The AusTraits plant dictionary.

Traits with intuitive names, a clear scope and explicit description are essential for all trait databases. The lack of unified, comprehensive, and machine-readable plant trait definitions limits the utility of trait databases, including reanalysis of data from a single database, or analyses that integrate data across multiple databases. Both can only occur if researchers are confident the trait concepts are consistent within and across sources. Here we describe the AusTraits Plant Dictionary (APD), a new data source of terms that extends the trait definitions included in a recent trait database, AusTraits. The development process of the APD included three steps: review and formalisation of the scope of each trait and the accompanying trait description; addition of trait metadata; and publication in both human and machine-readable forms. Trait definitions include keywords, references, and links to related trait concepts in other databases, enabling integration of AusTraits with other sources. The APD will both improve the usability of AusTraits and foster the integration of trait data across global and regional plant trait databases.

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

What state of the world are we in? Targeted monitoring to detect transitions in vegetation restoration projects.

Monitoring vegetation restoration is challenging because monitoring is costly, requires long-term funding, and involves monitoring multiple vegetation variables that are often not linked back to learning about progress toward objectives. There is a clear need for the development of targeted monitoring programs that focus on a reduced set of variables that are tied to specific restoration objectives. In this paper, we present a method to progress the development of a targeted monitoring program, using a pre-existing state-and-transition model. We (1) use field data to validate an expert-derived classification of woodland vegetation states; (2) use these data to identify which variable(s) help differentiate woodland states; and (3) identify the target threshold (for the variable) that signifies if the desired transition has been achieved. The measured vegetation variables from each site in this study were good predictors of the different states. We show that by measuring only a few of these variables, it is possible to assign the vegetation state for a collection of sites, and monitor if and when a transition to another state has occurred. For this ecosystem and state-and-transition models, out of nine vegetation variables considered, the density of immature trees and percentage of exotic understory vegetation cover were the variables most frequently specified as effective to define a threshold or transition. We synthesize findings by presenting a decision tree that provides practical guidance for the development of targeted monitoring strategies for woodland vegetation.

Building trait datasets: effect of methodological choice on a study of invasion.

Trait-based approaches are commonly used to understand ecological phenomena and processes. Trait data are typically gathered by measuring local specimens, retrieving published records, or a combination of the two. Implications of methodological choices in trait-based ecological studies-including source of data, imputation technique, and species selection criteria-are poorly understood. We ask: do different approaches for dataset-building lead to meaningful differences in trait datasets? If so, do these differences influence findings of a trait-based examination of plant invasiveness, measured as abundance and spread rate? We collected on-site (Victoria, Australia) and off-site (TRY database) height and specific leaf area records for as many species as possible out of 157 exotic herbaceous plants. For each trait, we built six datasets of species-level means using records collected on-site, off-site, on-site and off-site combined, and off-site supplemented via imputation based on phylogeny and/or trait correlations. For both traits, the six datasets were weakly correlated (ρ = 0.31-0.95 for height; ρ = 0.14-0.88 for SLA), reflecting differences in species' trait values from the various estimations. Inconsistencies in species' trait means across datasets did not translate into large differences in trait-invasion relationships. Although we did not find that methodological choices for building trait datasets greatly affected ecological inference about local invasion processes, we nevertheless recommend: (1) using on-site records to answer local-scale ecological questions whenever possible, and (2) transparency around methodological decisions related to selection of study species and estimation of missing trait values.

Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies.

Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach.

Exploring how functional traits modulate species distributions along topographic gradients in Baxian Mountain, North China.

The associations between functional traits and species distributions across environments have attracted increasing interest from ecologists and can enhance knowledge about how plants respond to the environments. Here, we applied a hierarchical generalized linear model to quantifying the role of functional traits in plant occurrence across topographic gradients. Functional trait data, including specific leaf area, maximum height, seed mass and stem wood density, together with elevation, aspect and slope, were used in the model. In our results, species responses to elevation and aspect were modulated by maximum height and seed mass. Generally, shorter tree species showed positive responses to incremental elevation, while this trend became negative as the maximum height exceeded 22 m. Most trees with heavy seeds (> 1 mg) preferred more southerly aspects where the soil was drier, and those light-seed trees were opposite. In this study, the roles of maximum height and seed mass in determining species distribution along elevation and aspect gradients were highlighted where plants are confronted with low-temperature and soil moisture deficit conditions. This work contributes to the understanding of how traits may be associated with species occurrence along mesoscale environmental gradients.

