predicting the responses of European grassland communities to climate and land cover change.

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Tree-Related Microhabitats and Multi-Taxon Biodiversity Quantification Exploiting ALS Data

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation.

Testing for concordance between predicted species richness, past prioritization, and marine protected area designations in the western Indian Ocean.

Scientific advances in environmental data coverage and machine learning algorithms have improved the ability to make large-scale predictions where data are missing. These advances allowed us to develop a spatially resolved proxy for predicting numbers of tropical nearshore marine taxa. A diverse marine environmental spatial database was used to model numbers of taxa from ∼1000 field sites, and the predictions were applied to all 7039 6.25-km2 reef cells in 9 ecoregions and 11 nations of the western Indian Ocean. Our proxy for total numbers of taxa was based on the positive correlation (r2=0.24) of numbers of taxa of hard corals and 5 highly diverse reef fish families. Environmental relationships indicated that the number of fish species was largely influenced by biomass, nearness to people, governance, connectivity, and productivity and that coral taxa were influenced mostly by physicochemical environmental variability. At spatial delineations of province, ecoregion, nation, and strength of spatial clustering, we compared areas of conservation priority based on our total species proxy with those identified in 3 previous priority-setting reports and with the protected area database. Our method identified 119 locations that fit 3 numbers of taxa (hard coral, fish, and their combination) and 4 spatial delineations (nation, ecoregion, province, and reef clustering) criteria. Previous publications on priority setting identified 91 priority locations of which 6 were identified by all reports. We identified 12 locations that fit our 12 criteria and corresponded with 3 previously identified locations, 65 that aligned with at least 1 past report, and 28 that were new locations. Only 34% of the 208 marine protected areas in this province overlapped with identified locations with high numbers of predicted taxa. Differences occurred because past priorities were frequently based on unquantified perceptions of remoteness and preselected priority taxa. Our environment-species proxy and modeling approach can be considered among other important criteria for making conservation decisions.

Patterns of Grewia (Malvaceae) diversity across geographical scales in Africa and Madagascar.

Quantifying spatial species richness is useful to describe biodiversity patterns across broad geographical areas, especially in large, poorly known plant groups. We explore patterns and predictors of species richness across Africa in one such group, the palaeotropical genus Grewia L. (Malvaceae). Grewia species richness was quantified by extracting herbarium records from GBIF and Tropicos and creating geographical grids at varying spatial scales. We assessed predictors of species richness using spatial regression models with 30 environmental variables. We explored species co-occurrence in Madagascar at finer resolutions using Schoener's index and compared species range sizes and International Union for Conservation of Nature status among ecoregions. Lastly, we derived a trait matrix for a subset of species found in Madagascar to characterize morphological diversity across space. Grewia species occur in 50 countries in Africa, with the highest number of species in Madagascar (93, with 80 species endemic). Species richness is highest in Madagascar, with ≤23 Grewia species in a grid cell, followed by coastal Tanzania/Kenya (≤13 species) and northern South Africa and central Angola (11 species each). Across Africa, higher species richness was predicted by variables related to aridity. In Madagascar, a greater range in environmental variables best predicted species richness, consistent with geographical grid cells of highest species richness occurring near biome/ecoregion transitions. In Madagascar, we also observe increasing dissimilarity in species composition with increasing geographical distance. The spatial patterns and underlying environmental predictors that we uncover in Grewia represent an important step in our understanding of plant distribution and diversity patterns across Africa. Madagascar boasts nearly twice the Grewia species richness of the second most species-rich country in Africa, which might be explained by complex topography and environmental conditions across small spatial scales.

Present status, future trends, and control strategies of invasive alien plants in China affected by human activities and climate change

Invasive alien plants (IAPs) have serious environmental and economic impacts, especially in vulnerable areas of China. However, IAP richness distribution patterns, their driving factors, and the dynamic shifts in potential distribution areas remain elusive. We assessed IAP richness distribution patterns and drivers using 402 IAPs recorded in China at 88 926 occurrence points, and then predicted their potential distribution areas. The results show that IAP hotspots were mainly located in southeastern China, especially coastal areas of the South and East and large inland cities. Population density, gross domestic product (GDP), and four climate variables associated with precipitation and temperature jointly influenced the richness distribution pattern of all IAPs. Specifically, population density and GDP impacted the richness distribution pattern of narrow‐range IAPs, and population density, GDP, distance to the nearest national highway, and five climate variables affected the richness distribution pattern of widespread IAPs. Only GDP contributed significantly to the richness distribution pattern of the top 5% hotspot grid cells, whereas population density, GDP, and precipitation in the driest month (BIO14) significantly influenced the richness distribution patterns of hotspots for both the top 10% and top 20%. Prediction analysis demonstrated that southeastern China would have particularly high invasion risk under both current and future climate scenarios. Regions with increases in predicted species richness are more common (44.83–64.97%) than those with decreases, except under the Representative Concentration Pathway (RCP) 4.5 scenario. Climate change will contribute greatly to the expansion of potential IAP distribution areas under both optimistic (RCP 2.5) and pessimistic scenarios (RCP 8.5). The results of this study provide insights into the priority management of IAPs by developing promising strategies for the control and prevention of IAP invasion.

