Predictor Of Species Richness Research Articles

Gap ecology in the Florida scrubby flatwoods: effects of time‐since‐fire, gap area, gap aggregation and microhabitat on gap species diversity

AbstractQuestionsThe ecology of landscape gaps is important for the maintenance of species diversity in a variety of plant ecosystems. In the scrubby flatwoods community type of the pyrogenic Florida scrub, we aimed to identify the relationship between time‐since‐fire, gap area, quality (microhabitat) and aggregation (structural connectivity) with neighbouring gaps on gap species diversity and occupancy.LocationArchbold Biological Station (ABS), Lake Wales Ridge, south‐central Florida, USA.MethodsWe measured species diversity, gap area, aggregation and microhabitat (percentage bare sand) in 120 gaps divided evenly amongst 1, 2, 4, 8, 16 and 26 yrs since fire in the scrubby flatwoods of ABS (typical fire return interval of 5–20 yrs) throughout the summer of 2011.ResultsGap area and aggregation were significantly highest in the first 4 yrs after fire, but decreased and remained fairly constant at longer times‐since‐fire. Percentage bare sand gradually decreased with time‐since‐fire until a strong decline at the 26‐yr mark, where litter cover was significantly higher than all previous years. Gap area strongly and positively predicted species richness (total and herb spp.) and total species diversity (H'). Bare sand content was positively correlated with the richness of species that are obligate seeders post‐fire. Gap aggregation was generally a poor predictor of diversity components, although it positively influenced the occurrence of Stipulicida setacea, a seeder species that occasionally resprouts after fire. Overall, species richness and diversity peaked at intermediate times‐since‐fire, probably because long‐unburned gaps were too small and inundated with litter to sustain higher richness of herbs and obligate seeders.ConclusionIn a prior gap ecology study, in neighbouring more xeric rosemary scrub, gaps were larger, more persistent and had higher bare sand cover, resulting in higher levels of total species richness and diversity for decades longer after fire than gaps in this study. In both these types of Florida scrub, the threat of fire suppression and its adverse effects on gap area and microhabitat may be detrimental for several endemic species that depend on gaps.

Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images

Capsule Variables obtained from airborne laser-scanning (ALS) enabled slight or fair predictions of bird presence, and including multispectral data further improved predictions slightly.Aims To assess the usefulness of ALS as a tool for predicting species richness and single-species presence, and to investigate if including information from multispectral aerial images further improved predictability of bird presence.Methods Bird presence data were sampled in a Norwegian boreal forest reserve. Prediction models were developed for species richness and presence of the eight most abundant species by the use of two different modelling approaches: generalized linear models and the machine learning method random forest. Predictor variables were descriptors of three-dimensional forest structure obtained by ALS, and descriptors of tree species composition obtained from multispectral aerial images.Results Cross-validation of the prediction models indicated overall slight or fair predictive capability. Best predictions were obtained for Goldcrest, Wren, and Willow Warbler. Inclusion of spectral variables derived from the aerial imagery slightly improved the predictive performance of several models, most notably for Willow Warbler.Conclusion We suggest that predictability of species richness and presence of single bird species can be improved by better matching of the scale of recording for birds and the predictor variables obtained by remote sensing.

Is there an optimum scale for predicting bird species' distribution in agricultural landscapes?

Changes in forest cover in agricultural landscapes affect biodiversity. Its management needs some indications about scale to predict occurrence of populations and communities. In this study we considered a forest cover index to predict bird species and community patterns in agricultural landscapes in south-western France. We used generalized linear models for that purpose with prediction driven by wooded areas' spatial distribution at nine different radii.Using 1064 point counts, we modelled the distribution of 10 bird species whose habitat preferences are spread along a landscape opening gradient. We also modelled the distribution of species richness for farmland species and for forest species. We used satellite images to construct a ‘wood/non-wood’ map and calculated a forest index, considering the surface area of wooded areas at nine radii from 110m to 910m. The models' predictive quality was determined by the AUC (for predicted presences) and ρ (for predicted species richness) criteria.We found that the forest cover was a good predictor of the distribution of seven bird species in agricultural landscapes (mean AUC for the seven species = 0.74 for the radius 110m). Species richness of farmland and forest birds was satisfactorily predicted by the models (ρ = 0.55 and 0.49, respectively, for the radius 110m). The presence of the studied species and species richness metrics were better predicted at smaller scales (i.e. radii between 110 m and 310 m) within the range tested.These results have implications for bird population management in agricultural landscapes since better pinpointing the scale to predict species distributions will enhance targeting efforts to be made in terms of landscape management.

