Predicting Plant Species Richness Research Articles

Small area and low connectivity constrain the diversity of plant life strategies in temporary ponds

AbstractAim(i) To determine whether area and connectivity of temporary ponds can predict plant species diversity, and the diversity and abundance of different plant life histories; (ii) To explore whether pond connectivity with the river prior to river regulation predicts better plant diversity patterns than current pond connectivity, suggestive of possible effects of connectivity loss.LocationEastern Carpathian Mountains, Romania, Europe.MethodsWe fitted linear and generalized linear models (LM and GLM) to examine whether pond area and current distance from the Olt River predict plant species richness, Shannon diversity and relative cover of different social behaviour types and overall plant species richness and Shannon diversity. Using historical maps, we measured pond distance from the river ca. 60 years before the Olt River was regulated, and we refitted the LM and GLM models using pond area and past distance from the river as independent variables.ResultsTotal plant species richness increased with pond area, and it decreased with the distance from the river, but total plant Shannon diversity index was affected, positively, only by pond area. The strength of responses to pond area and connectivity of species richness, Shannon diversity and relative cover varied across the different social behaviour types. Past and current distances between ponds and riverbeds had similar effects on plant diversity, with some evidence for stronger effect of the present connectivity on specialist species Shannon diversity and a weaker effect on disturbance tolerants, generalists and competitors.Main ConclusionsPond area and connectivity with the landscape are important predictors of the diversity of plant life history strategies, and therefore, useful tools in pond conservation. Consistent species richness and Shannon diversity responses of wetland specialists to pond area and connectivity make this life history type well suited for monitoring pond condition.

Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models

The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. Airborne hyperspectral imaging has shown promise for measuring plant diversity remotely, but to operationalise these efforts over large regions we need to advance satellite-based alternatives. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness. Our results provide a quantitative reference for future studies exploring the potential of spaceborne hyperspectral data for plant biodiversity mapping.

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.

Soil water retention capacity surpasses climate humidity in determining soil organic carbon content but not plant production in the steppe zone of Northern China

Soil hydraulic properties determine water transport, distribution and storage in soils, thus affect ecosystem functioning. Soil water retention parameters, such as saturation water content (SAT), water drained upper limit (DUL) and lower limit (DLL), characterize soil water-holding capacity, thus are important for plant growth and carbon and nutrient cycling in grassland ecosystem. However, we are still unclear to what a content these soil water retention parameters may regulate plant production and soil organic matter content in natural ecosystems in semi-arid grassland environment. We investigated plant species richness (SR) and aboveground biomass (AGB), soil organic carbon (SOC) and nitrogen (SON) contents, and soil water retention and other physical parameters in the steppe ecosystems along a climate transect, which spans the desert steppe, typical steppe and meadow steppe zones in Northern China. We analyzed the relative importance of climate factors, soil water retention parameters and other soil physical parameters in regulating plant AGB and SOC and SON contents using partial correlation analysis, and quantified the contribution of each separate and combined climate and soil physical parameters in predicting plant AGB and SOC and SON contents using geographic detectors method. We found that (i) plant SR and AGB, soil SAT and DUL, and SOC and SON are all significantly inter-correlated, and increased with the increase of climate humidity. (ii) Climate, but not soil physical parameters, was the most preponderant factor in predicting plant SR and AGB. (iii) Soil physical parameters were more important than climate parameters in regulating soil organic matter content; soil DUL was the best predictor to SOC content. Our results suggest that soil water retention parameters especially soil DUL, may play a greater role than climate factors in modulating SOC content, though climate is more predominant in regulating plant SR and production. Our results imply that we need to be cautious in estimating SOC content based on climate factors and vegetation biomass or remotely-sensed vegetation indices, and that soil physical properties need also to be incorporated along with vegetation parameters for improving SOC estimation at a regional scale.