Phenotypic plasticity masks range-wide genetic differentiation for vegetative but not reproductive traits in a short-lived plant.

Genetic differentiation and phenotypic plasticity jointly shape intraspecific trait variation, but their roles differ among traits. In short-lived plants, reproductive traits may be more genetically determined due to their impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with observational field data throughout the range of a widespread short-lived herb, Plantago lanceolata, we (1) disentangled genetic and plastic responses of functional traits to a set of environmental drivers and (2) assessed how genetic differentiation and plasticity shape observational trait-environment relationships. Reproductive traits showed distinct genetic differentiation that largely determined observational patterns, but only when correcting traits for differences in biomass. Vegetative traits showed higher plasticity and opposite genetic and plastic responses, masking the genetic component underlying field-observed trait variation. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related to fitness.

Predicting species and community responses to global change using structured expert judgement: An Australian mountain ecosystems case study.

Conservation managers are under increasing pressure to make decisions about the allocation of finite resources to protect biodiversity under a changing climate. However, the impacts of climate and global change drivers on species are outpacing our capacity to collect the empirical data necessary to inform these decisions. This is particularly the case in the Australian Alps which have already undergone recent changes in climate and experienced more frequent large-scale bushfires. In lieu of empirical data, we use a structured expert elicitation method (the IDEA protocol) to estimate the change in abundance and distribution of nine vegetation groups and 89 Australian alpine and subalpine species by the year 2050. Experts predicted that most alpine vegetation communities would decline in extent by 2050; only woodlands and heathlands are predicted to increase in extent. Predicted species-level responses for alpine plants and animals were highly variable and uncertain. In general, alpine plants spanned the range of possible responses, with some expected to increase, decrease or not change in cover. By contrast, almost all animal species are predicted to decline or not change in abundance or elevation range; more species with water-centric life-cycles are expected to decline in abundance than other species. While long-term ecological data will always be the gold standard for informing the future of biodiversity, the method and outcomes outlined here provide a pragmatic and coherent basis upon which to start informing conservation policy and management in the face of rapid change and a paucity of data.

Traits explain invasion of alien plants into tropical rainforests.

1. The establishment of new botanic gardens in tropical regions highlights a need for weed risk assessment tools suitable for tropical ecosystems. The relevance of plant traits for invasion into tropical rainforests has not been well studied.2. Working in and around four botanic gardens in Indonesia where 590 alien species have been planted, we estimated the effect of four plant traits, plus time since species introduction, on: (a) the naturalization probability and (b) abundance (density) of naturalized species in adjacent native tropical rainforests; and (c) the distance that naturalized alien plants have spread from the botanic gardens.3. We found that specific leaf area (SLA) strongly differentiated 23 naturalized from 78 non-naturalized alien species (randomly selected from 577 non-naturalized species) in our study. These trends may indicate that aliens with high SLA, which had a higher probability of naturalization, benefit from at least two factors when establishing in tropical forests: high growth rates and occupation of forest gaps. Naturalized aliens had high SLA and tended to be short. However, plant height was not significantly related to species' naturalization probability when considered alongside other traits.4. Alien species that were present in the gardens for over 30 years and those with small seeds also had higher probabilities of becoming naturalized, indicating that garden plants can invade the understorey of closed canopy tropical rainforests, especially when invading species are shade tolerant and have sufficient time to establish.5. On average, alien species that were not animal dispersed spread 78 m further into the forests and were more likely to naturalize than animal-dispersed species. We did not detect relationships between the measured traits and estimated density of naturalized aliens in the adjacent forests.6. Synthesis: Traits were able to differentiate alien species from botanic gardens that naturalized in native forest from those that did not; this is promising for developing trait-based risk assessment in the tropics. To limit the risk of invasion and spread into adjacent native forests, we suggest tropical botanic gardens avoid planting alien species with fast carbon capture strategies and those that are shade tolerant.

Plant functional traits reflect different dimensions of species invasiveness.

