Environmental and plant community drivers of plant pathogen composition and richness.

Interactions between individual plant pathogens and their environment have been described many times. However, the relative contribution of different environmental parameters as controls of pathogen communities remains largely unknown. Here we investigate the importance of environmental factors, including geomorphology, climate, land use, soil and plant community composition, for a broad range of aboveground and belowground fungal, oomycete and bacterial plant pathogens. We found that plant community composition is the main driver of the composition and richness of plant pathogens after taking into account all other tested parameters, especially those related to climate and soil. In the face of future changes in climate and land use, our results suggest that changes in plant pathogen community composition and richness will primarily be mediated through changes in plant communities, rather than the direct effects of climate or soils.

Land use, but not distance, drives fungal beta diversity.

Fungi are one of the most diverse taxonomic groups on the planet, but much of their diversity and community organization remains unknown, especially at local scales. Indeed, a consensus on how fungal communities change across spatial or temporal gradients-beta diversity-remains nascent. Here, we use a data set of plant-associated fungal communities (leaf, root, and soil) across multiple land uses from a New Zealand-wide study to look at fungal community turnover at small spatial scales (<1 km). Using hierarchical Bayesian beta regressions and Hill-number-based diversity profiles, we show that fungal communities are often markedly dissimilar at even small distances, regardless of land use. Moreover, diversity profile plots indicate that leaf, root, and soil-associated communities show different patterns in the dominance or rarity of dissimilar species. Leaf-associated communities differed from site to site in their low-abundance species, whereas root-associated communities differed between sites in the dominant species; soil-associated communities were intermediate. Land-use differences were largely driven by the lower turnover between high-productivity grassland sites. Further, we discuss the implications and benefits of using diversity profile plots of turnover to draw inferences into the mechanisms of how communities are structured across spatial gradients.

Rarity is a more reliable indicator of land-use impacts on soil invertebrate communities than other diversity metrics.

The effects of land use on soil invertebrates - an important ecosystem component - are poorly understood. We investigated land-use impacts on a comprehensive range of soil invertebrates across New Zealand, measured using DNA metabarcoding and six biodiversity metrics. Rarity and phylogenetic rarity - direct measures of the number of species or the portion of a phylogeny unique to a site - showed stronger, more consistent responses across taxa to land use than widely used metrics of species richness, effective species numbers, and phylogenetic diversity. Overall, phylogenetic rarity explained the highest proportion of land use-related variance. Rarity declined from natural forest to planted forest, grassland, and perennial cropland for most soil invertebrate taxa, demonstrating pervasive impacts of agricultural land use on soil invertebrate communities. Commonly used diversity metrics may underestimate the impacts of land use on soil invertebrates, whereas rarity provides clearer and more consistent evidence of these impacts.

Living within the Earth's soil are millions of insects, worms and other invertebrates, which help keep the ground healthy and fertile. There is a growing concern that changing land-use habits, such as agriculture and urban development, are causing these populations of invertebrates to decline. However, to what extent different types of land use negatively impact soil invertebrates is not clear. Healthy habitats often have a greater variety of species. This biodiversity can be measured in a number of ways, ranging from counting the number of species, to more complex approaches that calculate a species' role in an ecosystem or how close it is to extinction. Finding a way to sensitively measure the biodiversity of soil invertebrates could further researcher's understanding of how different types of land use are affecting these communities. A new method known as DNA metabarcoding has made it easier to distinguish between different species and calculate the biodiversity of entire populations. Now, Dopheide et al. have used this technique to study invertebrate communities from 75 sites across New Zealand which have been impacted by different land-use habits. This revealed that the most reliable and consistent way to uncover how land use affects soil invertebrates was to measure the rarity of species (i.e. the number of unique species present at each site). Dopheide et al. found that agriculture negatively affected soil invertebrates and that most types of invertebrates responded in a similar way. Horticulture ­ such as orchards and vineyards ­ had the most severe impact, with the lowest variety of species compared to grassland or forest. Other measurements of biodiversity, such as the number of different species, may underestimate the negative impact agriculture is having on invertebrate communities. The findings of Dopheide et al. highlight why developing strategies to preserve and restore these communities is so important. However, more work is needed to understand what specifically is causing biodiversity to decline and how this effect can be reversed.