Diversity of Malaysian insectivorous bat assemblages revisited

Abstract:To what extent tropical forest persisted in the Malay-Thai Peninsula during the Last Glacial Maximum, or contracted southwards with subsequent post-glacial expansion, has long been debated. These competing scenarios might be expected to have left contrasting broad spatial patterns of diversity of forest-dependent taxa. To test for a post-glacial northward spread of forest, we examined latitudinal clines of forest-dependent bat species at 15 forest sites across Peninsular Malaysia. From captures of 3776 insectivorous forest bats, we found that low richness characterized the north of the study area: predicted richness of 9–16 species, compared with 21–23 in the south. Predicted species richness decreased significantly with increasing latitude, but showed no relationship with either seasonality or peninsula width. Analyses of beta-diversity showed that differences between communities were not related to geographical distance, although there was evidence of greater differences in species numbers between the most distant sites. Assemblages were consistently dominated by six cave-roosting species from the families Rhinolophidae and Hipposideridae, while another 16 species were consistently rare. We suggest that these observed patterns are consistent with the hypothesized northward expansion of tropical rain forest since the Last Glacial Maximum, but emphasize that more surveys in the extreme north and south of the peninsula are required to support this assertion.

Birds and small urban parks: a study in a high plateau city

The goal of the present study was to assess which variables influence the distribution of birds in urban parks. Twenty parks were studied in a city on the Spanish high plateau during the breeding season, and relationships among park characteristics, bird richness, and individual bird presences were established. The park area and tree cover accounted for 73.1% of the variation in bird richness. Although area size was the best predictor of species richness (65% of the variation), some species did not seem to be related to this variable but rather to others, such as vegetation and the degree of park isolation with respect to other areas. Contrary to what is normally expected, the distance from our parks to the river did not influence their bird species richness.

Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines

AbstractAimRemote sensing data have been used in a growing number of studies to directly predict species richness or to improve the performance of species distribution models (SDMs), but their suitability for stacked species distribution models (S‐SDMs) remains unclear. In this case study, we evaluated the potential and limitations of remotely sensed data in S‐SDMs and addressed the commonly observed overestimation of species richness by S‐SDMs.LocationMexico.MethodsPhenological and statistical metrics were derived from remotely sensed time series data (2001–2009) of the Terra‐MODIS enhanced vegetation index and land surface temperature products. In a series of climatic and remote sensing‐based SDMs, the distribution ranges of 40 species of the genus Pinus (Pinaceae) were modelled based on presence‐only herbarium and field data using the maximum entropy algorithm and summed to estimate species richness. Three different species‐specific thresholds were applied to convert continuous model predictions into binary maps. Modelled species richness was compared to independent data from the Mexican National Forest Inventory.ResultsThe inclusion of remote sensing data led to significantly better predictions of species richness in comparison to the climate‐based models for the summed suitabilities and all thresholds considered. Both climatic and remote sensing‐based models allowed us to identify the areas with the highest pine species richness based on presence‐only data. Remote sensing‐based models compare closely with climate‐derived patterns, but provide better spatial resolution and more detailed information on local habitat availability.Main conclusionsThe results of this case study provide general guidance for the potential and limitations of using remote sensing data in S‐SDMs. Our results confirmed that remote sensing data may not only have the capability for improving individual SDMs, but also can be a potential tool for reducing the overestimation of species richness by S‐SDMs. This approach opens up new possibilities for species richness predictions in areas where biological survey data are scarce and where no species richness inventory data exist.