Trait-based invasiveness studies typically categorize exotic species as invasive or noninvasive, implicitly assuming species form two homogenous groups. However, species can become invasive in different ways (e.g., high abundance, fast spread), likely relying on different functional traits to do so. As such, binary classification may obscure traits associated with invasiveness. We tested whether (1) the way in which invasiveness is quantified influences its correlation with functional traits and (2) different demography-based metrics are related to different sets of traits. Using a case study of 251 herbs exotic to Victoria, Australia, we quantified species' invasiveness using 10 metrics: four continuous, demography-based dimensions of invasiveness (spread rate, local abundance, geographic and environmental range sizes) and six binary classifications of invasiveness (based on alternative sources and invasion criteria). We examined the correlation between species' invasiveness and a set of four traits known to relate to plant demography and invasion. Then, we examined whether different demographic dimensions of invasiveness were better explained by different sets of traits. We found that the way invasiveness was quantified was important: different traits were linked with different invasiveness metrics, and some traits showed opposite effects across metrics. Species with fast spread were either tall with small seeds (i.e., good colonizers), or had heavy, animal-dispersed seeds. Plants with a large environmental range had greater plasticity for some traits. Locally abundant plants had low SLA and heavy seeds (i.e., strong competitors). Animal dispersal was also key to reach a large geographic range. No traits were consistently related to the six binary classifications. Our results indicate that exotic plants are invasive in different ways and rely on different combinations of traits to be so. Some traits (e.g., seed mass) had complex relationships with invasion: they apparently promote, hampered, or had no influence on different dimensions of invasiveness. Our findings are consistent with the notion that plant species use strategies that may be near optimal under some, but not all, ecological conditions. Compared to binary classifications of invasiveness, the use of invasiveness dimensions advances clearer hypothesis testing in invasion science.

Indigenous plants promote insect biodiversity in urban greenspaces.

The contribution of urban greenspaces to support biodiversity and provide benefits for people is increasingly recognized. However, ongoing management practices favor vegetation oversimplification, often limiting greenspaces to lawns and tree canopy rather than multi-layered vegetation that includes under- and midstorey, and the use of nonnative species. These practices hinder the potential of greenspaces to sustain indigenous biodiversity, particularly for taxa like insects that rely on plants for food and habitat. Yet, little is known about which plant species may maximize positive outcomes for taxonomically and functionally diverse insect communities in greenspaces. Additionally, while cities are expected to experience high rates of introductions, quantitative assessments of the relative occupancy of indigenous vs. introduced insect species in greenspace are rare, hindering understanding of how management may promote indigenous biodiversity while limiting the establishment of introduced insects. Using a hierarchically replicated study design across 15 public parks, we recorded occurrence data from 552 insect species on 133 plant species, differing in planting design element (lawn, midstorey, and tree canopy), midstorey growth form (forbs, lilioids, graminoids, and shrubs) and origin (nonnative, native, and indigenous), to assess (1) the relative contributions of indigenous and introduced insect species and (2) which plant species sustained the highest number of indigenous insects. We found that the insect community was overwhelmingly composed of indigenous rather than introduced species. Our findings further highlight the core role of multi-layered vegetation in sustaining high insect biodiversity in urban areas, with indigenous midstorey and canopy representing key elements to maintain rich and functionally diverse indigenous insect communities. Intriguingly, graminoids supported the highest indigenous insect richness across all studied growth forms by plant origin groups. Our work highlights the opportunity presented by indigenous understory and midstorey plants, particularly indigenous graminoids, in our study area to promote indigenous insect biodiversity in urban greenspaces. Our study provides a blueprint and stimulus for architects, engineers, developers, designers, and planners to incorporate into their practice plant species palettes that foster a larger presence of indigenous over regionally native or nonnative plant species, while incorporating a broader mixture of midstorey growth forms.

Misinformation, internet honey trading and beekeepers drive a plant invasion.

Biological invasions are a major human induced global change that is threatening global biodiversity by homogenizing the world's fauna and flora. Species spread because humans have moved species across geographical boundaries and have changed ecological factors that structure ecosystems, such as nitrogen deposition, disturbance, etc. Many biological invasions are caused accidentally, as a byproduct of human travel and commerce driven product shipping. However, humans also have spread many species intentionally because of perceived benefits. Of interest is the role of the recent exponential growth in information exchange via internet social media in driving biological invasions. To date, this has not been examined. Here, we show that for one such invasive species, goldenrod, social networks spread misleading and incomplete information that is enhancing the spread of goldenrod invasions into new environments. We show that the notion of goldenrod honey as a "superfood" with unsupported healing properties is driving a demand that leads beekeepers to produce goldenrod honey. Social networks provide a forum for such information exchange and this is leading to further spread of goldenrod in many countries where goldenrod is not native, such as Poland. However, this informal social information exchange ignores laws that focus on preventing the further spread of invasive species and the strong negative effects that goldenrod has on native ecosystems, including floral resources that negatively impact honeybee performance. Thus, scientifically unsupported information on "superfoods" such as goldenrod honey that is disseminated through social internet networks has real world consequences such as increased goldenrod invasions into novel geographical regions which decreases native biodiversity.