Land use is a determinant of plant pathogen alpha- but not beta-diversity.

Little is known about the diversity patterns of plant pathogens and how they change with land use at a broad scale. We employed DNA metabarcoding to describe the diversity and composition of putative plant pathogen communities in three substrates (soil, roots, and leaves) across five major land uses at a national scale. Almost all plant pathogen communities (fungi, oomycetes, and bacteria) showed strong responses to land use and substrate type. Land use category could explain up to 24% of the variance in composition between communities. Alpha-diversity (richness) of plant pathogens was consistently lower in natural forests than in agricultural systems. In planted forests, there was also generally low pathogen alpha-diversity in soil and roots, but alpha-diversity in leaves was high compared with most other land uses. In contrast to alpha-diversity, differences in within-land use beta-diversity of plant pathogens (the predictability of plant pathogen communities within land use) were subtle. Our results show that large-scale patterns and distributions of putative plant pathogens can be determined using metabarcoding, allowing some of the first landscape level insights into these critically important communities.

Biases in the metabarcoding of plant pathogens using rust fungi as a model system.

Plant pathogens such as rust fungi (Pucciniales) are of global economic and ecological importance. This means there is a critical need to reliably and cost-effectively detect, identify, and monitor these fungi at large scales. We investigated and analyzed the causes of differences between next-generation sequencing (NGS) metabarcoding approaches and traditional DNA cloning in the detection and quantification of recognized species of rust fungi from environmental samples. We found significant differences between observed and expected numbers of shared rust fungal operational taxonomic units (OTUs) among different methods. However, there was no significant difference in relative abundance of OTUs that all methods were capable of detecting. Differences among the methods were mainly driven by the method's ability to detect specific OTUs, likely caused by mismatches with the NGS metabarcoding primers to some Puccinia species. Furthermore, detection ability did not seem to be influenced by differences in sequence lengths among methods, the most appropriate bioinformatic pipeline used for each method, or the ability to detect rare species. Our findings are important to future metabarcoding studies, because they highlight the main sources of difference among methods, and rule out several mechanisms that could drive these differences. Furthermore, strong congruity among three fundamentally different and independent methods demonstrates the promising potential of NGS metabarcoding for tracking important taxa such as rust fungi from within larger NGS metabarcoding communities. Our results support the use of NGS metabarcoding for the large-scale detection and quantification of rust fungi, but not for confirming the absence of species.

Towards robust and repeatable sampling methods in eDNA-based studies.

DNA-based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three-year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA-based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes.

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

Functional and environmental determinants of bark thickness in fire-free temperate rain forest communities.

PREMISE OF THE STUDY: In fire-prone ecosystems, variation in bark thickness among species and communities has been explained by fire frequency; thick bark is necessary to protect cambium from lethal temperatures. Elsewhere this investment is deemed unnecessary, and thin bark is thought to prevail. However, in rain forest ecosystems where fire is rare, bark thickness varies widely among species and communities, and the causes of this variation remain enigmatic. We tested for functional explanations of bark thickness variation in temperate rain forest species and communities. METHODS: We measured bark thickness in 82 tree species throughout New Zealand temperate rain forests that historically have experienced little fire and applied two complementary analyses. First, we examined correlations between bark traits and leaf habit, and leaf and stem traits. Second, we calculated community-weighted mean (CWM) bark thickness for 272 plots distributed throughout New Zealand to identify the environments in which thicker-barked communities occur. KEY RESULTS: Conifers had higher size-independent bark thickness than evergreen angiosperms. Species with thicker bark or higher bark allocation coefficients were not associated with "slow economic" plant traits. Across 272 forest plots, communities with thicker bark occurred on infertile soils, and communities with thicker bark and higher bark allocation coefficients occurred in cooler, drier climates. CONCLUSIONS: In non-fire-prone temperate rain forest ecosystems, investment in bark is driven by soil resources, cool minimum temperatures, and seasonal moisture stress. The role of these factors in fire-prone ecosystems warrants testing.

Functional traits reveal processes driving natural afforestation at large spatial scales.

