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The role of conspecific density dependence (CDD) in the maintenance of species richness is a central focus of tropical forest ecology. However, tests of CDD often ignore the integrated effects of CDD over multiple life stages and their long-term impacts on population demography. We combined a 10-year time series of seed production, seedling recruitment and sapling and tree demography of three dominant Southeast Asian tree species that adopt a mast-fruiting phenology. We used these data to construct individual-based models that examine the effects of CDD on population growth rates ( λ ) across life-history stages. Recruitment was driven by positive CDD for all species, supporting the predator satiation hypothesis, while negative CDD affected seedling and sapling growth of two species, significantly reducing λ . This negative CDD on juvenile growth overshadowed the positive CDD of recruitment, suggesting the cumulative effects of CDD during seedling and sapling development has greater importance than the positive CDD during infrequent masting events. Overall, CDD varied among positive, neutral and negative effects across life-history stages for all species, suggesting that assessments of CDD on transitions between just two stages (e.g. seeds seedlings or juveniles mature trees) probably misrepresent the importance of CDD on population growth and stability.

Tropical forests are threatened by degradation and deforestation but the consequences for these ecosystems are poorly understood, particularly at the landscape scale. We present the most extensive ecosystem analysis to date of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 79 variables categorized into four hierarchical ecological ‘levels’: 1) structure and environment, 2) species traits, 3) biodiversity and 4) ecosystem functions. Variables at the lowest levels that were directly impacted by the physical processes of timber extraction, such as soil characteristics, were sensitive to even moderate amounts of logging, whereas biodiversity and ecosystem functions proved remarkably resilient to logging in many cases, but were more affected by conversion to oil palm plantation.One-Sentence SummaryLogging tropical forest mostly impacts structure while biodiversity and functions are more vulnerable to habitat conversion

Current policy is driving renewed impetus to restore forests to return ecological function, protect species, sequester carbon and secure livelihoods. Here we assess the contribution of tree planting to ecosystem restoration in tropical and sub-tropical Asia; we synthesize evidence on mortality and growth of planted trees at 176 sites and assess structural and biodiversity recovery of co-located actively restored and naturally regenerating forest plots. Mean mortality of planted trees was 18% 1 year after planting, increasing to 44% after 5 years. Mortality varied strongly by site and was typically ca 20% higher in open areas than degraded forest, with height at planting positively affecting survival. Size-standardized growth rates were negatively related to species-level wood density in degraded forest and plantations enrichment settings. Based on community-level data from 11 landscapes, active restoration resulted in faster accumulation of tree basal area and structural properties were closer to old-growth reference sites, relative to natural regeneration, but tree species richness did not differ. High variability in outcomes across sites indicates that planting for restoration is potentially rewarding but risky and context-dependent. Restoration projects must prepare for and manage commonly occurring challenges and align with efforts to protect and reconnect remaining forest areas. The abstract of this article is available in Bahasa Indonesia in the electronic supplementary material. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'., Philosophical Transactions of the Royal Society B: Biological Sciences, 378 (1867), ISSN:0962-8436, ISSN:1471-2970, ISSN:0080-4622

Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity, but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used a long-term, field-scale tropical restoration experiment to test how the diversity of planted trees affected recovery measured across a 500 ha area of selectively logged forest using multiple sources of satellite data. Replanting with species rich mixtures of tree seedlings that had higher phylogenetic and functional diversity accelerated restoration rates. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance initial recovery after logging.

Natural forest is declining globally as the area of planted forest increases. Planted forests are often monocultures, despite results suggesting that higher species richness improves ecosystem functioning and stability. To test if this is generally the case, we performed a meta-analysis of available results. We assessed aboveground carbon stocks in mixed-species planted forests vs (a) the average of constituent species monocultures, (b) the best constituent species monoculture, and (c) commercial species monocultures. We investigated whether any advantage of mixtures over monocultures was positively related to species richness, as well as potential mechanisms driving differences in carbon stocks between mixtures and monocultures. The meta-analysis dataset included 79 comparisons from 21 sites. Carbon stocks in mixed planted forests were higher than the average of stocks in monocultures of their constituent species, containing on average 70% more carbon. Mixed planted forests also out-performed commercial monocultures, containing on average 77% more carbon. There was c.25% more carbon in mixed planted forests relative to the best performing monocultures, although this difference was not statistically significant. Overyielding was highest in four-species mixtures (richness range 2-6 species). More data providing better coverage of richness and age gradients (study sites aged 3.5-28 years) is needed to increase confidence in these results. None of the potential mechanisms we examined (nitrogen-fixer present vs absent; native vs non-native/mixed origin; tree diversity experiment vs forestry plantation) consistently explained variation in the diversity effects. This suggests that our findings are driven by a combination of small (statistically insignificant) effects from these sources or further unidentified mechanisms or some combination of the two. We conclude that increasing tree species richness in planted forests can increase carbon stocks while bringing other potential benefits associated with diversification. However, implementation will depend on the balance of these benefits relative to the operational challenges and costs of diversification.