Terrestrial gastropod diversity in an alpine region: disentangling effects of elevation, area, geometric constraints, habitat type and land‐use intensity

Elevational gradients have proven to be useful to examine key factors shaping species diversity patterns. This study examines the effects of elevation, area, geometric constraints, habitat type, environmental factors and land‐use intensity on terrestrial gastropod diversity patterns in Val Müstair, an alpine region influenced by different types of agricultural land use in the eastern Alps, Switzerland. Gastropods were sampled using a standardized method in 180 sites spanning an elevational range from 1215 to 2770 m and covering 11 different habitat types. A total of 11 102 specimens representing 70 species were recorded. Observed species richness, statistically estimated true richness (Chao) and geographically interpolated observed richness were used as measures of local species richness. The comparison of three alternative models (environmental, geometric constraints and gastropod abundance models) revealed that the environmental model explained most of the variation in all measures of local diversity. The best model combining the predictors of all three models showed that elevation, soil pH and habitat type affected all measures of local species richness. Similar analyses conducted at the level of 150‐m elevational bands showed that elevation was again the best predictor of species richness, while the area of the elevational band did not have any influence. However, in one out of the two measures of band species richness, the best model indicated that geometric constraints may also contribute to the observed pattern. At both spatial scales, all measures of species richness decreased with increasing elevation. An analysis of species‐specific life‐history traits showed that adult shell size of land snails decreased with increasing elevation. Most species with large shells were confined to lower elevations. The results indicate that environmental factors might be most important in shaping the observed patterns.

Southeast Asian primate communities: the effects of ecology and Pleistocene refuges on species richness

We examined historical and ecological factors affecting current primate biodiversity in Southeast Asia. In Africa, Madagascar and South America, but not Southeast Asia, primate species richness is positively associated with average rainfall and distance from the equator (latitude). We predicted that Southeast Asia's non-conformance may be due to the effect of dispersed Pleistocene refuges (locations of constricted tropical forests during glacial maxima which today are at least 305 m in altitude). Based on 45 forested sites (13 on large islands; 32 on the mainland) of at least 100 km(2) to minimize recent human impact, we determined correlations between extant primate species richness and rainfall, latitude and supplementary ecological variables, while controlling for refuges and islands. We found that refuge sites had significantly higher primate species richness than non-refuges (t = -2.76, P < 0.05), and distance from the nearest Pleistocene refuge was negatively correlated with species richness for non-refuge sites (r = -0.51, P < 0.05). There was no difference in species richness between sites on large islands and the mainland (t = -1.4, P = 0.16). The expected positive relationship between rainfall and species richness was not found (r = 0.17, P = 0.28). As predicted, primate species richness was negatively correlated with latitude (r = -0.39, P < 0.05) and positively correlated with mean temperature (r = 0.45, P < 0.05). General linear models indicated that a site's latitude (F1,38 = 6.18, P < 0.05) and Pleistocene refuge classification (F1,42 = 5.96, P < 0.05) were the best predictors of species richness. Both ecological and historical factors contribute to present day primate species richness in Southeast Asia, making its biodiversity less of an outlier than previously believed.

Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale

AbstractAimThe species–area relationship (SAR) is a prominent concept for predicting species richness and biodiversity loss. A key step in defining SARs is to accurately estimate the slope of the relationship, but researchers typically apply only one global (canonical) slope. We hypothesized that this approach is overly simplistic and investigated how geographically varying determinants of SARs affect species richness estimates of vascular plants at the global scale.LocationGlobal.MethodsWe used global species richness data for vascular plants from 1032 geographical units varying in size and shape. As possible determinants of geographical variation in SARs we chose floristic kingdoms and biomes as biogeographical provinces, and land cover as a surrogate for habitat diversity. Using simultaneous autoregressive models we fitted SARs to each set of determinants, compared their ability to predict the observed data and large‐scale species richness patterns, and determined the extent to which varying SARs differed from the global relationship.ResultsIncorporating variation into SARs improved predictions of global species richness patterns. The best model, which accounts for variation due to biomes, explained 46.1% of the species richness variation. Moreover, fitting SARs to biomes produced better results than fitting them to floristic kingdoms, supporting the hypothesis that energy availability complements evolutionary history in generating species richness patterns. Land cover proved to be less important than biomes, explaining only 36.4% of the variation, possibly owing to the high uncertainty in the data set. The incorporation of second‐order interactions of area, land cover and biomes did not improve the predictive ability of the models.Main conclusionsOur study contributes to a deeper understanding of SARs and improves the applicability of SARs through regionalization. Future models should explicitly consider geographically varying determinants of SARs in order to improve our assessment of the impact of global change scenarios on species richness patterns.

