Spatial heterogeneity influences native and nonnative plant species richness

Spatial heterogeneity may have differential effects on the distribution of native and nonnative plant species richness. We examined the effects of spatial heterogeneity on native and nonnative plant species richness distributions in the central part of Rocky Mountain National Park, Colorado, USA. Spatial heterogeneity around vegetation plots was characterized using landscape metrics, environmental/topographic variables (slope, aspect, elevation, and distance from stream or river), and soil variables (nitrogen, clay, and sand). The landscape metrics represented five components of landscape heterogeneity and were measured at four spatial extents (within varying radii of 120, 240, 480, and 960 m) using the FRAGSTATS landscape pattern analysis program. Akaike's Information Criterion adjusted for small sample size (AICc) was used to select the best models from a set of multiple linear regression models developed for native and nonnative plant species richness at four spatial extents and three levels of ecological hierarchy (i.e., landscape, land cover, and community). Both native and nonnative plant species richness were positively correlated with edge density, Simpson's diversity index and interspersion/juxtaposition index, and were negatively correlated with mean patch size. The amount of variation explained at four spatial extents and three hierarchical levels ranged from 30% to 70%. At the landscape level, the best models explained 43% of the variation in native plant species richness and 70% of the variation in nonnative plant species richness (240-m extent). In general, the amount of variation explained was always higher for nonnative plant species richness, and the inclusion of landscape metrics always significantly improved the models. The best models explained 66% of the variation in nonnative plant species richness for both the conifer land cover type and lodgepole pine community. The relative influence of the components of spatial heterogeneity differed for native and nonnative plant species richness and varied with the spatial extent of analysis and levels of ecological hierarchy. The study offers an approach to quantify spatial heterogeneity to improve models of plant biodiversity. The results demonstrate that ecologists must recognize the importance of spatial heterogeneity in managing native and nonnative plant species.     

Natural and anthropogenic influences on the diversity structure of reef fish communities in the Tuamotu Archipelago (French Polynesia)

Despite the rapid rate of human-induced species losses, the relative influence of natural and anthropogenic factors on the functional diversity of species assemblages remains unknown for most ecosystems. A model was previously developed to predict the diversity structure of coral reef fish assemblages in 10 atolls of low human pressure and contrasting morphology of the Tuamotu Archipelago (French Polynesia). This existing model predicted smoothed histograms (spectra) of species richness according to size classes, diet classes and life-history classes of fish assemblages using a combination of environmental characteristics at different spatial scales. The present study applied the model to Tikehau, another atoll of the same archipelago where commercial fishing is practiced and where the same sampling strategy was reproduced. Significant differences appeared between predicted and observed species richness in several size, diet and life-history classes of fish assemblages in Tikehau. Two parameters which were not accounted for in the initial model, i.e. fishing pressure and atoll position within the archipelago, explained together 63% of variance in model residuals, >60% being explained by fishing pressure only. The respective effects of fishing and atoll position on the diversity of coral reef fish assemblages are discussed, with the potential of such modelling approach to assess the relative importance of factors affecting functional diversity within communities.     

Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model

The species abundance distribution (SAD) is one of themost studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.     

Zooplankton biodiversity and lake trophic state: explanations invoking resource abundance and distribution

While empirical studies linking biodiversity to local environmental gradients have emphasized the importance of lake trophic status (related to primary productivity), theoretical studies have implicated resource spatial heterogeneity and resource relative ratios as mechanisms behind these biodiversity patterns. To test the feasibility of these mechanisms in natural aquatic systems, the biodiversity of crustacean zooplankton communities along gradients of total phosphorus (TP) as well as the vertical heterogeneity and relative abundance of their phytoplankton resources were assessed in 18 lakes in Quebec, Canada. Zooplankton community richness was regressed against TP, the spatial distribution of phytoplankton spectral groups, and the relative biomass of spectral groups. Since species richness does not adequately capture ecological function and life history of different taxa, features which are important for mechanistic theories, relationships between zooplankton functional diversity (FD) and resource conditions were examined. Zooplankton species richness showed the previously established tendency to a unimodal relationship with TP, but functional diversity declined linearly over the same gradient. Changes in zooplankton functional diversity could be attributed to changes in both the spatial distribution and type of phytoplankton resource. In the studied lakes, spatial heterogeneity of phytoplankton groups declined with TP, even while biomass of all groups increased. Zooplankton functional diversity was positively related to increased heterogeneity in cyanobacteria spatial distribution. However, a smaller amount of variation in functional diversity was also positively related to the ratio of biomass in diatoms/chrysophytes to cyanobacteria. In all observed relationships, a greater variation of functional diversity than species richness measures was explained by measured factors, suggesting that functional measures of zooplankton communities will benefit ecological research attempting to identify mechanisms behind environmental gradients affecting diversity.     