Given the worldwide plans for extensive tree planting, we urgently need to understand how and where implementation will contribute to goals such as those for carbon sequestration. We used a long-term, large-scale native reforestation project in the Scottish Highlands to assess the response of carbon storage and other ecosystem functions to reforestation and grazing exclusion. We measured aboveground carbon, topsoil carbon, topsoil nitrogen, decomposition rates, soil invertebrate feeding activity, tree regeneration, and ground-layer and moss height at 14 sites that are in the early stages of reforestation and fenced to exclude grazing. Reforestation areas were compared to unforested and mature forest areas that are both grazed and ungrazed, using 10 × 10 m plots. Aboveground carbon in the reforestation plots (1.4 kg/m 2 [95% CI: 0.6, 2.6], average age 20 years since reforestation) was c. 8% of the mature forest plots (17.1 kg/m 2 [13.1, 21.8]). Topsoil carbon was lower in the reforestation plots (18.78 kg/m 2 [11.79, 25.78]) than in the unforested (29.82 kg/m 2 [24.34, 35.29]) or mature forest (31.39 kg/m 2 [22.91, 39.88]) plots. Responses of other functions to the reforestation and grazing interventions varied. Our results suggest that reforestation may trigger carbon loss from areas with high initial soil carbon even with low disturbance establishment, at least in the short term. Our work emphasizes where we lack knowledge: on the potential for long-term re-accumulation of soil carbon under semi-natural native reforestation, soil carbon sequestration in the deeper soil layers, and the response of soil carbon to natural regeneration.

Diversifying planted forests by increasing genetic and species diversity is often promoted as a method to improve forest resilience to climate change and reduce pest and pathogen damage. In this study, we used a young tree diversity experiment replicated at two sites in the UK to study the impacts of tree diversity and tree provenance (geographic origin) on the oak (Quercus robur) insect herbivore community and a specialist biotrophic pathogen, oak powdery mildew. Local UK, French, and Italian provenances were planted in monocultures, provenance mixtures, and species mixes, allowing us to test whether: (a) local and nonlocal provenances differ in their insect herbivore and pathogen communities, and (b) admixing trees leads to associational effects on insect herbivore and pathogen damage. Tree diversity had variable impacts on foliar organisms across sites and years, suggesting that diversity effects can be highly dependent on environmental context. Provenance identity impacted upon both herbivores and powdery mildew, but we did not find consistent support for the local adaptation hypothesis for any group of organisms studied. Independent of provenance, we found tree vigor traits (shoot length, tree height) and tree apparency (the height of focal trees relative to their surroundings) were consistent positive predictors of powdery mildew and insect herbivory. Synthesis. Our results have implications for understanding the complex interplay between tree identity and diversity in determining pest damage, and show that tree traits, partially influenced by tree genotype, can be important drivers of tree pest and pathogen loads.

GLMs using the Poisson distribution are a good starting place when dealing with integer count data. The default log link function prevents the prediction of negative counts and the Poisson distribution models the variance (approximately equal to the mean).

One of the key features of scientific research is that results should be reproducible, and the same goes for the statistical analyses that the results are based on. Unfortunately, over the last decade or so we have discovered that a worrying amount of our work is not reproducible (or replicable—these terms are sometimes used for different aspects of the problem but are also often used interchangeably). The causes of this reproducibility crisis are complex and act at many points of the research process, but improving how we go about our statistical analysis will certainly help.

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear-model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of overuse of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics, which is rapidly becoming the lingua franca in many areas of science. This second edition adds new chapters, including one discussing some of the complexities of linear-model analysis and another introducing reproducible research documents using the R Markdown package. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

This chapter sets out the aims of the book, the approach, what is covered in the book, and what is not. The chapter introduces the R and RStudio software packages. It also summarizes the changes made to the second edition of the book. The book starts by introducing several different variations of the basic linear-model analysis (analysis of variance, linear regression, analysis of covariance, etc.). The later part of the book covers generalized linear models (for data with non-normal distributions). The book ends by combining these two extensions into generalized linear mixed-effects models. To allow a learning-by-doing approach, the R code necessary to perform the basic analysis is embedded in the text along with the key output from R.

This book began with the simplest types of linear model: one-way ANOVA and its simple linear regression equivalent. However, once more complex ANOVA and ANCOVA designs were encountered some complexities arose that were then skipped over. This chapter explores these complexities of linear-model analysis and some additional ones that arise with unbalanced designs—those with unequal numbers of replicates in the different treatment groups.

