Predictor Of Species Diversity Research Articles

The State of Knowledge of CCA Diversity in the Caribbean Coral Reefs

Crustose coralline algae (CCA) are a diverse and ecologically important species found in most of the world’s oceans. The current lack of taxonomic knowledge and relative abundance compromises our ability to predict species diversity numbers and, thus, their ecological roles and impacts on coral reefs. To gather a better understanding of the state of knowledge of crustose coralline algae taxonomy in the Caribbean, 107 different research papers, and other primary and secondary literature were studied; any source with taxonomical information, species identification, or genetic markers for identification was recorded. All Genebank codes were collected and sorted by supposed species marker and then ran through the National Center of Biotechnology Information. The location these genetic markers were gathered from was compared to the natural habitat range of the species, based on the Algaebank habitat description. Of the supposed 83 described species of crustose algae in the Caribbean, based on morphological characteristics, only 24 total were confirmed by DNA markers. This leaves at least 59 species of CCA to be confirmed in the Caribbean Sea with molecular markers. This indicates the importance of DNA barcode survey studies to assess the accurate diversity of this group in the region. With this limited knowledge apparent, it should be seen that a CCAs phylogenetic and taxonomical review must be done. An in-depth assessment should be conducted on CCA collections to identify the Caribbean species correctly and thus know their biodiversity in local habitats.

Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data

This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest in Ghana. We conducted a field campaign of tree species measurements to achieve this objective for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the most dominant tree species within the forest were identified. Other measurements, such as diameter at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The following species diversity indices were estimated from the field measurements: Shannon–Wiener (H′), Simpson diversity index (D2), species richness (S), and species evenness (J′). The PlanetScope surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity prediction. The spectral/pixel information of all bands, except the coastal band, was extracted for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index, EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index, and NDRE—normalized difference red edge index) were also calculated from the spectral bands and their pixel value extracted. A correlation analysis was then performed between the spectral bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and 2 (blue) significantly correlated with the two main species diversity indices (S and H′) due to their influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore, we conducted a stepwise regression analysis to investigate the most important spectral bands to consider when estimating species diversity from the PlanetScope satellite data. Like the correlation results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting species diversity. The model equations from the stepwise regression were used to predict tree species diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.

Assessing plant trait diversity as an indicators of species α‐ and β‐diversity in a subalpine grassland of the Italian Alps

AbstractAs the need for ecosystem biodiversity assessment increases within the climate crisis framework, more and more studies using spectral variation hypothesis (SVH) are proposed to assess biodiversity at various scales. The SVH implies optical diversity (also called spectral diversity) is driven by light absorption dynamics associated with plant traits (PTs) variability (which is an indicator of functional diversity) which is, in turn, determined by biodiversity. In this study, we examined the relationship between PTs variability, optical diversity and α‐ and β‐diversity at different taxonomic ranks at the Monte Bondone grasslands, Trentino province, Italy. The results of the study showed that the PTs variability, at the α scale, was not correlated with biodiversity. On the other hand, the results observed at the community scale (β‐diversity) showed that the variation of some of the investigated biochemical and biophysical PTs was associated with the β‐diversity. We used the Mantel test to analyse the relationship between the PTs variability and species β‐diversity. The results showed a correlation coefficient of up to 0.50 between PTs variability and species β‐diversity. For higher taxonomic ranks such as family and functional groups, a slightly higher Spearman's correlation coefficient of up to 0.64 and 0.61 was observed, respectively. The SVH approach was also tested to estimate β‐diversity and we found that spectral diversity calculated by Spectral Angle Mapper showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity approach failed to estimate α‐diversity. These findings suggest that optical and PTs diversity approaches can be used to predict species diversity in the grasslands ecosystem where the species turnover is high.

Density vs. Cover: Which is the better choice as the proxy for plant community species diversity estimated by spectral indexes?