Stacked machine learning models for predicting species richness and endemism for Mediterranean endemic plants in the Mareotis subsector in Egypt

An effective method for identifying species and evaluating the effects of changes caused by humans on specific species is the application of species distribution modelling (SDM) in desert environments. The fact that many dry lands and deserts throughout the world are situated in inhospitable regions may be the reason why such applications are still infrequently used on plant species in Egypt's Mediterranean region. Henceforth, the current study aims to map species richness and weighted endemism of Mediterranean endemics in the Mareotis subsector in Egypt and determine the environmental variables influencing distribution of these taxa. We produced a map of species distribution range using Ensemble SDMs. Further, stacked machine learning ensemble models derived from Random Forest (RF) and MaxEnt models were applied on 382 Mediterranean endemics distribution data to estimate and map diversity and endemism using two indices: species richness (SR) and weighted endemism index (WEI). The best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The results showed that the models had a good predictive ability (Area Under the Curve (AUC) for all SDMs was > 0.75), indicating high accuracy in forecasting the potential geographic distribution of Mediterranean endemics. The main bioclimatic variables that impacted potential distributions of most species were wind speed, elevation and minimum temperature of coldest month. According to our models, six hotspots were determined for Mediterranean endemics in the present study. The highest species richness was recorded in Sallum, Matrouh wadis and Omayed, followed by Burg El-Arab, Ras El-Hekma and Lake Mariut. Indeed, species richness and endemism hotspots are promising areas for conservation planning. This study can help shape policy and mitigation efforts to protect and preserve Mediterranean endemics in the coastal desert of Egypt. These hotspots should be focused on by policy makers and stakeholders and declared as protectorates in the region. The largest number of species per area would be protected by focusing primarily on the hotspots with high species richness.

Temporally stacked bee forage species distribution modeling for flower abundance mapping

Predicting spatial distribution of flowering forage availability is critical for guiding migratory beekeeping decisions.Species distribution modelling (SDM) is widely used to predict the geographic distribution or species ranges. Stacked distributions of multiple species (S-SDM) have been used in predicting species richness or assemblages. Here, we present a method for stacking SDMs based on a temporal element, the flowering phenology of melliferous flora species. First, we used presence-only data for thirty key forage species used for honey production in Western Australia, combined with environmental variables for predicting the geographic distribution of species, using MaxEnt software. The output distribution grids were then stacked based on monthly flowering times of each species to develop grids representing the richness of flowering species by grid cell. While designed for modelling flowering forage availability for a migratory beekeeping system, the approach can be used for predicting temporal forage availability for a range of different fauna that rely on melliferous flora.•How to use temporally stacked species distribution modelling for generic distribution of flowering availability using presence-only data.•A procedure for developing flowering richness and availability grids.

Poor performance of acoustic indices as proxies for bird diversity in a fragmented Amazonian landscape

Biodiversity loss is rampant worldwide, particularly in tropical regions like the Amazon. In the last decade, acoustic indices have been proposed as a rapid method for biodiversity assessment. However, their overall effectiveness as proxies for biodiversity is under debate. Here, we tested advanced statistical methods based on acoustic indices recently proposed to estimate species richness more accurately. Using an annotated audio dataset (2356 one-minute files) and song characterization of tropical bird assemblages from land-bridge islands in an Amazonian hydroelectric reservoir, we fitted both regression models and random forest algorithms to address the following questions: (1) do acoustic indices provide accurate estimates of bird species richness? (2) are univariate (a single index) or multivariate models (a combination of indices) better at predicting species richness? (3) at what temporal scale (minutes or hours) and with which measures (raw values or mean and standard deviation values)? (4) do these indices reflect spatial (island size) and temporal patterns (diel cycle) of singing activity? Although we found that multivariate models using a set of acoustic indices computed at a broader scale (hours) performed better than simpler models, their overall predictive power of species richness was poor for these tropical bird assemblages. The high heterogeneity and variation in the acoustic activity and signals of the Amazonian bird species present a considerable challenge for acoustic indices to capture changes in species diversity adequately. In agreement with recent studies, our findings point out the limits of acoustic indices, especially in tropical, highly diverse regions, emphasizing that caution should be used when applying this type of acoustic indices in biodiversity assessment. In contrast, all tested indices reflected distinct spatial and temporal patterns that were often related to habitat features (i.e. island size) and animal activity (i.e. choruses), supporting alternative (large-scale) applications of acoustic indices. Random forest algorithms confirmed the potential to classify island size based on soundscape characteristics. These findings suggest acoustic indices can capture differences in assemblage composition and bird activity along a habitat fragmentation gradient. Hence, they can more efficiently assess habitat and community structure than species diversity, occurrence, or abundance.

