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Semi-natural grasslands with grazing management are characterized by high fine-scale species richness and have a high conservation value. The fact that fine-scale surveys of grassland plant communities are time-consuming may limit the spatial extent of ground-based diversity surveys. Remote sensing tools have the potential to support field-based sampling and, if remote sensing data are able to identify grassland sites that are likely to support relatively higher or lower levels of species diversity, then field sampling efforts could be directed towards sites that are of potential conservation interest. In the present study, we examined whether aerial hyperspectral (414–2501 nm) remote sensing can be used to predict fine-scale plant species diversity (characterized as species richness and Simpson’s diversity) in dry grazed grasslands. Vascular plant species were recorded within 104 (4 m × 4 m) plots on the island of Öland (Sweden) and each plot was characterized by a 245-waveband hyperspectral data set. We used two different modeling approaches to evaluate the ability of the airborne spectral measurements to predict within-plot species diversity: (1) a spectral response approach, based on reflectance information from (i) all wavebands, and (ii) a subset of wavebands, analyzed with a partial least squares regression model, and (2) a spectral heterogeneity approach, based on the mean distance to the spectral centroid in an ordinary least squares regression model. Species diversity was successfully predicted by the spectral response approach (with an error of ca. 20%) but not by the spectral heterogeneity approach. When using the spectral response approach, iterative selection of important wavebands for the prediction of the diversity measures simplified the model but did not improve its predictive quality (prediction error). Wavebands sensitive to plant pigment content (400–700 nm) and to vegetation structural properties, such as above-ground biomass (700–1300 nm), were identified as being the most important predictors of plant species diversity. We conclude that hyperspectral remote sensing technology is able to identify fine-scale variation in grassland diversity and has a potential use as a tool in surveys of grassland plant diversity.

Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao’s estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics (‘Hill diversities’), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao’s estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity.

1. Recently habitat degradation, road construction and traffic have all increased with human populations, to the detriment of aquatic habitats and species. While numerous restoration programmes have been carried out, there is an urgent need to follow their success to better understand and compensate for the decline of amphibian populations. To this end, we followed the colonization success of an anuran community across multiple replacement ponds created to mitigate large-scale habitat disturbance. 2. Following construction of a highway in western France, a restoration project was initiated in 1999 and the success of restoration efforts was monitored. The amphibian communities of eight ponds were surveyed before they were destroyed. Replacement ponds were created according to precise edaphic criteria, consistent with the old pond characteristics and taking into account the amphibian species present in each. The presence of amphibian species was recorded every year during the breeding period for 4 years following pond creation. 3. Species richness initially declined following construction of the replacement ponds but generally returned to pre-construction levels. Species diversity followed the same pattern but took longer to reach the level of diversity recorded before construction. Pond surface area, depth and sun exposure were the most significant habitat characteristics explaining both amphibian species richness and diversity. Similarly, an increase in the number of vegetation strata was positively related to anuran species richness, indicating the need to maintain a heterogeneous landscape containing relatively large open wetland areas. 4. Synthesis and applications. We highlight the species-specific dynamics of the colonization process, including an increase in the number of replacement ponds inhabited over time by some species and, in some cases, an increase in population size. Our work suggests that successful replacement ponds can be designed around simple habitat features, providing clear benefits for a range of amphibian species, which will have positive cascading effects on local biodiversity. However, consideration must also be given to the terrestrial buffer zone when management strategies are being planned. Finally, our study offers insight into the successful establishment of anuran communities over a relatively short time in restored or replacement aquatic environments.

A central question of community ecology is to understand how the interplay between processes of the Neutral Theory (e.g., immigration and ecological drift) and niche-based processes (e.g., environmental filtering, intra- and interspecific density dependence) shape species diversity in competitive communities. The articulation between these two categories of mechanisms can be studied through the lens of the intermediate organizational level of \"functional groups\" (FGs), defined as clusters of species with similar traits. Indeed, FGs stress ecological differences among species and are thus likely to unravel non-neutral interactions within communities. Here we presented a novel approach to explore how FGs affect species coexistence by comparing species and functional diversity patterns. Our framework considers the Neutral Theory as a mechanistic null hypothesis. It assesses how much the functional diversity deviates from species diversity in communities, and compares this deviation, called the \"average functional deviation,\" to a neutral baseline. We showed that the average functional deviation can indicate reduced negative density dependence or environmental filtering among FGs. We validated our framework using simulations illustrating the two situations. We further analyzed tropical tree communities in Western Ghats, India. Our analysis of the average functional deviation revealed environmental filtering between deciduous and evergreen FGs along a broad rainfall gradient. By contrast, we did not find clear evidence for reduced density dependence among FGs. We predict that applying our approach to new case studies where environmental gradients are milder and FGs are more clearly associated to resource partitioning should reveal the missing pattern of reduced density dependence among FGs.

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower.