Predicting plant species richness in forested landslide zones using geostatistical methods

Landslides, like most natural disturbances, facilitate the evolution of new plant species. Hence, a detail characterization of topographic conditions can improve the prediction and mapping of species in such complex terrains. Within the Outer (Flysch) Upper Carpathian region, south Poland, we analyze the role of convergence points prepared in a previous study from slope and slope exposition (aspect) data, derived from a 1 m digital elevation model. Convergence points reflected microscale variability in topographic conditions and were analyzed in this study as convergence point density (CPD). Our objective was to use CPD to predict species richness on forested landslides using three geostatistical methods; Ordinary kriging (OK), Ordinary cokriging (OCK), and regression kriging (RK). Our results showed a relatively high correlation (r ∼ 0.65) between species richness and CPD compared to slope or slope exposition or with both. OCK and RK generally improved prediction. However, the application of cokriging in such terrains remains challenging and will not be appropriate, particularly if species richness has a small sample size. RK outperformed OK and OCK, decreasing the root mean square error (RMSE) by 33% and 10%, respectively. RK was also more robust to topographic heterogeneity and the limited number of observations than OCK. We conclude that a denser sampling of species composition or a more robust indicator is needed to improve these results. Notwithstanding these limitations, our results can be used as the first step to support short-term conservation efforts, especially when time-dependent changes in species composition are unimportant.

Island biogeography theory predicts plant species richness of remnant grassland patches in the agro-pastoral ecotone of northern China

Habitat loss and fragmentation are critical threats that cause biodiversity loss. However, the driving factors of species diversity in fragmented habitats are complex. This study was conducted in the agro-pastoral ecotone of northern China, a region that has suffered from severe habitat fragmentation due to agricultural activities. Based on the plant species richness, patch characteristics (patch area, fractal dimension, and isolation), landscape characteristics (grassland area ratio within buffer zones with different radii (50–500 m) around each patch), and environmental characteristics (mean annual precipitation, mean annual temperature, altitude, extremely high temperature, extremely low temperature, slope, and aspect) of 82 remnant grassland patches, we first explored the appropriate buffer zone with the most powerful impact on species richness of remnant grassland patches (SRP). Furthermore, we analyzed the relationships between a series of predictors and SRP. Finally, we confirmed the critical driving factors of SRP through a random forest analysis. The results showed that the landscape scale with the most powerful impact on SRP was 150 m. The patch area, grassland area ratio of a buffer zone with a 150 m radius, mean annual precipitation, and slope all had significantly positive effects on SRP. Among these factors, patch area and patch isolation were the top two driving factors of SRP. Our study suggests that large and highly connected patches should be protected for the construction of natural reserves. Meanwhile, the habitat around patches within a suitable range should also not be ignored. We highlight that the island biogeography theory could be used to predict plant species richness of remnant grassland patches in the agro-pastoral ecotone of northern China.

Effects of local characteristics and landscape patterns on plant richness: A multi-scale investigation of multiple dispersal traits

Abstract Plant species distributions are determined by various factors (including the dispersal modes and the complex physical environment) across multiple spatial scales, in which the dispersal distance of different dispersal modes and the external environment (including local characteristics and landscape patterns) are closely related. Therefore, understanding the response mechanism of plant species with different dispersal abilities to environmental factors is critical for developing effective landscape management strategies and prioritizing biodiversity conservation efforts at the appropriate scale. In this study, we aimed to explore: (1) the spatial distribution of plant species richness; (2) the spatial scale at which plant species have the strongest response to environment variables (scale of effect); and (3) to identify the key factors that affect the plant species at local and six landscape scales (a 0.5 km, 1 km, 1.5 km, 2 km, 3 km and 4 km-radius circle) considering multiple dispersal traits. We conducted vegetation surveys at 53 sites and classified plant species into three dispersal modes, including biotic dispersal (BD), abiotic dispersal (AD) and autochorous (AT), based on the respective dispersal vector of the seed. We classified land use types into 15 categories and clipped 6 specified radii around each sampling site using ENVI 5.1 and ArcGIS 10.3 software. We applied linear mixed effects models (LMM) to analyze the local and the surrounding landscape variables in predicting plant species richness in Qingshui town of Beijing, China. Our results revealed that the response traits of species to environment factors varied across different scales, proving the existence of scale effect in our study area. The biotically-dispersed (BD) and abiotically-dispersed (AD) species favored structurally complex habitats with greater tree height and cover of herb at the local scale. The area-weighted mean of shape metric (SHAPE_MN) of shrubbery and the euclidean nearest neighbor distance distribution (ENN_AM) of bare land play a major role in determining BD and AD species at large spatial extents, which suggest that the patterns and ecological attributes of patches of these two land use types hinder the migration and colonization ability of species and thus affect the species richness. These results suggest that a multi-scale approach should be encouraged to help guide effective landscape managements in conserving biodiversity for different response traits.

