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.

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.

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.

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.

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.

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.

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.

Climatic warming strengthens a positive feedback between alpine shrubs and fire.

Climate change is expected to increase fire activity and woody plant encroachment in arctic and alpine landscapes. However, the extent to which these increases interact to affect the structure, function and composition of alpine ecosystems is largely unknown. Here we use field surveys and experimental manipulations to examine how warming and fire affect recruitment, seedling growth and seedling survival in four dominant Australian alpine shrubs. We found that fire increased establishment of shrub seedlings by as much as 33-fold. Experimental warming also doubled growth rates of tall shrub seedlings and could potentially increase their survival. By contrast, warming had no effect on shrub recruitment, postfire tussock regeneration, or how tussock grass affected shrub seedling growth and survival. These findings indicate that warming, coupled with more frequent or severe fires, will likely result in an increase in the cover and abundance of evergreen shrubs. Given that shrubs are one of the most flammable components in alpine and tundra environments, warming is likely to strengthen an existing feedback between woody species abundance and fire in these ecosystems.

Translocation strategies for multiple species depend on interspecific interaction type.

Conservation translocations, anthropogenic movements of species to prevent their extinction, have increased substantially over the last few decades. Although multiple species are frequently moved to the same location, current translocation guidelines consider species in isolation. This practice ignores important interspecific interactions and thereby risks translocation failure. We model three different two-species systems to illustrate the inherent complexity of multispecies translocations and to assess the influence of different interaction types (consumer-resource, mutualism, and competition) on translocation strategies. We focus on how these different interaction types influence the optimal founder population sizes for successful translocations and the order in which the species are moved (simultaneous or sequential). Further, we assess the effect of interaction strength in simultaneous translocations and the time delay between translocations when moving two species sequentially. Our results show that translocation decisions need to reflect the type of interaction. While all translocations of interacting species require a minimum founder population size, which is demarked by an extinction boundary, consumer-resource translocations also have a maximum founder population limit. Above the minimum founder size, increasing the number of translocated individuals leads to a substantial increase in the extinction boundary of competitors and consumers, but not of mutualists. Competitive and consumer-resource systems benefit from sequential translocations, but the order of translocations does not change the outcomes for mutualistic interaction partners noticeably. Interspecific interactions are important processes that shape population dynamics and should therefore be incorporated into the quantitative planning of multispecies translocations. Our findings apply whenever interacting species are moved, for example, in reintroductions, conservation introductions, biological control, or ecosystem restoration.

The Roles of Ecological and Evolutionary Processes in Plant Community Assembly: The Environment, Hybridization, and Introgression Influence Co-occurrence of Eucalyptus.

Introgressive hybridization is increasingly recognized as having influenced the gene pools of large genera of plants, yet it is rarely invoked as an explanation for why closely related plant species do not co-occur. Here, we asked how the environment and tendency to interbreed relate to neighborhood co-occurrence patterns for Eucalyptus species in the Grampians National Park, Victoria, Australia. We identified species pairs that have experienced ongoing hybridization and introgression on the basis of the extent of incongruence between chloroplast DNA (JLA+ region) and nuclear ribosomal DNA (internal transcribed spacer region) phylogenies, geographic patterns of gene sharing, and field observation of intermediate morphologies. Co-occurrence, trait data (specific leaf area [SLA], maximum height, and seed mass), and environmental data were measured in plots distributed along environmental gradients. Trait and habitat similarity influenced species co-occurrence the most overall (e.g., co-occurring species had similar SLA). Reproductively compatible species were an exception; they rarely co-occurred despite being functionally similar. The negative effect of reproductive compatibility was stronger than the positive effect of SLA on co-occurrence. Our results emphasize the dominant roles of the environment and the importance of evolution in structuring local assemblages. We argue that the mechanism responsible for preventing closely related species from co-occurring in this system is reproductive interference rather than competitive exclusion. Reproductive interference should be considered more generally as a potential cause of phylogenetic overdispersion.

Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change.

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (Eoccupied ), the instability of suitable area (Einstability ) and the overlap between the current and future spatial distribution (Eoverlap ). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree (BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence.

Modeling rates of life form cover change in burned and unburned alpine heathland subject to experimental warming.

