Human activities, especially conversion and degradation of habitats, are causing global biodiversity declines. How local ecological assemblages are responding is less clear--a concern given their importance for many ecosystem functions and services. We analysed a terrestrial assemblage database of unprecedented geographic and taxonomic coverage to quantify local biodiversity responses to land use and related changes. Here we show that in the worst-affected habitats, these pressures reduce within-sample species richness by an average of 76.5%, total abundance by 39.5% and rarefaction-based richness by 40.3%. We estimate that, globally, these pressures have already slightly reduced average within-sample richness (by 13.6%), total abundance (10.7%) and rarefaction-based richness (8.1%), with changes showing marked spatial variation. Rapid further losses are predicted under a business-as-usual land-use scenario; within-sample richness is projected to fall by a further 3.4% globally by 2100, with losses concentrated in biodiverse but economically poor countries. Strong mitigation can deliver much more positive biodiversity changes (up to a 1.9% average increase) that are less strongly related to countries' socioeconomic status.

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Global effects of land use on local terrestrial biodiversity

Human activities are not random in their negative and positive impacts on biotas. Emerging evidence shows that most species are declining as a result of human activities ('losers') and are being replaced by a much smaller number of expanding species that thrive in human-altered environments ('winners'). The result will be a more homogenized biosphere with lower diversity at regional and global scales. Recent data also indicate that the many losers and few winners tend to be non-randomly distributed among higher taxa and ecological groups, enhancing homogenization.

Biotic homogenization: a few winners replacing many losers in the next mass extinction

Generalized Linear Models (2nd ed.)

1 Introduction and Overview.- 1.1 Objectives.- 1.2 Multivariate Statistics: An Ecological Perspective.- 1.3 Multivariate Description and Inference.- 1.4 Multivariate Confusion!.- 1.5 Types of Multivariate Techniques.- 1.5.1 Ordination.- 1.5.2 Cluster Analysis.- 1.5.3 Discriminant Analysis.- 1.5.4 Canonical Correlation Analysis.- 2 Ordination: Principal Components Analysis.- 2.1 Objectives.- 2.2 Conceptual Overview.- 2.2.1 Ordination.- 2.2.2 Principal Components Analysis (PCA).- 2.3 Geometric Overview.- 2.4 The Data Set.- 2.5 Assumptions.- 2.5.1 Multivariate Normality.- 2.5.2 Independent Random Sample and the Effects of Outliers.- 2.5.3 Linearity.- 2.6 Sample Size Requirements.- 2.6.1 General Rules.- 2.6.2 Specific Rules.- 2.7 Deriving the Principal Components.- 2.7.1 The Use of Correlation and Covariance Matrices.- 2.7.2 Eigenvalues and Associated Statistics.- 2.7.3 Eigenvectors and Scoring Coefficients.- 2.8 Assessing the Importance of the Principal Components.- 2.8.1 Latent Root Criterion.- 2.8.2 Scree Plot Criterion.- 2.8.3 Broken Stick Criterion.- 2.8.4 Relative Percent Variance Criterion.- 2.8.5 Significance Tests.- 2.9 Interpreting the Principal Components.- 2.9.1 Principal Component Structure.- 2.9.2 Significance of Principal Component Loadings.- 2.9.3 Interpreting the Principal Component Structure.- 2.9.4 Communality.- 2.9.5 Principal Component Scores and Associated Plots.- 2.10 Rotating the Principal Components.- 2.11 Limitations of Principal Components Analysis.- 2.12 R-Factor Versus Q-Factor Ordination.- 2.13 Other Ordination Techniques.- 2.13.1 Polar Ordination.- 2.13.2 Factor Analysis.- 2.13.3 Nonmetric Multidimensional Scaling.- 2.13.4 Reciprocal Averaging.- 2.13.5 Detrended Correspondence Analysis.- 2.13.6 Canonical Correspondence Analysis.- Appendix 2.1.- 3 Cluster Analysis.- 3.1 Objectives.- 3.2 Conceptual Overview.- 3.3 The Definition of Cluster.- 3.4 The Data Set.- 3.5 Clustering Techniques.- 3.6 Nonhierarchical Clustering.- 3.6.1 Polythetic Agglomerative Nonhierarchical Clustering.- 3.6.2 Polythetic Divisive Nonhierarchical Clustering.- 3.7 Hierarchical Clustering.- 3.7.1 Polythetic Agglomerative Hierarchical Clustering.- 3.7.2 Polythetic Divisive Hierarchical Clustering.- 3.8 Evaluating the Stability of the Cluster Solution.- 3.9 Complementary Use of Ordination and Cluster Analysis.- 3.10 Limitations of Cluster Analysis.- Appendix 3.1.- 4 Discriminant Analysis.- 4.1 Objectives.- 4.2 Conceptual Overview.- 4.2.1 Overview of Canonical Analysis of Discriminance.- 4.2.2 Overview of Classification.- 4.2.3 Analogy with Multiple Regression Analysis and Multivariate Analysis of Variance.- 4.3 Geometric Overview.- 4.4 The Data Set.- 4.5 Assumptions.- 4.5.1 Equality of Variance-Covariance Matrices.- 4.5.2 Multivariate Normality.- 4.5.3 Singularities and Multicollinearity.- 4.5.4 Independent Random Sample and the Effects of Outliers.- 4.5.5 Prior Probabilities Are Identifiable.- 4.5.6 Linearity 153.- 4.6 Sample Size Requirements.- 4.6.1 General Rules.- 4.6.2 Specific Rules.- 4.7 Deriving the Canonical Functions.- 4.7.1 Stepwise Selection of Variables.- 4.7.2 Eigenvalues and Associated Statistics.- 4.7.3 Eigenvectors and Canonical Coefficients.- 4.8 Assessing the Importance of the Canonical Functions.- 4.8.1 Relative Percent Variance Criterion.- 4.8.2 Canonical Correlation Criterion.- 4.8.3 Classification Accuracy.- 4.8.4 Significance Tests.- 4.8.5 Canonical Scores and Associated Plots.- 4.9 Interpreting the Canonical Functions.- 4.9.1 Standardized Canonical Coefficients.- 4.9.2 Total Structure Coefficients.- 4.9.3 Covariance-Controlled Partial F-Ratios.- 4.9.4 Significance Tests Based on Resampling Procedures.- 4.9.5 Potency Index.- 4.10 Validating the Canonical Functions.- 4.10.1 Split-Sample Validation.- 4.10.2 Validation Using Resampling Procedures.- 4.11 Limitations of Discriminant Analysis.- Appendix 4.1.- 5 Canonical Correlation Analysis.- 5.1 Objectives.- 5.2 Conceptual Overview.- 5.3 Geometric Overview.- 5.4 The Data Set.- 5.5 Assumptions.- 5.5.1 Multivariate Normality.- 5.5.2 Singularities and Multicollinearity.- 5.5.3 Independent Random Sample and the Effects of Outliers.- 5.5.4 Linearity.- 5.6 Sample Size Requirements.- 5.6.1 General Rules.- 5.6.2 Specific Rules.- 5.7 Deriving the Canonical Variates.- 5.7.1 The Use of Covariance and Correlation Matrices.- 5.7.2 Eigenvalues and Associated Statistics.- 5.7.3 Eigenvectors and Canonical Coefficients.- 5.8 Assessing the Importance of the Canonical Variates.- 5.8.1 Canonical Correlation Criterion.- 5.8.2 Canonical Redundancy Criterion.- 5.8.3 Significance Tests.- 5.8.4 Canonical Scores and Associated Plots.- 5.9 Interpreting the Canonical Variates.- 5.9.1 Standardized Canonical Coefficients.- 5.9.2 Structure Coefficients.- 5.9.3 Canonical Cross-Loadings.- 5.9.4 Significance Tests Based on Resampling Procedures.- 5.10 Validating the Canonical Variates.- 5.10.1 Split-Sample Validation.- 5.10.2 Validation Using Resampling Procedures.- 5.11 Limitations of Canonical Correlation Analysis.- Appendix 5.1.- 6 Summary and Comparison.- 6.1 Objectives.- 6.2 Relationship Among Techniques.- 6.2.1 Purpose and Source of Variation Emphasized.- 6.2.2 Statistical Procedure.- 6.2.3 Type of Statistical Technique and Variable Set Characteristics.- 6.2.4 Data Structure.- 6.2.5 Sampling Design.- 6.3 Complementary Use of Techniques.- Appendix: Acronyms Used in This Book.