Recent studies have shown the crucial role of plant cover in the spectral signals of remote sensing images. However, how plant species cover affects the spectral-species diversity relationship remains largely unknown. To clarify this, we conducted a study in wetlands and obtained high spatial resolution multispectral images collected by unmanned aerial vehicles (UAVs). Quadrat information was obtained through a field survey. The traditional species diversity indexes and Hill numbers of each quadrat were computed by species density and cover. The mean, standard deviation, and coefficient of variation of the normalized difference vegetation index (NDVI) were computed based on the multispectral images. We employed univariate and multivariate regression models to assess the ability of spectral information to predict species diversity and the explanatory degree of variables to the multivariate regression models were decomposed. The results showed that rather than species density, spectral indexes were more strongly related to species diversity indexes computed by species cover, and the robustness and universality of the methods that predict plant species diversity based on spectral indexes have been improved by considering species cover. Meanwhile, in light of the flexibility of Hill numbers that assign different weights to dominant and rare species in the community, we believe that Hill numbers are the better choice in the spectral-species diversity studies, which can suitable to various communities with different density-cover relationships. In addition, our study also provided an insightful perspective for future spectral-plant species diversity research.

Improved prediction of tree species richness and interpretability of environmental drivers using a machine learning approach

Biodiversity is in decline globally and predicting species diversity is critically important if current trends are to be reversed. Tree species richness (TSR) has long been a key measure of biodiversity, but considerable uncertainties exist in current models, particularly given the classic statistical assumptions and poor ecological interpretability of machine learning outcomes. Here, we test several ecologically interpretable machine learning approaches to predict TSR and interpret the driving environmental factors in the continental United States. We develop two artificial neural networks (ANN) and one random forest (RF) model to predict TSR using Forest Inventory and Analysis data and 20 environmental covariates and compare them to a classic generalized linear model (GLM). Models were evaluated on an independent, unseen testing dataset using R2 and Mean Absolute Error (MAE) and residual spatial autocorrelation analysis. An Interpretable Machine Learning approach, SHapley Additive exPlanations (SHAP), was adopted to explain the major environmental factors driving TSR. Compared to a baseline GLM (R2 = 0.7; MAE = 4.7), the ANN and RF models achieved R2 greater than 0.9 and MAE<3.1. Additionally, the ANN and RF models produced less spatially clustered TSR residuals than the GLM. SHAP analysis suggested that TSR is best predicted by Aridity Index, Forest Area, Altitude, Mean Precipitation of the Driest Quarter and Mean Annual Temperature. SHAP further revealed a non-linear relationship of environmental covariates with TSR and complex interactions that were not revealed by the GLM. The study highlights the need for conservation efforts of forest areas and reducing precipitation-related physiological stress on tree species in low forested but arid regions. The machine learning approach used here is transferrable for studies of biodiversity for other organisms or prediction of TSR under future climatic scenarios.

Predicting diversity in benthic macro‐scale communities associated with mussel matrices in three Pacific ecoregions

AbstractMarine mussels are ubiquitous and their tough byssal threads allow for the formation of expansive, age‐aggregated mats known as mussel matrices which can host marine invertebrates and algal macro‐benthic communities playing an important role in food‐web dynamics. Yet, despite the significant implications for biodiversity and intertidal ecosystem functioning, the role of mussel size, individual morphology and community arrangement in determining the structure of the associated community has never been investigated. Species representative of the green, brown and red marine algal phyla as well as polychaetes, crustaceans and gastropods were sampled from mussel matrices in the Guayaquil (GUAY), Humboldtian (HWS, HNS) and North American Pacific Fjordland (NAPF) marine ecoregions. Linear mixed effects modelling (LMM) and linear effects modelling (LEM) were used to determine the effect of mussel matrices as a predictor of species diversity by incorporating variability due to ecoregion and sampling site. Shell length and stratum index demonstrated significant effect on species richness (Sobs) and Menhinick's richness (D), while stratum index demonstrated an effect on species diversity (H). In the linear mixed model analysis, shell length explained most of the variation in Menhinick's richness (D) and observed species richness (Sobs), while stratum index explained most of the variation in (D) in the linear effects model. Our findings reveal that the level of complexity in mussel assemblages plays a major role in determining species diversity.