MP15-19 DETERMINING DIFFERENCES IN CLINICALLY INFECTED VERSUS NONINFECTIOUS BIOFILM AT THE TIME OF REVISION SURGERY USING NEXT GENERATION SEQUENCING

You have accessJournal of UrologyCME1 Apr 2023MP15-19 DETERMINING DIFFERENCES IN CLINICALLY INFECTED VERSUS NONINFECTIOUS BIOFILM AT THE TIME OF REVISION SURGERY USING NEXT GENERATION SEQUENCING Gerard Henry, Caleb Phillips, Jared Wallen, Seth Hollenbach, Niccole Diaz, Joon Yau Leong, and Paul Chung Gerard HenryGerard Henry More articles by this author , Caleb PhillipsCaleb Phillips More articles by this author , Jared WallenJared Wallen More articles by this author , Seth HollenbachSeth Hollenbach More articles by this author , Niccole DiazNiccole Diaz More articles by this author , Joon Yau LeongJoon Yau Leong More articles by this author , and Paul ChungPaul Chung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003235.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: We have known biofilm is present at the time of revision surgery in penile prostheses (PP) for both infectious and noninfectious etiologies with wonderment on what factors caused infections. Next-generation sequencing (NGS) compares DNA to identify isolates and report relative abundances. NGS has become the gold standard for identification of isolates in several other medical specialties. The objective of this study was to determine what makes some biofilms become clinically infected versus staying noninfectious. METHODS: 126 patients from 2 centers underwent PP surgery for infectious and non-infectious reasons. During revision surgery, a swab specimen from the pump space were collected and sent for evaluation at the institutional laboratory and for NGS testing (MicroGenDX, Lubbock, Texas, USA). Species’ relative abundances, sample richness and diversity, and compositional differences among samples were calculated and tested for group differences. The effect of sample variables on inter-sample variation in species richness (number of species detected per sample) was assessed using multiple ANOVA in which surgical indication, physician, race, and time to surgery were simultaneously included as predictors of species richness. RESULTS: 104 (83%) patients underwent revision surgery for non-infectious reasons with 22 (17%) for infectious reasons. The median age was 69 years with a racial breakdown was 84 White, 41 African American, and 1 unknown. Surgical indication was significant (p=0.04) with infectious revision having less specie richness; but greater distance from Group Centroid (p=0.03) as compared to noninfectious patients’ biofilm. The prevalence rates of species within each group were tabulated, and prevalence rates for species that were among the top 10 most frequently observed in either group are shown in Figure 1. Testing was conducted to assess significant prevalence rates in either group: P. aeruginosa was significantly over-represented in infection samples. CONCLUSIONS: Clinically infected PP biofilm using NGS appears to have less specie richness and more compositionally distinct than biofilm from noninfectious patients’ PP. It appears Pseudomonas aeruginosa appears to be a highly likely causative agent for PP infections. Source of Funding: MicroGenDX © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e200 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gerard Henry More articles by this author Caleb Phillips More articles by this author Jared Wallen More articles by this author Seth Hollenbach More articles by this author Niccole Diaz More articles by this author Joon Yau Leong More articles by this author Paul Chung More articles by this author Expand All Advertisement PDF downloadLoading ...

The dominance-diversity dilemma in animal conservation biology.

The alteration of environmental conditions has two major outcomes on the demographics of living organisms: population decline of the common species and extinction of the rarest ones. Halting the decline of abundant species as well as the erosion of biodiversity require solutions that may be mismatched, despite being rooted in similar causes. In this study, we demonstrate how rank abundance distribution (RAD) models are mathematical representations of a dominance-diversity dilemma. Across 4,375 animal communities from a range of taxonomic groups, we found that a reversed RAD model correctly predicts species richness, based solely on the relative dominance of the most abundant species in a community and the total number of individuals. Overall, predictions from this RAD model explained 69% of the variance in species richness, compared to 20% explained by simply regressing species richness on the relative dominance of the most abundant species. Using the reversed RAD model, we illustrate how species richness is co-limited by the total abundance of a community and the relative dominance of the most common species. Our results highlight an intrinsic trade-off between species richness and dominance that is present in the structure of RAD models and real-world animal community data. This dominance-diversity dilemma suggests that withdrawing individuals from abundant populations might contribute to the conservation of species richness. However, we posit that the positive effect of harvesting on biodiversity is often offset by exploitation practices with negative collateral consequences, such as habitat destruction or species bycatches.