Multivariate statistics for wildlife and ecology research

The future of tropical forest biodiversity depends more than ever on the effective management of human-modified landscapes, presenting a daunting challenge to conservation practitioners and land use managers. We provide a critical synthesis of the scientific insights that guide our understanding of patterns and processes underpinning forest biodiversity in the human-modified tropics, and present a conceptual framework that integrates a broad range of social and ecological factors that define and contextualize the possible future of tropical forest species. A growing body of research demonstrates that spatial and temporal patterns of biodiversity are the dynamic product of interacting historical and contemporary human and ecological processes. These processes vary radically in their relative importance within and among regions, and have effects that may take years to become fully manifest. Interpreting biodiversity research findings is frequently made difficult by constrained study designs, low congruence in species responses to disturbance, shifting baselines and an over-dependence on comparative inferences from a small number of well studied localities. Spatial and temporal heterogeneity in the potential prospects for biodiversity conservation can be explained by regional differences in biotic vulnerability and anthropogenic legacies, an ever-tighter coupling of human-ecological systems and the influence of global environmental change. These differences provide both challenges and opportunities for biodiversity conservation. Building upon our synthesis we outline a simple adaptive-landscape planning framework that can help guide a new research agenda to enhance biodiversity conservation prospects in the human-modified tropics.

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Prospects for tropical forest biodiversity in a human-modified world.

Summary In many bird monitoring surveys, no attempt is made to estimate bird densities or abundance. Instead, counts of one form or another are made, and these are assumed to correlate with bird density. Unless complete counts on sample plots are feasible, this approach can easily lead to false conclusions, because detectability of birds varies by species, habitat, observer and many other factors. Trends in time of counts often reflect trends in detectability, rather than trends in abundance. Conclusions are further compromised when surveys are conducted at unrepresentative sites. We consider how to avoid these problems. We give a brief description of distance sampling methods, which allow detectability to be estimated. We consider strategies to ease their implementation, to enhance their reliability, to adapt the methods for difficult species, and to deal with circumstances in which representative sampling is problematic. We also consider some of the common problems encountered, and suggest solutions.