Predicting plant species richness with satellite images in the largest dry forest nucleus in South America

Biodiversity assessment is considered an important indicator of ecosystem health by various initiatives worldwide. Satellite remote sensing (SRS) has allowed the development of tools that can assist with the practical search of information related to species richness. The aim of this study was to test whether Landsat satellite spectral variables could be used as indicators of plant species diversity in the Caatinga, the largest nucleus of dry forest in South America. To obtain plant diversity data (richness and Shannon's index), an exhaustive search of plant phytosociological studies carried out in Caatinga was conducted. Pearson's correlation and PCA analysis was used to test the association between spectral variables and plant diversity. Regressions were used to test the models that best explain species richness. The results indicate that a positive correlation exists between richness and the near-infrared (NIR) spectral band (r = 0.744; p < 0.001). This spectral band was also responsible for explaining better the variation of leaf level reflectance among eight species that occur in the region (df = 7; F = 26317.55; p < 0.001). Therefore, the NIR band variable can be used as an indicator of species richness using power and quadratic regression models, because they were one of the best fit association recorded between spectral variable and plant diversity index, when compared to other studies in natural environments. Thus, we provide important information about biodiversity that can be used in different researches, from ecological modeling for theoretical approaches to practical applications in Caatinga. The potential use of Landsat satellite imagery to estimate species richness makes biodiversity assessments easier and provides a continuous source of data for monitoring in Brazilian semiarid region.

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

Numerous spectral indices have been developed to assess plant diversity. However, since they are developed in different areas and vegetation type, it is difficult to make a comprehensive comparison among these indices. The primary objective of this study was to explore the optimum spectral indices that can predict plant species richness across different communities in sandy grassland. We use 7339 spectral indices (7217 we developed and 122 that were extracted from literature) to predict plant richness using a two-year dataset of plant species and spectra information at 270 plots. For this analysis, we employed cluster analysis, correlation analysis, and stepwise linear regression. The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness. Within the 7339 indices that were investigated, the first-order derivative values at 606 and 583 nm, the reflectance combinations on red bands: (R802 − R465)/(R802 + R681) and (R750 − R550)/(R750 + R550) showed a stable performance in both the independent calibration and validation datasets (R2 &gt; 0.27, p &lt; 0.001, RMSE &lt; 1.7). They can be regarded as the best spectral indices to estimate plant species richness in sandy grasslands. In addition to these spectral variation indices, the first derivative values or the normalized difference of the sensitive bands also reflect plant diversity. These results can help to improve the estimation of plant diversity using satellite-based airborne and hand-held hyperspectral sensors.