Elevated global temperatures are expected to alter vegetation dynamics by interacting with physiological processes, biotic relationships and disturbance regimes. However, few studies have explicitly modeled the effects of these interactions on rates of vegetation change, despite such information being critical to forecasting temporal patterns in vegetation dynamics. In this study, we build and parameterize rate-change models for three dominant alpine life forms using data from a 7-year warming experiment. These models allowed us to examine how the interactions between experimental warming, the abundance of bare ground (a measure of past disturbance) and neighboring life forms (a measure of life form interaction) affect rates of cover change in alpine shrubs, graminoids and forbs. We show that experimental warming altered rates of life form cover change by reducing the negative effects of neighboring life forms and positive effects of bare ground. Furthermore, we show that our models can predict the observed direction and rate of life form cover change at burned and unburned long-term monitoring sites. Model simulations revealed that warming in unburned vegetation is expected to result in increased forb and shrub cover and decreased graminoid cover. In contrast, in burned vegetation, warming is predicted to slow post-fire regeneration in both graminoids and forbs and facilitate rapid expansion in shrub cover. These findings illustrate the applicability of modeling rates of vegetation change using experimental data. Our results also highlight the need to account for both disturbance and the abundance of other life forms when examining and forecasting vegetation dynamics under climatic change.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.

Correlations between physical and chemical defences in plants: tradeoffs, syndromes, or just many different ways to skin a herbivorous cat?

Most plant species have a range of traits that deter herbivores. However, understanding of how different defences are related to one another is surprisingly weak. Many authors argue that defence traits trade off against one another, while others argue that they form coordinated defence syndromes. We collected a dataset of unprecedented taxonomic and geographic scope (261 species spanning 80 families, from 75 sites across the globe) to investigate relationships among four chemical and six physical defences. Five of the 45 pairwise correlations between defence traits were significant and three of these were tradeoffs. The relationship between species' overall chemical and physical defence levels was marginally nonsignificant (P = 0.08), and remained nonsignificant after accounting for phylogeny, growth form and abundance. Neither categorical principal component analysis (PCA) nor hierarchical cluster analysis supported the idea that species displayed defence syndromes. Our results do not support arguments for tradeoffs or for coordinated defence syndromes. Rather, plants display a range of combinations of defence traits. We suggest this lack of consistent defence syndromes may be adaptive, resulting from selective pressure to deploy a different combination of defences to coexisting species.

Examining change over time in habitat attributes using Bayesian reinterpretation of categorical assessments.

Prospects for evaluating effects of vegetation restoration have long been limited by availability of appropriately sensitive baseline data. Data that are typically collected to justify investment in restoration are rarely suitable for estimating subsequent change over time, but given how commonly such data are collected, can they contribute something to learning about ecological change over time? We compared vegetation and habitat data from a quantitative reassessment of 25 habitat restoration sites seven years after they were initially assessed using a semiquantitative, categorical scoring system. Our aim was to estimate the change at sites between the first, semiquantitative survey and a second, quantitative survey. We treated the initial values as effectively unknown and used Bayesian models to infer plausible values using three different informative prior distributions, variously comprising the initial site assessments and modeled values from a statewide data set. We successfully constructed models of change over time between the two surveys, and regardless of which prior model was implemented, our data analysis suggested that cover of exotic species was reduced, but canopy cover, the cover of organic litter, and the length of fallen logs were all increased after the seven-year period. A small increase in the mean number of large-diameter trees was likely due to initial measurement error. Site fertility and canopy cover were important covariates in explaining the magnitude of change in total log length. Sites with higher canopy cover decreased more in weed cover and increased more in litter cover. Our approach could be used to retrospectively analyze any ordinal data set where there is a scoring logic that can be interpreted quantitatively. Data sets where treatment contrasts and untreated controls exist will be particularly valuable for testing the utility of our approach. While this novel approach should prove a useful analytical complement to genuine longitudinal monitoring and space-for-time surveys, it is no substitute for initiation of learning about management effectiveness using data from purposefully designed and measured surveys.

Differences in forest plant functional trait distributions across land-use and productivity gradients.

PREMISE OF STUDY: Plant functional traits are commonly used as proxies for plant responses to environmental challenges, yet few studies have explored how functional trait distributions differ across gradients of land-use change. By comparing trait distributions in intact forests with those across land-use change gradients, we can improve our understanding of the ways land-use change alters the diversity and functioning of plant communities. METHODS: We examined how the variation and distribution of trait values for seven plant functional traits differ between reference natural forest and three types of land-use conversion (pasture, old-field, or "legacy" sites-regrowth following logging), landscape productivity (NPP) and vegetation strata (tree or non-tree "understory"), in a meta-analysis of studies from 15 landscapes across five continents. KEY RESULTS: Although trait variation often differed between land-uses within a landscape, these patterns were rarely consistent across landscapes. The variance and distribution of traits were more likely to differ consistently between natural forest and land-use conversion categories for understory (non-tree) plants than for trees. Landscape productivity did not significantly alter the difference in trait variance between natural forest and land-use conversion categories for any trait except dispersal. CONCLUSIONS: Our results suggest that even for traits well linked to plant environmental response strategies, broad classes of land-use change and landscape productivity are not generally useful indicators of the mechanisms driving compositional changes in human-modified forest systems.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change.

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature. 2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable. 3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation. 4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation. 5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon. 6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.