Influence of landscape and livestock management on dung beetle diversity in tropical cattle pastures

To achieve a balance between conservation and livestock production in the context of highly degraded tropical landscapes, we need a research approach that considers relationships among biodiversity, landscape structure, and farming practices. Given that dung beetles are sensitive to both landscape changes and local scale management practices, these insects are used as ecological indicators that provide information regarding the pasture’s health. We assessed the response of dung beetle diversity to livestock management practices and landscape structure in 22 cattle pastures from a regional relictual landscape dominated by productive land uses. Livestock management practices were the best predictors for species richness, Shannon diversity, and number of individuals. Macrocyclic lactone use was negatively related to species richness and number of individuals; high cattle densities increased Shannon diversity in the studied ranches. Patches of old secondary forest cover had a positive significant relationship with every response variable and was the best predictor for biomass, but also contributed to maintaining high species richness in cattle pastures, suggesting that secondary forests provide favorable microclimatic conditions for dung beetles in highly degraded landscapes. This study provides evidence that conservation of old secondary forests in highly modified landscapes and the rational use of macrocyclic lactones can be useful management tools to favor dung beetle diversity and the ecosystem services they provide, thus ensuring pasture health.

Diversifying modelling techniques to disentangle the complex patterns of species richness and diversity in the protected afromontane grasslands

Ecological research has focused on the importance of environmental factors on spatial biodiversity variations and organisation. This is important because of scant conservation resources. We used stepwise backward selection and random feature selection (RFE) to identify a parsimonious model that can predict species richness and diversity metrics in response to three models; biotic, abiotic, and topo-edaphic. Our results show that both metrics are good predictors of one another, mainly because species diversity is a combination of species richness and abundance, and further highlights the importance of biotic variables in predicting species distribution. The two modelling techniques selected soil texture and its interactions with topographic variables as the most important variables. However, random forest performed worse than multiple linear regression in the prediction of diversity metrics. This research highlights the importance of topographically controlled edaphic factors as drivers of species richness and diversity in mountainous grasslands where topography inherently controls the geomorphic, hydrological, and, as a result, ecological processes.

Forest structure predicts species richness and functional diversity in Amazonian mixed‐species bird flocks

AbstractSecondary forest has the potential to act as an important habitat for biodiversity and restoring ecological benefits. Functional diversity, which includes morphological and behavioral traits that mediate species interactions with the surrounding environment, relates to the resilience of ecosystems. To assess the relationship between habitat structural differences in primary and secondary forest and the resultant differences in functional diversity of avian species, we followed 11 mixed‐species flocks at the Biological Dynamics of Forest Fragments Project, near Manaus, Brazil. We used remote sensing LiDAR to assess which three‐dimensional forest structural features are most closely associated with variation in species richness and functional diversity in secondary and primary tropical forest flocks. The species richness of flocks in primary forest increased in areas with higher elevation and higher leaf area density in the understory and subcanopy but was not correlated with habitat structure in secondary forest. Functional diversity increased at lower elevations and with a denser subcanopy in both primary forest and secondary forest but only increased with greater understory leaf area density in primary forest. Together, these results indicate that a dense subcanopy and understory can be important for mixed‐species flocks and that flock richness and functional diversity can be predicted by vegetation structure.Abstract in Portuguese is available with online material.

Past and present effects of habitat amount and fragmentation per se on plant species richness, composition and traits in a deforestation hotspot

Worldwide, human activities are rapidly changing land cover and its spatial configuration. While it is widely acknowledged that habitat loss is a major cause of biodiversity loss, there is less agreement on how biodiversity responds to changes in habitat configuration. We assessed the effects of forest amount and forest fragmentation per se (the number of patches for a given forest amount, an aspect of configuration) on woody species richness, composition, and traits in the Dry Chaco forest, a global deforestation hotspot. We sampled woody plants in 24 forest sites varying in forest amount and fragmentation per se in the surrounding landscapes. Using Generalized Linear Modeling we tested whether a model with just forest amount was at least as able to predict species richness as a model with either patch size or isolation or the combination of both. We also tested whether forest amount and fragmentation per se influenced species richness, composition, and the density of four species traits. Finally, we compared these responses to forest amount and fragmentation per se measured in the past (2009) vs. in the present (2017) to look for time-lagged responses. We found that: 1) in support of the habitat amount hypothesis, species richness was more strongly related to forest amount than to the size and/or isolation of the forest patch containing the sample plot; 2) the positive effect of forest amount on species richness was more important than the effect of fragmentation per se (also positive); 3) fragmentation per se changed species composition such that plots in landscapes with more fragmented forest had species with smaller leaves and seeds, and higher wood density; and 4) species richness showed a time-lagged response to forest amount but not to forest fragmentation per se. Our results suggest that preservation of native Dry Chaco forest should be prioritized regardless of its fragmentation level, for conserving woody plant species diversity.

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.