ANCOVA of designed experiments combines one categorical and one continuous explanatory variable. Panel plots are usually the best way to graphically display ANCOVA designs, with a separate linear regression within each level of the factor. ANCOVA can test for effects of both variables and interactions between them. The chapter focuses on ANCOVA of designed experiments. A detailed analysis is given of a subset of the variables from an experimental study of the effects of low-level atmospheric pollutants and drought on agricultural yields.

The last chapter conducted a simple analysis of Darwin’s maize data using R as an oversized pocket calculator to work out confidence intervals ‘by hand’. This is a simple way to learn about analysis and good for demystifying the process, but it is inefficient. Instead, we want to take advantage of the more sophisticated functions that R provides that are designed to perform linear-model analysis. This chapter explores those functions by repeating and extending the analysis of Darwin’s maize data.

This chapter begins with a linear-model analysis where the aim is to compare the effects of different treatments. Each treatment is applied to a group of experimental units and, if the treatments are effective, they will produce differences between the groups. Experiments are designed to test hypotheses about the effects of the treatments and to quantify the strength of these effects. This chapter focuses on describing the treatment effects (a step we often skimp on in our haste to get to the punchline), saving testing for later.

Analysis of variance is introduced using a worked analysis of some data collected by Charles Darwin on inbreeding depression in experimental maize plants. ANOVA is used to compare the heights of a group of cross-pollinated plants with a group of self-fertilized seedlings. The least squares process is explained, including the calculation of sums of squares and variances and the calculation of F-ratios. The organization of the results of a linear model into an ANOVA table is explained. The R code necessary to perform the analysis is worked through and explained in terms of the underlying statistical concepts, and the interpretation of the R output is demonstrated.

Binomial GLMs can also be used to analyse binary data as a special case, with some minor differences introduced into the analysis by the constrained nature of the binary data.

Statistics is all about signal and noise. The analysis of Darwin’s maize data presented earlier focused initially on description. This chapter focuses on estimating the differences in height and testing whether we think there is an effect of the pollination treatments. The goal is to estimate the mean heights for the two treatments in Darwin’s experiment and the difference between them, and to calculate various measures that quantify our confidence (and, on the flip side, uncertainty) in the estimates, which we can use to judge whether they appear to be different or not.

This chapter moves on from simple ‘one-way’ designs to more complex factorial designs. It extends the simple linear model to include interactions as well as average main effects. Interactions are assessed relative to a null additive expectation where the treatments have no effect on each other. Interactions can be positive, when effects are more than additive, or negative, when they are less than expected. The chapter considers in detail the analysis of an example data set concerning the mechanisms of loss of plant diversity following fertilizer treatment.

This chapter extends the use of linear models to relationships with continuous explanatory variables, in other words, linear regression. The goal of the worked example (on timber hardness data) given in detail in this chapter is prediction, not hypothesis testing. Confidence intervals and prediction intervals are explained. Graphical approaches to checking the assumptions of linear-model analysis are explored in further detail. The effects of transformations on linearity, normality, and equality of variance are investigated.

In this chapter we use linear regression to model the relationship between wood density and timber hardness. Our first goal is to estimate the coefficients of the linear model—the regression intercept and slope. The aim of this chapter is to put those coefficients to work in predicting timber hardness from wood density.

This chapter reiterates some of the most important points made in the book. It comes full circle by revisiting the introductory motivating example and applying the methods learned in the previous chapter on binary GLMs. A list gives some parting words of advice, including some suggestions of where to go next for more advice and information. An annotated list of suggestions for further reading is provided. The details of an online support website and the materials that will be provided there are given.

GLMs with a binomial distribution are designed for the analysis of binomial counts (how many times something occurred relative to the total number of possible times it could have occurred). A logistic link function constrains predictions to be above zero and below the maximum using the S-shaped logistic curve. Overdispersion can be diagnosed and dealt with using a quasi-maximum likelihood extension to GLM analysis.

This chapter introduces a motivating example (which is returned to at the end of the book)—analysis of data from the Space Shuttle Challenger disaster. The chapter illustrates the importance of statistics, including informal graphical analysis.

Arbuscular mycorrhizal (AM) and ectomycorrhizal (EcM) associations are critical for host-tree performance. However, how mycorrhizal associations correlate with the latitudinal tree beta-diversity remains untested. Using a global dataset of 45 forest plots representing 2,804,270 trees across 3840 species, we test how AM and EcM trees contribute to total beta-diversity and its components (turnover and nestedness) of all trees. We find AM rather than EcM trees predominantly contribute to decreasing total beta-diversity and turnover and increasing nestedness with increasing latitude, probably because wide distributions of EcM trees do not generate strong compositional differences among localities. Environmental variables, especially temperature and precipitation, are strongly correlated with beta-diversity patterns for both AM trees and all trees rather than EcM trees. Results support our hypotheses that latitudinal beta-diversity patterns and environmental effects on these patterns are highly dependent on mycorrhizal types. Our findings highlight the importance of AM-dominated forests for conserving global forest biodiversity., The relationship of mycorrhizal associations with latitudinal gradients in tree beta-diversity is unexplored. Using a global dataset approach, this study examines how trees with arbuscular mycorrhizal and ectomycorrhizal associations contribute to latitudinal beta-diversity patterns and the environmental controls of these patterns.