Sentinel-2 time series analysis for monitoring multi-taxon biodiversity in mountain beech forests

Biodiversity monitoring represents a major challenge to supporting proper forest ecosystem management and biodiversity conservation. The latter is indeed shifting in recent years from single-species to multi-taxon approaches. However, multi-taxonomic studies are quite rare due to the effort required for performing field surveys. In this context, remote sensing is a powerful tool, continuously providing consistent and open access data at a different range of spatial and temporal scales. In particular, the Sentinel-2 (S2) mission has great potential to produce reliable proxies for biological diversity. In beech forests of two Italian National Parks, we sampled the beetle fauna, breeding birds, and epiphytic lichens. First, we calculated Shannon’s entropy and Simpson’s diversity. Then, to produce variables for biodiversity assessment, we exploited S2 data acquired in the 4 years 2017–2021. S2 images were used to construct spectral bands and photosynthetic indices time series, from which 91 harmonic metrics were derived. For each taxon and multi-taxon community, we assessed the correlation with S2 harmonic metrics, biodiversity indices, and forest structural variables. Then, to assess the potential of the harmonic metrics in predicting species diversity in terms of Shannon’s and Simpson’s biodiversity indices, we also fit a random forests model between each diversity index and the best 10 harmonic metrics (in terms of absolute correlation, that is, the magnitude of the correlation) for each taxon. The models’ performance was evaluated via the relative root mean squared error (RMSE%). Overall, 241 beetle, 27 bird, and 59 lichen species were recorded. The diversity indices were higher for the multi-taxon community than for the single taxa. They were generally higher in the CVDA site than in GSML, except for the bird community. The highest correlation values between S2 data and biodiversity indices were recorded in CVDA for multi-taxon and beetle communities (| r| = 0.52 and 0.38, respectively), and in GSML for lichen and beetle communities (| r| = 0.34 and 0.26, respectively). RMSE% ranged between 2.53 and 9.99, and between 8.1 and 16.8 for the Simpson and Shannon index, respectively. The most important variables are phase and RMSE of red-Edge bands for bird and lichen communities, while RMSE and time of tassel cap and from EVI indices for beetles and multi-taxon diversity. Our results demonstrate that S2 data can be used for identifying potential biodiversity hotspots, showing that the herein presented harmonic metrics are informative for several taxa inhabiting wood, giving concrete support to cost-effective biodiversity monitoring and nature-based forest management in complex mountain systems.

Size-dependent associations of woody plant structural diversity with soil C:N:P stoichiometry in a subtropical forest

Woody plant structural diversity and soil C:N:P stoichiometry have widely been examined for their spatial patterns and changes across environmental gradients, but the interactions and relationship of these biotic and abiotic variables have not been well understood. Here, we investigated the associations of woody plant structural diversity variables with soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and their stoichiometry. We found only weak associations between soil C:N:P stoichiometry and species diversity variables, however, stronger significant associations were detected between C:N:P stoichiometry and species diversity variables for the adult trees and saplings when analyses were carried out with appropriate size stratification of woody plants. Most size diversity variables were significantly correlated with TOC, TN, TP, and their stoichiometric ratios, and the size diversity variables were greater in strength than species diversity in their associations with TOC, TN, TP, and C:N:P stoichiometric ratios. In most cases, C:N:P stoichiometric ratios were more sensitive than TOC, TN, or TP in predicting species diversity and size diversity. Our findings demonstrate that the associations of woody plant species diversity with TOC, TN, TP, C:N:P stoichiometry are size-dependent, and the size diversity is much more sensitive than species diversity in predicting the change of soil TOC, TN, TP, and C:N:P stoichiometric ratios. These findings also suggest that an appropriate size stratification will help demonstrate the linear relations between woody plant structural diversity and C:N:P stoichiometry and amplify the environmental signals from soil factors in predicting the biotic variables.