New metric of microhabitat complexity predicts species richness on a rocky shore

AbstractMost habitat complexity–species diversity relationships are specific to the habitat type, taxonomic group and spatial scale investigated. There is a need for a biologically relevant metric of habitat complexity that is transferable across systems and scales. Here, we introduce a novel metric of landscape microhabitat complexity that may overcome such constraints. It is scaled to the size of individual organisms and it numerically captures structural elements of the habitat that are biologically relevant to individuals. Our model ecosystem to develop this metric was a rocky shore in Eastern Australia. Here species richness of invertebrates was linked to habitat complexity quantified by the new metric. Importantly, measurements made at the scale of the organism predicted species richness at the landscape scale. Testing the generality of this approach across different systems may prove useful in a broader conservation biology context.

Diversity of Eastern North American Ant Communities along Environmental Gradients

Studies of species diversity patterns across regional environmental gradients seldom consider the impact of habitat type on within-site (alpha) and between-site (beta) diversity. This study is designed to identify the influence of habitat type across geographic and environmental space, on local patterns of species richness and regional turnover patterns of ant diversity in the northeastern United States. Specifically, I aim to 1) compare local species richness in paired open and forested transects and identify the environmental variables that best correlate with richness; and 2) document patterns of beta diversity throughout the region in both open and forested habitat. I systematically sampled ants at 67 sites from May to August 2010, spanning 10 degrees of latitude, and 1000 meters of elevation. Patterns of alpha and beta diversity across the region and along environmental gradients differed between forested and open habitats. Local species richness was higher in the low elevation and warmest sites and was always higher in open habitat than in forest habitat transects. Richness decreased as temperature decreased or elevation increased. Forested transects show strong patterns of decreasing dissimilarity in species composition between sites along the temperature gradient but open habitat transects did not. Maximum temperature of the warmest month better predicted species richness than either latitude or elevation. I find that using environmental variables as key predictors of richness yields more biologically relevant results, and produces simpler macroecological models than commonly used models which use only latitude and elevation as predictors of richness and diversity patterns. This study contributes to the understanding of mechanisms that structure the communities of important terrestrial arthropods which are likely to be influenced by climatic change.

Factors affecting species richness of marine elasmobranchs

Many studies on elasmobranchs, sharks and batoids (rays, skates and guitarfishes), have focused on the factors responsible for biomass decline, but little attention has been paid to the factors that affect species richness. We used the software package ModestR to determine the geographical distribution of all valid marine elasmobranch species (512 species of sharks and 619 species of batoids), thereby making it possible to determine the species composition of the elasmobranch community in any area worldwide. The primary aim of this study was to identify the factors associated with the species richness of elasmobranchs. The data were analyzed using multiple regressions and Support Vector Machine (SVM) in cells of 1o× 1o with the analyzed abiotic variables being bathymetry, chlorophyll a, sea surface temperature, photosynthetically available radiation, pH, cloud cover, the concentrations of calcite, silicate, phosphate and nitrate, salinity, particulate organic carbon, diffuse attenuation and dissolved oxygen. The mean area of occupancy of the species was used as an indicator of niche occupancy. The model performed with SVM explained 97 and 99 % of the variance observed in the species richness of batoids and sharks, respectively. Mean area of occupancy, temperature and bathymetry were the variables with a higher contribution to the variance observed in both sharks and batoids. The negative residuals of the model performed with SVM indicated areas with lower than predicted species richness. These may be potential areas with undiscovered and/or unregistered species, or areas with decreased species richness due to the negative effect of anthropogenic factors, i.e. overfishing

Image Texture Predicts Avian Density and Species Richness

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas.