Globally, there is increasing interest in tree planting, leading to many country-level commitments to reforestation. In the UK, current commitments would achieve 17% forest cover by 2050, with the highest rates of forest expansion expected in Scotland. Forest expansion with native trees is expected to increase biodiversity, particularly woodland specialist species, and associated ecosystem services. Despite this, data on biodiversity changes over the early stages of reforestation are sparse, particularly for upland areas in Scotland where opportunities for forest expansion are greatest. We collected data on the response of plants, carabid beetles and birds to native reforestation and grazing exclusion, using sites reforested over the last 30 years in the Scottish Highlands. Biodiversity in ungrazed, reforested sites was compared to unforested controls and mature native forest, both grazed and ungrazed. Mean bird species richness in reforested plots (4.4 [95% CI: 3.2, 5.9]) was higher than in unforested plots (0.8 [0.5, 1.3]), but lower than in mature forest plots (7.0 [5.4, 8.3]). In contrast, there was no systematic difference in plant or carabid beetle species richness in reforested, unforested or mature forest plots, or between grazed and ungrazed plots for the species richness of any groups. Woodland specialist bird and plant species were found in the reforested plots, and richness of woodland specialist bird species was predicted to reach levels in mature forest c. 36 years after reforestation. Species assemblages differed across habitat categories. For birds and plants, species assemblages in reforested sites were intermediate to unforested and mature sites. For carabid beetles, the assemblages in mature and reforested sites were comparable and differed from unforested sites. Grazing did not strongly influence species assemblages. Policy implications. We show that woodland specialists colonise reforested sites and species assemblages transition towards those found in the target habitat within the first 30 years of reforestation with native species. Native forest should be prioritised in Scotland's future forest expansion targets, given that mature native forest is scarce and fragmented in the Scottish Highlands and that the ultimate gain from native forest expansion may accrue over long time-scales.

International audience; Our planet is facing significant changes of biodiversity across spatial scales. Although the negative effects of local biodiversity (α diversity) loss on ecosystem stability are well documented, the consequences of biodiversity changes at larger spatial scales, in particular biotic homogenization, that is, reduced species turnover across space (β diversity), remain poorly known. Using data from 39 grassland biodiversity experiments, we examine the effects of β diversity on the stability of simulated landscapes while controlling for potentially confounding biotic and abiotic factors. Our results show that higher β diversity generates more asynchronous dynamics among local communities and thereby contributes to the stability of ecosystem productivity at larger spatial scales. We further quantify the relative contributions of α and β diversity to ecosystem stability and find a relatively stronger effect of α diversity, possibly due to the limited spatial scale of our experiments. The stabilizing effects of both α and β diversity lead to a positive diversity-stability relationship at the landscape scale. Our findings demonstrate the destabilizing effect of biotic homogenization and suggest that biodiversity should be conserved at multiple spatial scales to maintain the stability of ecosystem functions and services.

Eutrophication is a widespread environmental change that usually reduces the stabilizing effect of plant diversity on productivity in local communities. Whether this effect is scale dependent remains to be elucidated. Here, we determine the relationship between plant diversity and temporal stability of productivity for 243 plant communities from 42 grasslands across the globe and quantify the effect of chronic fertilization on these relationships. Unfertilized local communities with more plant species exhibit greater asynchronous dynamics among species in response to natural environmental fluctuations, resulting in greater local stability (alpha stability). Moreover, neighborhood communities that have greater spatial variation in plant species composition within sites (higher beta diversity) have greater spatial asynchrony of productivity among communities, resulting in greater stability at the larger scale (gamma stability). Importantly, fertilization consistently weakens the contribution of plant diversity to both of these stabilizing mechanisms, thus diminishing the positive effect of biodiversity on stability at differing spatial scales. Our findings suggest that preserving grassland functional stability requires conservation of plant diversity within and among ecological communities., Eutrophication has been shown to weaken diversity-stability relationships in grasslands, but it is unclear whether the effect depends on scale. Analysing a globally distributed network of grassland sites, the authors show a positive role of beta diversity and spatial asynchrony as drivers of stability but find that nitrogen enrichment weakens the diversity-stability relationships at different spatial scales.