MOCHI: a comprehensive cross-platform tool for amplicon-based microbiota analysis.

MotivationMicrobiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses.ResultsWe present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real time and visualizes data in both tabular and graphical formats.Availability and implementationMOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw.Supplementary information Supplementary data are available at Bioinformatics online.

Ground‐dwelling mammal diversity responds positively to productivity and habitat heterogeneity in a fire‐prone region

AbstractEnvironmental heterogeneity has a consistent, positive effect on species diversity globally, principally due to increased niche space in heterogeneous environments. In flammable ecosystems, fire‐mediated heterogeneity (pyrodiversity) is expected to increase species diversity, and the application of diverse fire regimes is a common management goal. We used landscape‐scale sampling units and linear mixed models to determine the response of ground‐dwelling mammal alpha, beta, and gamma diversity to spatial habitat heterogeneity (functional heterogeneity) and three indirect measures of spatial heterogeneity, two pyrodiversity indices based on fire history maps, and another based on mapped vegetation types. In addition, we tested the consistency of species diversity responses across a productivity gradient and examined the extent to which prescribed fire influenced habitat heterogeneity. Beta diversity responded positively to habitat heterogeneity across the productivity gradient, but more strongly at high compared with low productivity. In contrast, alpha and gamma diversity responded positively to productivity, while a weak negative effect of habitat heterogeneity on alpha diversity was also evident. At the scale of our investigation, the productivity gradient across the study area was the most influential driver of species diversity. Spatial heterogeneity within 100‐ha landscapes increased community differentiation among sites (beta diversity), had a weak negative effect on alpha diversity, but had no influence on landscape‐scale species richness (gamma diversity). The occurrence of recent fire had a strong, positive effect on habitat heterogeneity, while the diversity of vegetation types and postfire age classes had a smaller positive influence. Our findings show that prescribed fire can be used to increase landscape‐scale structural heterogeneity, but this will not always result in additional species. Finally, we suggest that using a functional representation of spatial heterogeneity (e.g., the spatial arrangement of habitat structure) as a predictor of species diversity is likely to reveal responses that may otherwise be overlooked. Modern remote‐sensing technologies will aid the development of habitat‐based heterogeneity metrics across large spatial extents.

Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices

Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.

Variation in mammal species richness and relative abundance in the Karoo

Understanding how climate change and land transformation may impact the distribution and diversity of wildlife species requires landscape-level foundational biodiversity surveys. The Karoo BioGaps Project aims to provide such data and to support the scientific assessment for shale gas development projects in the Karoo basin. In this paper we present the findings of the BioGaps mammal survey, which recorded medium and large mammals across twenty-five 1 km × 1 km sampling sites within the proposed fracking footprint using camera trapping techniques. We use sample rarefaction curves, non-parametric species richness estimators and non-metric multidimensional scaling plots to explore both species richness and community structure. We also used a generalised linear model to investigate how species diversity varies with both site-specific and landscape-level predictors. A total of 38 species were recorded at the majority of sites. Longitude (z = 4.018, p = 0.0005) emerged as the best predictor of species diversity across the study area, which we suggest is linked to the clear east–west aridity gradient. Together these results reveal the cosmopolitan distribution of the mammal taxa in the Karoo and could be used to inform decision-making linked to mining activities in the area.

Simplicity from complex interactions.

Several recent theoretical studies develop tools to predict species diversity in large model ecosystems, setting a new benchmark for understanding the mechanism of species coexistence in natural ecosystems.