Global patterns and predictors of tropical reef fish species richness

In the marine realm, the tropics host an extraordinary diversity of taxa but the drivers underlying the global distribution of marine organisms are still under scrutiny and we still lack an accurate global predictive model. Using a spatial database for 6336 tropical reef fishes, we attempted to predict species richness according to geometric, biogeographical and environmental explanatory variables. In particular, we aimed to evaluate and disentangle the predictive performances of temperature, habitat area, connectivity, mid‐domain effect and biogeographical region on reef fish species richness. We used boosted regression trees, a flexible machine‐learning technique, to build our predictive model and structural equation modeling to test for potential ‘mediation effects’ among predictors. Our model proved to be accurate, explaining 80% of the total deviance in fish richness using a cross‐validated procedure. Coral reef area and biogeographical region were the primary predictors of reef fish species richness, followed by coast length, connectivity, mid‐domain effect and sea surface temperature, with interactions between the region and other predictors. Important indirect effects of water temperature on reef fish richness, mediated by coral reef area, were also identified. The relationship between environmental predictors and species richness varied markedly among biogeographical regions. Our analysis revealed that a few easily accessible variables can accurately predict reef fish species richness. They also highlight concerns regarding ongoing environmental declines, with region‐specific responses to variation in environmental conditions predicting a variable response to anthropogenic impacts.

The impact of modelling choices in the predictive performance of richness maps derived from species‐distribution models: guidelines to build better diversity models

Summary The stacking of species‐distribution models (S‐SDMs) is receiving attention by conservation researchers because this approach is capable of simultaneously predicting species richness and composition. However, the steps required to build S‐SDMs implies at least two choices that influence its predictive performance which have not been extensively assessed: the selection of the modelling algorithm and the application of a threshold to transform the species‐distribution models into binary maps to be added together to build the final S‐SDM. Our goal was to provide guidelines concerning the best combinations of modelling algorithms and thresholds with which to build more accurate S‐SDMs. We generated 380 S‐SDMs of 1224 tree species in Mesoamerica by combining 19 distribution modelling methods with 20 different thresholds using presence‐only data from the Global Biodiversity Information Facility. We compared the predicted richness and composition with inventory data obtained from the BIOTREE‐NET forest plot database. We designed two indicators of predictive performance that were based on the diversity factors used to measure species turnover: a (shared species between the observed and predicted compositions), b and c (the exclusive species of the predicted and observed compositions respectively) and compared them with the Sorensen and Beta‐Simpson turnover measures. Our proposed indexes and the Sorensen index proved suitable as indicators of predictive performance for S‐SDMs, whereas the Beta‐Simpson turnover measure presented issues that would prevent its application to evaluate S‐SDMs. Some modelling methods – especially machine learning and ensemble model forecasting methods performed significantly better than others in minimizing the error in predicted richness and composition. Our results also points out that restrictive thresholds (with high omission errors) lead to more accurate S‐SDMs in terms of species richness and composition. Here, we demonstrate that particular combinations of modelling methods and thresholds provide results with higher predictive performance. These results provide clear modelling guidelines that will help S‐SDM modellers to select the appropriate combination of modelling methods and thresholds to build more accurate S‐SDMs, and therefore will have a positive impact on the quality of the diversity models used to assist conservation planning.

Woody Species Regeneration in Atlantic Forest Restoration Sites Depends on Surrounding Landscape

Both passive and active restoration are limited by diaspore arrival from neighboring sources. Surrounding landscape is thus important for restoration success in fragmented landscapes, where reforestation, for long periods, may be limited to the planted species pool. We sampled woody species regeneration in 17 reforestation sites in Southern Brazil to investigate the effects of distance to seed sources and amount of remaining nearby Atlantic Forest habitat. The abundance and species richness of regenerating plants were explained by multiple regressions performed using plantation age and species richness, distance to the nearest patch, and surrounding habitat. Distance to the nearest forest remnant, through both the matrix in a straight line and riparian vegetation, was the best predictor of species richness and abundance of regenerating plants. Riparian corridors doubled the distance at which forest remnants influenced restoration sites. However, the area of forest remnants in the site neighborhood did not influence regeneration diversity, suggesting that the quality of both the seed source (including the status of seed dispersing fauna) and reforestation environment for the establishment of forest species should be investigated.

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

BackgroundThe quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.Methodology/Principal FindingsWe model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.Conclusions/SignificanceTexture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.