Modeling biodiversity benchmarks in variable environments.

Effective environmental assessment and management requires quantifiable biodiversity targets. Biodiversity benchmarks define these targets by focusing on specific biodiversity metrics, such as species richness. However, setting fixed targets can be challenging because many biodiversity metrics are highly variable, both spatially and temporally. We present a multivariate, hierarchical Bayesian method to estimate biodiversity benchmarks based on the species richness and cover of native terrestrial vegetation growth forms. This approach uses existing data to quantify the empirical distributions of species richness and cover within growth forms, and we use the upper quantiles of these distributions to estimate contemporary, "best-on-offer" biodiversity benchmarks. Importantly, we allow benchmarks to differ among vegetation types, regions, and seasons, and with changes in recent rainfall. We apply our method to data collected over 30 yr at ~35,000 floristic plots in southeastern Australia. Our estimated benchmarks were broadly consistent with existing expert-elicited benchmarks, available for a small subset of vegetation types. However, in comparison with expert-elicited benchmarks, our data-driven approach is transparent, repeatable, and updatable; accommodates important spatial and temporal variation; aligns modeled benchmarks directly with field data and the concept of best-on-offer benchmarks; and, where many benchmarks are required, is likely to be more efficient. Our approach is general and could be used broadly to estimate biodiversity targets from existing data in highly variable environments, which is especially relevant given rapid changes in global environmental conditions.

Forecasting species range dynamics with process-explicit models: matching methods to applications.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

Using functional traits to predict species growth trajectories, and cross-validation to evaluate these models for ecological prediction.

Modeling plant growth using functional traits is important for understanding the mechanisms that underpin growth and for predicting new situations. We use three data sets on plant height over time and two validation methods-in-sample model fit and leave-one-species-out cross-validation-to evaluate non-linear growth model predictive performance based on functional traits. In-sample measures of model fit differed substantially from out-of-sample model predictive performance; the best fitting models were rarely the best predictive models. Careful selection of predictor variables reduced the bias in parameter estimates, and there was no single best model across our three data sets. Testing and comparing multiple model forms is important. We developed an R package with a formula interface for straightforward fitting and validation of hierarchical, non-linear growth models. Our intent is to encourage thorough testing of multiple growth model forms and an increased emphasis on assessing model fit relative to a model's purpose.

A field ecologist's adventures in the virtual world: using simulations to design data collection for complex models.

Field data collection can be expensive, time consuming, and difficult; insightful research requires statistical analyses supported by sufficient data. Pilot studies and power analysis provide guidance on sampling design but can be challenging to perform, as ecologists increasingly collect multiple types of data over different scales. Despite a growing simulation literature, it remains unclear how to appropriately design data collection for many complex projects. Approaches that seek to achieve realism in decision-making contexts, such as management strategy evaluation and virtual ecologist simulations, can help. For a relatively complex analysis, we develop and demonstrate a flexible simulation approach that informs what data are needed and how long those data will take to collect, under realistic fieldwork constraints. We simulated data collection and analysis under different constraint scenarios that varied in deterministic (field trip length, travel, and measurement times) and stochastic (species detection and occupancy rates and inclement weather) features. In our case study, we fit plant height data to a multispecies, three-parameter, nonlinear growth model. We tested how the simulated data sets, based on the varying constraint scenarios, affected the model fit (parameter bias, uncertainty, and capture rate). Species prevalence in the field exerted a stronger influence on the data sets and downstream model performance than deterministic aspects such as travel times. When species detection and occupancy were not considered, the field time needed to collect an adequate data set was underestimated by 40%. Simulations can assist in refining fieldwork design, estimating field costs, and incorporating uncertainties into project planning. We argue that combining data collection, analysis, and decision-making processes in a flexible virtual setting can help address many of the decisions that field ecologists face when designing field-based research.