Relationships between Plant and Animal Species Richness at a Regional Scale in China

Abstract:  Important questions in conservation biology and ecology include whether species diversities of different groups of organisms are correlated and, in particular, whether plant diversity influences animal diversity. I used correlation and partial regression analyses to examine the relationships between species richness of vascular plants and four major groups of terrestrial vertebrates (mammals, amphibians, reptiles, and birds) in 28 provinces in China. Species richness data were obtained from the literature. Environmental variables included normalized difference vegetation index, mean January temperature, mean annual temperature, annual precipitation, May through August precipitation, actual evapotranspiration, potential evapotranspiration, and elevation range. Species richness was strongly and positively correlated among the five groups of organisms. Plant richness was correlated with animal richness more strongly than the richness of different animal groups correlated with each other except for reptile richness, which had a slightly higher correlation with amphibian richness than with plant richness. Plant richness uniquely explained 41 times more variance in the species richness of the four vertebrate groups combined than environmental variables uniquely did, suggesting that plant richness influences terrestrial vertebrate richness at the regional scale examined. Because of strong correlations between the diversity of vascular plants and vertebrates, the diversity of vascular plants may be used as a surrogate for the diversity of terrestrial animals in China. My results have implications for selection of areas to be protected at both regional and local scales.     

Niche complementarity for nitrogen: an explanation for the biodiversity and ecosystem functioning relationship?

The relationship between plant diversity and productivity has largely been attributed to niche complementarity, assuming that plant species are complementary in their resource use. In this context, we conducted an 15N field study in three different grasslands, testing complementarity nitrogen (N) uptake patterns in terms of space, time, and chemical form as well as N strategies such as soil N use, symbiotic N fixation, or internal N recycling for different plant species. The relative contribution of different spatial, temporal, and chemical soil N pools to total soil N uptake of plants varied significantly among the investigated plant species, within and across functional groups. This suggests that plants occupy distinct niches with respect to their relative N uptake. However, when the absolute N uptake from the different soil N pools was analyzed, no spatial, temporal, or chemical variability was detected, but plants, and in particular functional groups, differed significantly with respect to their total soil N uptake irrespective of treatment. Consequently, our data suggest that absolute N exploitation on the ecosystem level is determined by species or functional group identity and thus by community composition rather than by complementary biodiversity effects. Across functional groups, total N uptake from the soil was negatively correlated with leaf N concentrations, suggesting that these functional groups follow different N use strategies to meet their N demands. While our findings give no evidence for a biodiversity effect on the quantitative exploitation of different soil N pools, there is evidence for different and complementary N strategies and thus a potentially beneficial effect of functional group diversity on ecosystem functioning.     

Downscaling species occupancy from coarse spatial scales

The measurement and prediction of species' populations at different spatial scales is crucial to spatial ecology as well as conservation biology. An efficient yet challenging goal to achieve such population estimates consists of recording empirical species' presence and absence at a specific regional scale and then trying to predict occupancies at finer scales. So far the majority of the methods have been based on particular species' distributional features deemed to be crucial for downscaling occupancy. However, only a minority of them have dealt explicitly with specific spatial features. Here we employ a wide class of spatial point processes, the shot noise Cox processes (SNCP), to model species occupancies at different spatial scales and show that species' spatial aggregation is crucial for predicting population estimates at fine scales starting from coarser ones. These models are formulated in continuous space and locate points regardless of the arbitrary resolution that one employs to study the spatial pattern. We compare the performances of nine models, calibrated at regional scales and demonstrate that a very simple class of SNCP, the Thomas process, is able to outperform other published models in predicting occupancies down to areas four orders of magnitude smaller than the ones employed for the parameterization. We conclude by explaining the ability of the approach to infer spatially explicit information from spatially implicit measures, the potential of the framework to combine niche and spatial models, and the possibility of reversing the method to allow upscaling.     

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.     

A Successful Predictive Model of Species Richness Based on Indicator Species

Abstract:  Because complete species inventories are expensive and time-consuming, scientists and land managers seek techniques to alleviate logistic constraints on measuring species richness, especially over large spatial scales. We developed a method to identify indicators of species richness that is applicable to any taxonomic group or ecosystem. In an initial case study, we found that a model based on the occurrence of five indicator species explained 88% of the deviance of species richness of 56 butterflies in a mountain range in western North America. We validated model predictions and spatial transferability of the model using independent, newly collected data from another, nearby mountain range. Predicted and observed values of butterfly species richness were highly correlated with 93% of the observed values falling within the 95% credible intervals of the predictions. We used a Bayesian approach to update the initial model with both the model-building and model-validation data sets. In the updated model, the effectiveness of three of the five indicator species was similar, whereas the effectiveness of two species was reduced. The latter species had more erratic distributions in the validation data set than in the original model-building data set. This objective method for identifying indicators of species richness could substantially enhance our ability to conduct large-scale ecological assessments of any group of animals or plants in any geographic region and to make effective conservation decisions.     