Occupancy and environmental responses of habitat specialists and generalists depend on dispersal traits

AbstractEcologists have been interested in understanding communities through the lens of specialists and generalists to predict species diversity and distribution patterns and to ameliorate worldwide declines in specialist species. Dispersal traits are assumed to be associated with specialization (specialists are weaker dispersers than generalists), but dispersal modes can be variable within groups. Niche‐based predictions of occupancy and environmental responses were assessed using invertebrates from California vernal pools that were categorized by specialization (endemic or widespread taxa) and dispersal mode (passive or active dispersal). Data from a latitudinal gradient resulted in widespread taxa with greater percent occupancy than endemic taxa as predicted, but passive dispersers had greater occupancy than active dispersers in contrast to predictions. Endemic species and widespread‐active dispersers exhibited similar levels of specialization measured as coefficient of variation among treatments in a mesocosm experiment. This suggested that habitat choice was important, and these differences in specialization were scale dependent (generalists across habitat types and specialists within a habitat type). A negative correlation between latitudinal occupancy and level of specialization demonstrated how local‐scale responses and landscape patterns were related and depend on both specialization and dispersal traits. This study underscores how habitat heterogeneity and species traits, including specialization and dispersal, can interact to affect community patterns at different spatial scales.

Testing the accuracy of genus-level data to predict species diversity in Cenozoic marine diatoms

Abstract Correlations between past biotic diversity and climate can inform humanity’s response to predictions of future global climate change, e.g., extinction risk with global warming. Paleodiversity studies, however, frequently use genera as a proxy for species diversity, a practice that has often been questioned. Tests using actual data of the impact of using species-level versus genus-level taxonomy in paleodiversity-paleoenvironmental studies are also lacking. We conduct such a test, based on a recent study that showed a strong correlation of Cenozoic marine planktonic diatom species diversity to geochemical climate proxies. Using an updated version of the data set, we still find a strong correlation of Cenozoic diatom species diversity to environment. Using identical data but instead genera results in the loss of all significant correlations of diversity to environmental change. This occurs due to the earlier rise and later stability in genus versus species diversity data, a pattern known to be common between taxonomic ranks in the fossil record, and in general models of diversification. We conclude that studies of paleodiversity, particularly those addressing biotic responses to future environmental change, need to demonstrate the adequacy of genera as a proxy for species diversity, or use species-level data.

Satellite remote sensing to monitor species diversity: potential and pitfalls

AbstractAssessing the level of diversity in plant communities from field‐based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost‐effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field‐based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha‐diversity is relatively straightforward, calculation of beta‐diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma‐diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments.

Predicting Plant Diversity Response to Disturbance: Applicability of the Intermediate Disturbance Hypothesis and Mass Ratio Hypothesis

Predicting the relationships between disturbance, biodiversity and productivity of ecosystems continue to preoccupy ecologists and resource managers. Two hypotheses underpin many of the discussions. The Intermediate Disturbance Hypothesis (IDH), which proposes that biodiversity peaks at intermediate levels of disturbance, is often extended to predict that productivity follows the same response pattern. The Mass Ratio Hypothesis (MRH) proposes that the biological traits of the dominant species are the critical drivers of ecosystem function (e.g., productivity) and that these species increase in biomass rapidly after disturbance then stabilize. As a consequence, species diversity first peaks then declines after disturbance as a few species dominate the site. Both provide a conceptual link among disturbance, species diversity and productivity (an index of ecosystem function). We assessed the current state of empirical support for these two hypotheses with a literature survey and determined if their conformance is related to ecosystem type or site productivity. Conformance of IDH reported in past reviews (considering all ecosystems) ranged from 16 to 21%. This contrasts with our finding that in terrestrial ecosystems conformance to IDH was 46% (22 of 48 studies), 17% studies reported non-compliance, and 23% reported inconclusive results. Most studies explained their results with respect to IDH or MRH. Only two studies were specifically designed to test the validity of IDH or MRH. We conclude that (i) the IDH is mostly applicable to predict species diversity response to disturbance in upland sites of medium to high productivity and the MRH is applicable to organic sites of low productivity; (ii) there is a critical need for more studies specifically designed to test these hypotheses in natural ecosystems using common protocols; and (iii) enhanced understanding of these models will add value in refining management policies and in the selection of meaningful diversity indicators of sustainability.