Facultative and Obligate Trees in a Mesic Savanna: Fire Effects on Savanna Structure Imply Contrasting Strategies of Eco-Taxonomic Groups.

Fire is a major determinant of savanna tree communities and, as such, manipulation of fire frequency is an important management tool. Resolving the effects of fire management on tree size class distributions can help managers predict and plan for short-term ecological and economic outcomes, reveal different strategies by which woody plants cope with frequent fire, and help us predict vegetation changes under future fire scenarios. Savanna structure and size class distribution are strongly influenced by the ability of suppressed tree resprouts to escape stem death by frequent fire. A widespread assumption is that resprouts have an imperative to escape fire to reach sexual maturity in the canopy and thereby ensure long-term species viability. We use a census of Australian mesic savanna tree communities subjected to annual, triennial, and fire exclusion (unburnt) fire treatments to ask how fire frequency affects size class distributions within and between eco-taxonomic groups of species. Total tree densities did not significantly differ, but were highest in the triennial (7,610 ± se 1,162 trees ha-1) and unburnt fire treatments (7,051 ± se 578 trees ha-1) and lowest in the annual fire treatment (6,168 ± se 523 trees ha-1). This was caused by increased sapling densities in the triennial and unburnt fire treatments, predominantly of Acacia and pantropical genera. Eucalypts (Eucalyptus and Corymbia spp.) dominated the canopy across all fire treatments indicating relatively greater success in recruiting to larger sizes than other species groups. However, in the sub-canopy size classes eucalypts co-dominated with, and in some size classes were outnumbered by, pantropicals and Acacia, regardless of fire treatment. We hypothesize that such results are caused by fundamental differences in woody plant strategies, in particular sexual reproduction, that have not been widely recognized in Australian savannas.

Useful surrogates of soil texture for plant ecologists from airborne gamma-ray detection.

Plant ecologists require spatial information on functional soil properties but are often faced with soil classifications that are not directly interpretable or useful for statistical models. Sand and clay content are important soil properties because they indicate soil water-holding capacity and nutrient content, yet these data are not available for much of the landscape. Remotely sensed soil radiometric data offer promise for developing statistical models of functional soil properties applicable over large areas. Here, we build models linking radiometric data for an area of 40,000 km2 with soil physicochemical data collected over a period of 30 years and demonstrate a strong relationship between gamma radiometric potassium (40K), thorium (²³²Th), and soil sand and clay content. Our models showed predictive performance of 43% with internal cross-validation (to held-out data) and ~30% for external validation to an independent test dataset. This work contributes to broader availability and uptake of remote sensing products for explaining patterns in plant distribution and performance across landscapes.

Identifying species at coextinction risk when detection is imperfect: Model evaluation and case study.

Losing a species from a community can cause further extinctions, a process also known as coextinction. The risk of being extirpated with an interaction partner is commonly inferred from a species' host-breadth, derived from observing interactions between species. But observational data suffers from imperfect detection, making coextinction estimates highly unreliable. To address this issue and to account for data uncertainty, we fit a hierarchical N-mixture model to individual-level interaction data from a mutualistic network. We predict (1) with how many interaction partners each species interacts (to indicate their coextinction risk) and (2) how completely the community was sampled. We fit the model to simulated interactions to investigate how variation in sampling effort, interaction probability, and animal abundances influence model accuracy and apply it to an empirical dataset of flowering plants and their insect visitors. The model performed well in predicting the number of interaction partners for scenarios with high abundances, but indicated high parameter uncertainty for networks with many rare species. Yet, model predictions were generally closer to the true value than the observations. Our mutualistic plant-insect community most closely resembled the scenario of high interaction rates with low abundances. Median estimates of interaction partners were frequently much higher than the empirical data indicate, but uncertainty was high. Our analysis suggested that we only detected 14-59% of the flower-visiting insect species, indicating that our study design, which is common for pollinator studies, was inadequate to detect many species. Imperfect detection strongly affects the inferences from observed interaction networks and hence, host specificity, specialisation estimates and network metrics from observational data may be highly misleading for assessing a species' coextinction risks. Our study shows how models can help to estimate coextinction risk, but also indicates the need for better data (i.e., intensified sampling and individual-level observations) to reduce uncertainty.