An understanding of the processes governing natural afforestation over large spatial scales is vital for enhancing forest carbon sequestration. Models of tree species occurrence probability in non-forest vegetation could potentially identify the primary variables determining natural afforestation. However, inferring processes governing afforestation using tree species occurrence is potentially problematic, since it is impossible to know whether observed occurrences are due to recruitment or persistence of existing trees following disturbance. Plant functional traits have the potential to reveal the processes by which key environmental and land cover variables influence afforestation. We used 10,061 survey plots to identify the primary environmental and land cover variables influencing tree occurrence probability in non-forest vegetation in New Zealand. We also examined how these variables influenced diversity of functional traits linked to plant ecological strategy and dispersal ability. Mean annual temperature was the most important environmental predictor of tree occurrence. Local woody cover and distance to forest were the most important land cover variables. Relationships between these variables and ecological strategy traits revealed a trade-off between ability to compete for light and colonize sites that were marginal for tree occurrence. Biotically dispersed species occurred less frequently with declining temperature and local woody cover, suggesting that abiotic stress limited their establishment and that biotic dispersal did not increase ability to colonize non-woody vegetation. Functional diversity for ecological strategy traits declined with declining temperature and woody cover and increasing distance to forest. Functional diversity for dispersal traits showed the opposite trend. This suggests that low temperatures and woody cover and high distance to forest may limit tree species establishment through filtering on ecological strategy traits, but not on dispersal traits. This study shows that 'snapshot' survey plot data, combined with functional trait data, may reveal the processes driving tree species establishment in non-forest vegetation over large spatial scales.

Scientific foundations for an IUCN Red List of ecosystems.

An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world's ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity.

Status assessment of New Zealand's naturally uncommon ecosystems.

Globally, ecosystems are under increasing anthropogenic pressure; thus, many are at risk of elimination. This situation has led the International Union for Conservation of Nature (IUCN) to propose a quantitative approach to ecosystem-risk assessment. However, there is a need for their proposed criteria to be evaluated through practical examples spanning a diverse range of ecosystems and scales. We applied the IUCN's ecosystem red-list criteria, which are based on changes in extent of ecosystems and reductions in ecosystem processes, to New Zealand's 72 naturally uncommon ecosystems. We aimed to test the applicability of the proposed criteria to ecosystems that are naturally uncommon (i.e., those that would naturally occur over a small area in the absence of human activity) and to provide information on the probability of ecosystem elimination so that conservation priorities might be set. We also tested the hypothesis that naturally uncommon ecosystems classified as threatened on the basis of IUCN Red List criteria contain more threatened plant species than those classified as nonthreatened. We identified 18 critically endangered, 17 endangered, and 10 vulnerable ecosystems. We estimated that naturally uncommon ecosystems contained 145 (85%) of mainland New Zealand's taxonomically distinct nationally critical, nationally endangered, and nationally vulnerable plant species, 66 (46%) of which were endemic to naturally uncommon ecosystems. We estimated there was a greater number of threatened plant species (per unit area) in critically endangered ecosystems than in ecosystems classified as nonthreatened. With their high levels of endemism and rapid and relatively well-documented history of anthropogenic change, New Zealand's naturally uncommon ecosystems provide an excellent case-study for the ongoing development of international criteria for threatened ecosystems. We suggest that interactions and synergies among decline in area, decline in function, and the scale of application of the criteria be used to improve the IUCN criteria for threatened ecosystems.

Trends in entropy production during ecosystem development in the Amazon Basin.

Understanding successional trends in energy and matter exchange across the ecosystem-atmosphere boundary layer is an essential focus in ecological research; however, a general theory describing the observed pattern remains elusive. This paper examines whether the principle of maximum entropy production could provide the solution. A general framework is developed for calculating entropy production using data from terrestrial eddy covariance and micrometeorological studies. We apply this framework to data from eight tropical forest and pasture flux sites in the Amazon Basin and show that forest sites had consistently higher entropy production rates than pasture sites (0.461 versus 0.422 W m(-2) K(-1), respectively). It is suggested that during development, changes in canopy structure minimize surface albedo, and development of deeper root systems optimizes access to soil water and thus potential transpiration, resulting in lower surface temperatures and increased entropy production. We discuss our results in the context of a theoretical model of entropy production versus ecosystem developmental stage. We conclude that, although further work is required, entropy production could potentially provide a much-needed theoretical basis for understanding the effects of deforestation and land-use change on the land-surface energy balance.