And the winner is …. ! A test of simple predictors of plant species richness in agricultural grasslands

Land-use intensification, nutrient enrichment and enhanced productivity levels are major drivers of the current biodiversity loss in agricultural grasslands. Although these drivers are potentially strongly causally linked, their potential to explain changes in biodiversity can enhance each other and often varies over time. Here, we explored the explanatory power of different abiotic drivers and the Ellenberg indicator value for nutrient availability to predict plant species richness using data of 150 grassland plots over seven years. The approach also allows to assess the temporal robustness of many of these drivers. Our results revealed the Ellenberg indicator value for nutrients to be the best and temporally most consistent predictor of plant species richness. Unweighted mean Ellenberg values showed an even stronger negative relationship to plant species richness than abundance weighted mean values. Direct measurements of soil and plant biomass nutrients, aboveground biomass and land-use intensity predicted plant species richness less well and showed considerable temporal variability in the respective association. In case of biomass nitrogen and phosphorus concentrations, current year weather conditions are assumed to strongly affect the strength of the relationship by shifting the phenological development considerable among sampling years. Although mainly driven by aboveground biomass, a combination of biomass nutrient concentrations with productivity, i.e. the nutrient stock in the aboveground biomass, turned out to better predict plant species richness than its single components and inter-annual variability in the relationship was markedly lower. Especially for aboveground biomass and the intensity of land-use, long-term mean values significantly improved the explanatory power compared to single year measurements. We conclude that studies explaining biodiversity changes should carefully take into account temporal variability in the drivers under study before judging the strength of a particular mechanistic relationship.

Using airborne laser scanning to predict plant species richness and assess conservation threats in the oil sands region of Alberta’s boreal forest

Abstract Timely and cost-effective monitoring of biodiversity across large areas is a major challenge, yet an important component of monitoring programs that inform policy and conservation strategies. Recent advances in Airborne Laser Scanning (ALS) provide new opportunities to simultaneously measure vegetation structure and terrain morphology at fine spatial scales. However, there is limited research on whether ALS metrics correlate with biodiversity measures. Here we used vascular plant data from 283 quarter-hectare (50 m × 50 m) plots from the boreal forest in northeast Alberta, Canada, to evaluate the potential for ALS-derived metrics to explain species richness patterns for vascular plants, as well as for four growth forms: herbaceous (including forbs and graminoids) and woody plants. We found canopy height from ALS was the most consistent and important (positive) factor related to local patterns in vascular plant species richness. Multivariate regression models of ALS-derived metrics explained 20–35% of the variation in species richness among vascular plants and the four subclasses. When considering the current distribution of in situ oil sands leases in the region, vascular plant richness inside of the leases is higher than outside. Areas delineated for woodland caribou conservation had lower average plant richness suggesting that it will do little to protect hotspots of vascular plant diversity in Alberta’s boreal forest. Our results highlight the value of using fine-scale measures of ALS-derived vegetation structure to explain, predict, and potentially monitor local plant diversity for a high latitude forested ecosystem.

Island biogeography theory outweighs habitat amount hypothesis in predicting plant species richness in small grassland remnants

ContextThe habitat amount hypothesis has rarely been tested on plant communities. It remains unclear how habitat amount affect species richness in habitat fragments compared to island effects such as isolation and patch size.ObjectivesHow do patch size and spatial distribution compared to habitat amount predict plant species richness and grassland specialist plant species in small grassland remnants? How does sampling area affect the prediction of spatial variables on species richness?MethodsWe recorded plant species density and richness on 131 midfield islets (small remnants of semi-natural grassland) situated in 27 landscapes in Sweden. Further, we tested how habitat amount, compared to focal patch size and distance to nearest neighbor predicted species density and richness of plants and of grassland specialists.ResultsA total of 381 plant species were recorded (including 85 grassland specialist species). A combination of patch size and isolation was better in predicting both density and richness of species compared to habitat amount. Almost 45% of species richness and 23% of specialist species were explained by island biogeography parameters compared to 19 and 11% by the amount of habitat. A scaled sampling method increased the explanation level of island biogeography parameters and habitat amount.ConclusionsHabitat amount as a concept is not as good as island biogeography to predict species richness in small habitats. Priority in landscape planning should be on larger patches rather than several small, even if they are close together. We recommend a sampling area scaled to patch size in small habitats.

Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape

Google Earth provides a freely available, global mosaic of high-resolution imagery from different sensors that has become popular in environmental and ecological studies. However, such imagery lacks the near-infrared band often used in studying vegetation, thus its potential for estimating vegetation properties remains unclear. In this study, we assess the potential of Google Earth imagery to describe and predict vegetation attributes. Further, we compare it to the potential of SPOT imagery, which has additional spectral information. We measured basal area, vegetation height, crown cover, density of individuals, and species richness in 60 plots in the oak forests of a complex volcanic landscape in central Mexico. We modelled each vegetation attribute as a function of surface metrics derived from Google Earth and SPOT images, and selected the best-supported linear models from each source. Total species richness was the best-described and predicted variable: the best Google Earth-based model explained nearly as much variation in species richness as its SPOT counterpart (R2 = 0.44 and 0.51, respectively). However, Google Earth metrics emerged as poor predictors of all remaining vegetation attributes, whilst SPOT metrics showed potential for predicting vegetation height. We conclude that Google Earth imagery can be used to estimate species richness in complex landscapes. As it is freely available, Google Earth can broaden the use of remote sensing by researchers and managers in low-income tropical countries where most biodiversity hotspots are found.

Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah

AbstractIn this study, we test for the key bioclimatic variables that significantly explain the current distribution of plant species richness in a southern African ecosystem as a preamble to predicting plant species richness under a changed climate. We used 54,000 records of georeferenced plant species data to calculate species richness and spatially interpolated climate data to derive nineteen bioclimatic variables. Next, we determined the key bioclimatic variables explaining variation in species richness across Zimbabwe using regression analysis. Our results show that two bioclimatic variables, that is, precipitation of the warmest quarter (R2 = 0.92, P &lt; 0.001) and temperature of the warmest month (R2 = 0.67, P &lt; 0.001) significantly explain variation in plant species richness. In addition, results of bioclimatic modelling using future climate change projections show a reduction in the current bio‐climatically suitable area that supports high plant species richness. However, in high‐altitude areas, plant richness is less sensitive to climate change while low‐altitude areas show high sensitivity. Our results have important implications to biodiversity conservation in areas sensitive to climate change; for example, high‐altitude areas are likely to continue being biodiversity hotspots, as such future conservation efforts should be concentrated in these areas.

Inclusion of explicit measures of geodiversity improve biodiversity models in a boreal landscape

Measures of geodiversity may provide a potentially useful surrogate for biodiversity patterns in insufficiently surveyed areas. However, their reliability in modelling the spatial variation in species richness is inadequately understood. We investigated whether the explanatory and predictive power of species richness models can be improved by considering explicit measures of geodiversity (variability of earth surface materials, forms and processes) in addition to climate and topography variables. Vascular plant species richness was modelled in two study areas in Northern Europe, Finland at the resolution of 500 or 1000 m, and as a function of three geodiversity (geological, geomorphological and hydrological diversity) variables, and six climate and topography variables. Variation partitioning was used to identify the independent and shared contributions of the geodiversity, climate and topography variable groups in explaining the spatial patterns of species richness. Generalized additive models were used to explore the ability of the different explanatory variables in predicting plant species richness within and between the study areas. In both the study areas, the inclusion of measures of geodiversity improved the explanatory power, predictive ability and robustness of the plant species richness models. In conclusion, the explicit measures of geodiversity appear to be promising surrogates of biodiversity, which reflect important abiotic resource factors, and may thus provide an equally, or even more reliable basis for transferring biodiversity models to new areas than models based on climate and topography variables.

Butterfly and grasshopper diversity patterns in humid Mediterranean grasslands: the roles of disturbance and environmental factors