/pdf/estimating-bird-abundance-making-methods-work-3qkrmnzkof.pdf

Estimating bird abundance : making methods work

Bird surveys : expedition field techniques

Little of Brazil's remaining Atlantic forest is protected, so it is important to assess how well the region's wildlife can persist in areas/habitats outside reserves. We studied bird diversity and abundance during 546 point counts in the Sooretama/Linhares reserve, 200 point counts in 31 forest fragments (10- 150 h), and 50 point counts in <30-year-old Eucalyptus plantations, within 7 km of the reserve. Only eight bird species were recorded in Eucalyptus, and this impoverishment, as compared to some Eucalyptus plantations elsewhere in Brazil may be a result of intensive clearance of understory vegetation. Species diversity in forest fragments was significantly lower than in the reserve. Twelve, mostly non-forest or edge species, were significantly commoner in the fragments, but nineteen species were frequent in the reserve but rare or absent in forest fragments. These included two Pyrrhura parakeets, a Brotogeris parakeet, a trogon Trogon, a jacamar Galbula, woodpeckers Piculus and Campephilus, Myrmotherula antwrens, and Hemithraupus and Tachyphonus tanagers. Bird species richness at points in forest fragments did not decline with fragment size, distance from the reserve, or forest quality. However, forest in fragments was more heavily degraded than forest within the reserve and poor forest quality may be the cause of declines in some species. Whilst protection of forest within reserves is a priority, management of forest fragments may aid conservation of some threatened species.

Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil

Although small-scale agroforestry systems (swiddens, complex and single-crop-dominated agroforests, and homegardens) form a diverse and important tropical land use, there has been no attempt to collate information on their value for biodiversity. This paper reviews 52 published studies that compared species richness and/or abundance between agroforests and primary forest, and 27 studies that compared biodiversity parameters across agroforests. The former covered a broad range of taxa and geographical areas, but few focused on homegardens, while those comparing across agroforestry systems were biased towards studies of plants (21 studies) and homegardens (13 of 27). Of 43 studies comparing species richness or diversity across habitats, 34 reported lower richness in agroforests than in adjacent forest. There was also high β diversity between primary forests and agroforests. Patterns of abundance shifts were less straightforward, with many species traits (for example diets) being generally poor indicators of response to agricultural disturbance. Among the few trends identified, restricted-range or rare species, and terrestrial and some understorey vertebrates tended to decline most, and open country species, granivores and generalists increased most in agroforests. Variability in biodiversity retention across systems has been linked most strongly to economic function, management intensity and extent of remnant forest within the landscape, as well as more subtle cultural influences. Species richness and abundance generally decrease with increasing prevalence of crop species, more intensive management, decreasing stratum richness and shortening of cultivation cycles. Increasing holding size did not necessarily reduce α diversity. Knowledge of the general effects of small-scale agroforestry on biodiversity is substantial, but the great diversity of systems and species responses mean that it is difficult to accurately predict biodiversity losses and gains at a local level. Further work is required on the influence of spatial and temporal structure of agricultural holdings on biodiversity retention across agriculture/succession/forest mosaics, how β diversity across individual holdings influences biodiversity across landscapes, and ultimately on how agricultural intensification can be best managed to minimize future losses of biodiversity from tropical landscapes.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0376892908004840

Biodiversity in small-scale tropical agroforests : a review of species richness and abundance shifts and the factors influencing them

The suitability of point count distance methods for estimating densities of tropical parrots and hornbills was assessed during surveys in Indonesia. The methods will perform well, so long as the following are considered. (1) Enough bird records must be accumulated to model species' detection curves precisely. For some species, around 2000 point counts may be needed and, in very rare species, the method may not be appropriate. Pooling data across habitats, species or years may increase precision in cases of small sample size. (2) Point counts are likely to be less biased than line transects because bird detection rates close to the recorder may be higher and there may be less chance of double-counting birds. Distances greater than 400 m between census points are unnecessary. (3) Count periods lasting ten minutes may be needed to ensure that most birds close to the recorder are detected. Controlled flushing of concealed birds after the main count period may also be appropriate. (4) The best time of day for census is the period when bird detectability is high but bird mobility low. For many large avian frugivores, this corresponds to the period between one hour after dawn and 10.30 h. (5) Records of flying birds must be excluded from density calculations. In the species studied, between 2% and 20% should be added to density estimates to compensate for the exclusion of flying birds.

Stuart J. Marsden

Papers

Estimating bird abundance : making methods work

Bird surveys : expedition field techniques

Bird diversity and abundance in forest fragments and Eucalyptus plantations around an Atlantic forest reserve, Brazil

Biodiversity in small-scale tropical agroforests : a review of species richness and abundance shifts and the factors influencing them

Estimation of parrot and hornbill densities using a point count distance sampling method