Structural habitats support high biodiversity by providing refuge, forage resources, and recruitment habitat that upwardly influence the broader faunal community structure. However, there are few system-wide studies that empirically measure communities before and after major shifts in habitat structure or availability, limiting our ability to predict the consequences of changes in structural habitat above local scales. We used the collapse of the Apalachicola Bay, Florida oyster population as a natural experiment to assess the impacts of estuary-wide structural habitat loss on the nekton community through long-term faunal monitoring data. Habitat losses of this magnitude are expected to decrease diversity and alter composition, so we expected to observe these changes in Apalachicola Bay after the oyster population collapse. We assessed changes in Gini-Simpson diversity and composition over a 21-year period encompassing the 2012 oyster collapse using generalized linear mixed models to account for confounding drivers. We surprisingly found no evidence that Gini-Simpson diversity or composition differed immediately before or following the collapse, which may be explained if estuarine fauna were able to effectively use other habitats. This suggests that proportional changes in diversity or composition from loss or gain of structural habitats cannot be assumed at the system-wide scale.

Global biodiversity assessments have traditionally emphasized species richness; however, a comprehensive understanding of marine biodiversity patterns requires incorporating measures of evenness to capture differences in dominance and rarity among species. In this study, we evaluate the evenness in diversity globally of sea anemones (Actiniaria), a cosmopolitan group of understudied marine invertebrates. We assembled a dataset of 247,542 occurrence records from GBIF (Global Biodiversity Information Facility), converted them into incidence data, and estimated diversity at multiple spatial scales using rarefaction, extrapolation, and coverage-standardized Shannon and Simpson indices. We find the highest evenness-based diversity in areas where marine provinces and current systems converge, notably the Philippines, Chile, South Africa, the eastern United States, and Haida Gwaii, British Columbia. Regions with high evenness globally only overlapped with regions of greatest species richness globally in one case, Haida Gwaii. Integration of evenness-based metrics alongside species richness improves the comprehensiveness of biodiversity assessments and points to regions and species in need of further exploration.

Within urban green spaces, tree species diversity is believed to correlate with aboveground biomass, though there is some disagreement within the literature on the strength and directionality of the relationship. Therefore, we assessed the relationship between the biodiversity of woody species and the aboveground biomass of woody plant species in the tropical, coastal city of Haikou in southern China. To accomplish this, we obtained comprehensive tree and site data through field sampling of 190 urban functional units (UFUs, or work units) corresponding to six types of land uses governmental‐institutional, industrial‐commercial, park‐recreational, residential, transport infrastructure, and undeveloped area. Based on our field data, we investigated the relationship between tree species diversity and aboveground biomass using multiple regression, which revealed significant relationships across all five types of land uses. Aboveground biomass in green spaces was also correlated with anthropogenic factors, especially time since urban development, or site age, annual maintenance frequency by human caretakers, and human population density. Among these factors, maintenance is the strongest predictor of aboveground biomass in urban green space. Therefore, this study highlights the critical role of maintenance of urban green space in promoting both aboveground biomass and woody biodiversity in urban ecosystems and, consequently, on urban ecosystem services. Our findings contribute to a deeper understanding of the ecosystem services provided by communities of woody plant species in urban areas. We investigated the relationship between tree species diversity and aboveground biomass using multiple regression, which revealed significant relationships across all five types of land uses. Aboveground biomass in green spaces was also correlated with anthropogenic factors, especially time since urban development, or site age, annual maintenance frequency by human caretakers, and human population density. Among these factors, maintenance is the strongest predictor of aboveground biomass in urban green space. Therefore, this study highlights the critical role of maintenance of urban green space in promoting both aboveground biomass and woody biodiversity in urban ecosystems and, consequently, on urban ecosystem services. Our findings contribute to a deeper understanding of the ecosystem services provided by communities of woody plant species in urban areas.

Energycane and biomass sorghum are two of the most promising cellulosic energy crops in the southeastern US. Research on these two energy crops has focused mainly on biomass production, and there is a lack of knowledge on their ability to promote biodiversity and ecosystem services. This paper presents results from a comprehensive study on ground-active arthropod diversity in seven sites across five states in the southeastern US (Florida, Georgia, Louisiana, Mississippi, and Texas). Pitfall traps were deployed four times during each crop season for energycane, biomass sorghum, and a local reference conventional crop from 2020 to 2022. Arthropod abundance (individuals/(trap × day)) values were 4.9 ± 0.46, 3.7 ± 0.18, and 2.6 ± 0.16 (mean ± stderr) for conventional crops, biomass sorghum, and energycane, respectively, with a significant difference found only between conventional crops and energycane. Individuals were identified to arthropod orders, and Hill’s diversity indices were calculated based on the number of individuals in each arthropod order instead of the number of individuals in each arthropod species. Order-based arthropod richness values were 5.3, 5.2, and 4.8 for biomass sorghum, conventional crops, and energycane, with significant difference found only between biomass sorghum and energycane. There was no significant difference in the order-based Shannon diversity and Simpson diversity between the three crop types. The effective number of arthropod orders for the two energy crops decreased from 5.0 to 3.4 to 2.9 with increasing order of diversity from arthropod richness to Shannon diversity to Simpson diversity. The explained variability by environmental factors also decreased with increasing Hill’s order of diversity. The results from this study indicate no significant advantage in order-based arthropod diversity in growing biomass sorghum and energycane. This research fills a critical knowledge gap in understanding the impacts of cellulosic energy crop production on biodiversity and ecosystem services.

Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.