The present paper studies butterfly, grasshopper and vascular plant communities in ten seasonally flooded grasslands with different anthropogenic disturbance regimes (NW Greece). Disturbance intensity was assessed on the basis of disturbance frequency and type (grazing, mowing, trampling, constructions). The distribution patterns of butterflies are regulated by humidity and elevation (Redundancy Analysis). Elevation, flower-heads abundance, low disturbance intensity and plant species richness predict grasshopper species richness well, while the latter together with humidity predict plant species richness (Generalized Linear Models). Chorthippus lacustris, a critically endangered endemic grasshopper species, is positively associated with humid microhabitats with high flower-heads abundance. An indicator value procedure reveals four butterfly species as being typical species for habitats with a pronounced character of hedgerows and tree lines. Conservation management of grassland butterflies should focus on the maintenance of the humid character of the humid grasslands as well as on the maintenance of hedgerows and tree lines. The reduction of human-induced disturbance towards occasional grazing and mowing seems to benefit both butterfly and grasshopper communities. Finally, we suggest the use of grasshoppers as surrogates for vascular plants and vice versa, given their congruent species richness patterns.

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.

CORINE-based landscape indices weakly correlate with plant species richness in a northern European landscape transect

We present the results of one of the few available tests of how CORINE (CLC2000) is likely to perform as a basis for the calculation of landscape indices, for environmental monitoring over large areas. This paper investigates to what extent landscape structural indices based on this widely used European land cover database can be used to predict plant species richness in a 2,000 km2 transect in the northeast of Scotland. We investigate both statistical and map resolution issues by comparing the performance of CORINE-based common landscape indices with the same indices derived from a much more detailed geographic data set. In our case study, only shape-related indices show correlation with species richness, but effect size, important for monitoring, is small. The results highlight the area-specific and map specific nature of the performance of landscape indices for protecting plant diversity.

Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia

Changes in species composition and diversity are the inevitable consequences of climate change, as well as land use and land cover change. Predicting species richness at regional spatial scales using remotely sensed biophysical variables has emerged as a viable mechanism for monitoring species distribution. In this study, we evaluate the utility of MODIS-based productivity (GPP and EVI) and surface water content (NDSVI and LSWI) in predicting species richness in the semi-arid region of Inner Mongolia, China. We found that these metrics correlated well with plant species richness and could be used in biome- and life form-specific models. The relationships were evaluated on the basis of county-level data recorded from the Flora of Inner Mongolia, stratified by administrative (i.e., counties), biome boundaries (desert, grassland, and forest), and grouped by life forms (trees, grasses, bulbs, annuals and shrubs). The predictor variables included: the annual, mean, maximum, seasonal midpoint (EVImid), standard deviation of MODIS-derived GPP, EVI, LSWI and NDSVI. The regional pattern of species richness correlated with GPPSD (R2=0.27), which was also the best predictor for bulbs, perennial herbs and shrubs (R2=0.36, 0.29 and 0.40, respectively). The predictive power of models improved when counties with >50% of cropland were excluded from the analysis, where the seasonal dynamics of productivity and species richness deviate patterns in natural systems. When stratified by biome, GPPSD remained the best predictor of species richness in grasslands (R2=0.30), whereas the most variability was explained by NDSVImax in forests (R2=0.26), and LSWIavg in deserts (R2=0.61). The results demonstrated that biophysical estimates of productivity and water content can be used to predict plant species richness at the regional and biome levels.

Predicting plant species richness in a managed forest

This paper describes an attempt to predict ground flora species richness under various forest management scenarios. The approach is based on a geographic information system (GIS) and uses three standard map layers of topography, soils and stands to derive environmental gradients of light, nutrients, water and disturbance. A simple floristic survey provides the data necessary to relate plant distribution with environmental variables. The potential distribution of 60 understorey plant species is modelled based on the four derived gradients. The sum of these maps, i.e., the total potential diversity, is used as a proxy for the prediction of actual species richness. The model predicts high species diversity along roads and in relatively disturbed areas and low species diversity in stands with coniferous species and in stands of old, deciduous trees (mainly beech). The overall predicted pattern of species diversity corresponds well with observations made in the forests. However, the model explained only a fraction of the variation in the data set on the plot level. Dispersal effects, demographic stochasticity and biological factors are the probable causes of this. The combination of GIS-based spatial operations and fuzzy cognitive mapping proved to be an efficient way of producing gradient maps based on standard forestry maps and expert knowledge.