Global diversity patterns and cross‐taxa convergence in freshwater systems

Whereas global patterns and predictors of species diversity are well known for numerous terrestrial taxa, our understanding of freshwater diversity patterns and their predictors is much more limited. Here, we examine spatial concordance in global diversity patterns for five freshwater taxa (i.e. aquatic mammals, aquatic birds, fishes, crayfish and aquatic amphibians) and investigate the environmental factors driving these patterns at the river drainage basin grain. We find that species richness and endemism patterns are significantly correlated among taxa. We also show that cross-taxon congruence patterns are often induced by common responses of taxa to their contemporary and historical environments (i.e. convergent patterns). Apart from some taxa distinctiveness (i.e. fishes), the 'climate/productivity' hypothesis is found to explain the greatest variance in species richness and endemism patterns, followed by factors related to the 'history/dispersion' and 'area/environmental heterogeneity' hypotheses. As aquatic amphibians display the highest levels of congruency with other taxa, this taxon appears to be a good 'surrogate' candidate for developing global freshwater conservation planning at the river drainage basin grain.

Island phytophagy: explaining the remarkable diversity of plant-feeding insects

Plant-feeding insects have undergone unparalleled diversification among different plant taxa, yet explanations for variation in their diversity lack a quantitative, predictive framework. Island biogeographic theory has been applied to spatially discrete habitats but not to habitats, such as host plants, separated by genetic distance. We show that relationships between the diversity of gall-inducing flies and their host plants meet several fundamental predictions from island biogeographic theory. First, plant-taxon genetic distinctiveness, an integrator for long-term evolutionary history of plant lineages, is a significant predictor of variance in the diversity of gall-inducing flies among host-plant taxa. Second, range size and structural complexity also explain significant proportions of the variance in diversity of gall-inducing flies among different host-plant taxa. Third, as with other island systems, plant-lineage age does not predict species diversity. Island biogeographic theory, applied to habitats defined by genetic distance, provides a novel, comprehensive framework for analysing and explaining the diversity of plant-feeding insects and other host-specific taxa.

Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners

Epi-macrobenthic species richness, abundance and composition are linked with type, assemblage and structural complexity of seabed habitat within coastal ecosystems. However, the evaluation of these habitats is highly hindered by limitations related to both waterborne surveys (slow acquisition, shallow water and low reactivity) and water clarity (turbid for most coastal areas). Substratum type/diversity and bathymetric features were elucidated using a supervised method applied to airborne bathymetric LiDAR waveforms over Saint-Siméon–Bonaventure's nearshore area (Gulf of Saint-Lawrence, Québec, Canada). High-resolution underwater photographs were taken at three hundred stations across an 8-km2 study area. Seven models based upon state-of-the-art machine learning techniques such as Naïve Bayes, Regression Tree, Classification Tree, C 4.5, Random Forest, Support Vector Machine, and CN2 learners were tested for predicting eight epi-macrobenthic species diversity metrics as a function of the class number. The Random Forest outperformed other models with a three-discretized Simpson index applied to epi-macrobenthic communities, explaining 69% (Classification Accuracy) of its variability by mean bathymetry, time range and skewness derived from the LiDAR waveform. Corroborating marine ecological theory, areas with low Simpson epi-macrobenthic diversity responded to low water depths, high skewness and time range, whereas higher Simpson diversity relied upon deeper bottoms (correlated with stronger hydrodynamics) and low skewness and time range. The degree of species heterogeneity was therefore positively linked with the degree of the structural complexity of the benthic cover. This work underpins that fully exploited bathymetric LiDAR (not only bathymetrically derived by-products), coupled with proficient machine learner, is able to rapidly predict habitat characteristics at a spatial resolution relevant to epi-macrobenthos diversity, ranging from clear to turbid waters. This method might serve both to nurture marine ecological theory and to manage areas with high species heterogeneity where navigation is hazardous and water clarity opaque to passive optical sensors.