Predicting abundance of desert riparian birds: validation and calibration of the Effective Area Model.

Reliable prediction of the effects of landscape change on species abundance is critical to land managers who must make frequent, rapid decisions with long-term consequences. However, due to inherent temporal and spatial variability in ecological systems, previous attempts to predict species abundance in novel locations and/or time frames have been largely unsuccessful. The Effective Area Model (EAM) uses change in habitat composition and geometry coupled with response of animals to habitat edges to predict change in species abundance at a landscape scale. Our research goals were to validate EAM abundance predictions in new locations and to develop a calibration framework that enables absolute abundance predictions in novel regions or time frames. For model validation, we compared the EAM to a null model excluding edge effects in terms of accurate prediction of species abundance. The EAM outperformed the null model for 83.3% of species (N=12) for which it was possible to discern a difference when considering 50 validation sites. Likewise, the EAM outperformed the null model when considering subsets of validation sites categorized on the basis of four variables (isolation, presence of water, region, and focal habitat). Additionally, we explored a framework for producing calibrated models to decrease prediction error given inherent temporal and spatial variability in abundance. We calibrated the EAM to new locations using linear regression between observed and predicted abundance with and without additional habitat covariates. We found that model adjustments for unexplained variability in time and space, as well as variability that can be explained by incorporating additional covariates, improved EAM predictions. Calibrated EAM abundance estimates with additional site-level variables explained a significant amount of variability (P < 0.05) in observed abundance for 17 of 20 species, with R2 values >25% for 12 species, >48% for six species, and >60% for four species when considering all predictive models. The calibration framework described in this paper can be used to predict absolute abundance in sites different from those in which data were collected if the target population of sites to which one would like to statistically infer is sampled in a probabilistic way.     

Beta diversity at different spatial scales: plant communities in organic and conventional agriculture.

Biodiversity studies that guide agricultural subsidy policy have generally compared farming systems at a single spatial scale: the field. However, diversity patterns vary across spatial scales. Here, we examined the effects of farming system (organic vs. conventional) and position in the field (edge vs. center) on plant species richness in wheat fields at three spatial scales. We quantified alpha-, beta-, and gamma-diversity at the microscale in 800 plots, at the mesoscale in 40 fields, and at the macroscale in three regions using the additive partitioning approach, and evaluated the relative contribution of beta-diversity at each spatial scale to total observed species richness. We found that alpha-, beta-, and gamma-diversity were higher in organic than conventional fields and higher at the field edge than in the field center at all spatial scales. In both farming systems, beta-diversity at the meso- and macroscale explained most of the overall species richness (up to 37% and 25%, respectively), indicating considerable differences in community composition among fields and regions due to environmental heterogeneity. The spatial scale at which beta-diversity contributed the most to overall species richness differed between rare and common species. Total richness of rare species (present in < or = 5% of total samples) was mainly explained by differences in community composition at the meso- and macroscale (up to 27% and 48%, respectively), but only in organic fields. Total richness of common species (present in > or = 25% of total samples) was explained by differences in community composition at the micro- and mesoscale (up to 29% and 47%, respectively), i.e., among plots and fields, independent of farming system. Our results show that organic farming made the greatest contribution to total species richness at the meso (among fields) and macro (among regions) scale due to environmental heterogeneity. Hence, agri-environment schemes should exploit this large-scale contribution of beta-diversity by tailoring schemes at regional scales to maximize dissimilarity between conservation areas using geographic information systems rather than focusing entirely at the classical local-field scale, which is the current practice.     

Estimating non-native plant richness with a species-accumulation model along roads

Monitoring non-native plant richness is important for biodiversity conservation and scientific research. The species-area model (SA model) has been used frequently to estimate the total species richness within a region. However, the conventional SA model may not provide robust estimations of non-native plant richness because the ecological processes associated with the accumulation of exotic and native plants may differ. Because roads strongly dictate the distributions of exotic plants, we propose a species-accumulation model along roads (SR model), rather than an SA model, to estimate the non-native plant richness within a region. Using 270 simulated data sets, we compared the differences in performance between the SR and SA models. A decision tree based on prediction accuracy was created to guide model application, which was validated using field data from 3 national nature reserves in 3 different provinces in China. The SR model significantly outperformed the SA model when non-native species were restricted to the roadsides and the proportion of uncommon exotic species was small. More importantly, the SR model accurately estimated the non-native plant richness in all field sites with an error of <1 species per site. We believe our new model meets the practical need to efficiently and robustly estimate non-native plant richness, which may facilitate effective biodiversity conservations and promote research on non-native plant invasion and vegetation dynamics.     