Shrubby today but not tomorrow? Structure, composition and regeneration dynamics of direct seeded revegetation

Summary The establishment of direct seeded revegetation is well researched. However, there is little understanding of whether revegetation simplifies with age and loses many of the short‐lived understorey shrub species that provide critical resources for birds and other fauna, or regenerates sufficiently to be self‐sustaining. We sought to address this by investigating the change in structure and composition of 33 direct seeded sites established by Greening Australia between 1990 and 1996 in the Southern Tablelands of NSW. Transects were used to collect data describing the abundance and richness of woody plants in 1998 and 2008, and the abundance of woody plant regeneration in 2008. Our analysis showed the predicted number of live stems per metre declined exponentially from 5.8 stems/m (∼17 000 stems/ha) at age 1.5 years to 1.5 stems/m (∼4500 stems/ha) at age 17.5 years. Predicted woody plant species richness also declined with age, with a linear relationship. The number of species in the seed mix affected predicted woody plant species richness. However, large increases in seed mix richness produced relatively small increases in predicted species richness. Regeneration (new stems) was present at high levels (&gt;100 stems/ha); however, 82% of regeneration appeared to originate from delayed germination of sown seed rather than seed from established plants (recruitment). The predicted abundance of new stems (delayed germination and recruitment) declined with age, and for a given age increased with row width. Young stands (11.5 years), seeded with wide rows (4 m), had approximately six times the new stems of similar aged stands seeded with narrow rows (2 m). Our results indicate direct seeded stands simplify with age, becoming less dense and containing fewer species. Maintaining a diversity of shrub species in direct seeded sites may therefore require ongoing disturbances (scarifying, scalping, fire, thinning) of established sites and changes to establishment techniques for new sites. We suggest further research to establish the status of soil seed banks in direct seeded sites, testing different forms of disturbances to trigger regeneration in older revegetation sites and establishing new sites using wide rather than narrow rows and seed mixes enriched with species from genera other than Eucalyptus or Acacia.

Clade Age and Species Richness Are Decoupled Across the Eukaryotic Tree of Life

Explaining the dramatic variation in species richness across the tree of life remains a key challenge in evolutionary biology. At the largest phylogenetic scales, the extreme heterogeneity in species richness observed among different groups of organisms is almost certainly a function of many complex and interdependent factors. However, the most fundamental expectation in macroevolutionary studies is simply that species richness in extant clades should be correlated with clade age: all things being equal, older clades will have had more time for diversity to accumulate than younger clades. Here, we test the relationship between stem clade age and species richness across 1,397 major clades of multicellular eukaryotes that collectively account for more than 1.2 million described species. We find no evidence that clade age predicts species richness at this scale. We demonstrate that this decoupling of age and richness is unlikely to result from variation in net diversification rates among clades. At the largest phylogenetic scales, contemporary patterns of species richness are inconsistent with unbounded diversity increase through time. These results imply that a fundamentally different interpretative paradigm may be needed in the study of phylogenetic diversity patterns in many groups of organisms.

A remote sensing approach to biodiversity assessment and regionalization of the Canadian boreal forest

Successful conservation planning for the Canadian boreal forest requires biodiversity data that are both accessible and reliable. Spatially exhaustive data is required to inform on conditions, trends and context, with context enabling consideration of conservation opportunities and related trade-offs. However, conventional methods for measuring biodiversity, while useful, are spatially constrained, making it difficult to apply over wide geographic regions. Increasingly, remotely sensed imagery and methods are seen as a viable approach for acquiring explicit, repeatable and multi-scale biodiversity data over large areas. To identify relevant remotely derived environmental indicators specific to biodiversity within the Canadian boreal forest, we assessed indicators of the physical environment such as seasonal snow cover, topography and vegetation production. Specifically, we determined if the indicators provided distinct information and whether they were useful predictors of species richness (tree, mammal, bird and butterfly species). Using cluster analysis, we also assessed the applicability of these indicators for broad ecosystem classification of the Canadian boreal forest and the subsequent attribution of these stratified regions (i.e. clusters). Our results reveal that the indicators used in the cluster creation provided unique information and explained much of the variance in tree (92.6%), bird (84.07%), butterfly (61.4%) and mammal (22.6%) species richness. Spring snow cover explained the most variance in species richness. Results further show that the 15 clusters produced using cluster analysis were principally stratified along a latitudinal gradient and, while varied in size, captured a range of different environmental conditions across the Canadian boreal forest. The most important indicators for discriminating between the different cluster groups were seasonal greenness, a multipart measure of climate, topography and land use, and wetland cover, a measure of the percentage of wetland within a 1 km2 cell.