Earthworms and legumes control litter decomposition in a plant diversity gradient

The role of species and functional group diversity of primary producers for decomposers and decomposition processes is little understood. We made use of the "Jena Biodiversity Experiment" and tested the hypothesis that increasing plant species (1, 4, and 16 species) and functional group diversity (1, 2, 3, and 4 groups) beneficially affects decomposer density and activity and therefore the decomposition of plant litter material. Furthermore, by manipulating the densities of decomposers (earthworms and springtails) within the plant diversity gradient we investigated how the interactions between plant diversity and decomposer densities affect the decomposition of litter belonging to different plant functional groups (grasses, herbs, and legumes). Positive effects of increasing plant species or functional group diversity on earthworms (biomass and density) and microbial biomass were mainly due to the increased incidence of legumes with increasing diversity. Neither plant species diversity nor functional group diversity affected litter decomposition, However, litter decomposition varied with decomposer and plant functional group identity (of both living plants and plant litter). While springtail removal generally had little effect on decomposition, increased earthworm density accelerated the decomposition of nitrogen-rich legume litter, and this was more pronounced at higher plant diversity. The results suggest that earthworms (Lumbricus terrestris L.) and legumes function as keystone organisms for grassland decomposition processes and presumably contribute to the recorded increase in primary productivity with increasing plant diversity.     

Spatial predictions of phylogenetic diversity in conservation decision making

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.     

Seed availability and insect herbivory limit recruitment and adult density of native tall thistle

Understanding spatial and temporal variation in factors influencing plant regeneration is critical to predicting plant population growth. We experimentally evaluated seed limitation, insect herbivory, and their interaction in the regeneration and density of tall thistle (Cirsium altissimum) across a topographic ecosystem productivity gradient in tallgrass prairie over two years. On ridges and in valleys, we used a factorial experiment manipulating seed availability and insect herbivory to quantify effects of: seed input on seedling density, insect herbivory on juvenile density, and cumulative impacts of both seed input and herbivory on reproductive adult density. Seed addition increased seedling densities at three of five sites in 2006 and all five sites in 2007. Insect herbivory reduced seedling survival across all sites in both years, as well as rosette survival from the previous year's seedlings. In both years, insecticide treatment of seed addition plots led to greater adult tall thistle densities in the following year, reflecting the increase in juvenile thistle densities in the experimental year. Seedling survival was not density dependent. Our analytical projection model predicts a significant long-term increase in adult densities from seed input, with a greater increase under experimentally reduced insect herbivory. While plant community biomass and water stress varied significantly between ridges and valleys, the effects of seed addition and insect herbivory did not vary with gradient position. These results support conceptual models that predict seedling and adult densities of short-lived monocarpic perennial plants should be seed limited. Further, the experiment demonstrates that even at high juvenile plant densities, at which density dependence potentially could have overridden herbivore effects on plant survival, insect herbivory strongly affected juvenile thistle performance and adult densities of this native prairie species.     

Increased primary production shifts the structure and composition of a terrestrial arthropod community

Numerous studies have examined relationships between primary production and biodiversity at higher trophic levels. However, altered production in plant communities is often tightly linked with concomitant shifts in diversity and composition, and most studies have not disentangled the direct effects of production on consumers. Furthermore, when studies do examine the effects of plant production on animals in terrestrial systems, they are primarily confined to a subset of taxonomic or functional groups instead of investigating the responses of the entire community. Using natural monocultures of the salt marsh cordgrass Spartina alterniflora, we were able to examine the impacts of increased plant production, independent of changes in plant composition and/or diversity, on the trophic structure, composition, and diversity of the entire arthropod community. If arthropod species richness increased with greater plant production, we predicted that it would be driven by: (1) an increase in the number of rare species, and/or (2) an increase in arthropod abundance. Our results largely supported our predictions: species richness of herbivores, detritivores, predators, and parasitoids increased monotonically with increasing levels of plant production, and the diversity of rare species also increased with plant production. However, rare species that accounted for this difference were predators, parasitoids, and detritivores, not herbivores. Herbivore species richness could be simply explained by the relationship between abundance and diversity. Using nonmetric multidimensional scaling (NMDS) and analysis of similarity (ANOSIM), we also found significant changes in arthropod species composition with increasing levels of production. Our findings have important implications in the intertidal salt marsh, where human activities have increased nitrogen runoff into the marsh, and demonstrate that such nitrogen inputs cascade to affect community structure, diversity, and abundance in higher trophic levels.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.