Forecasting trait responses in novel environments to aid seed provenancing under climate change.

Revegetation projects face the major challenge of sourcing optimal plant material. This is often done with limited information about plant performance and increasingly requires factoring resilience to climate change. Functional traits can be used as quantitative indices of plant performance and guide seed provenancing, but trait values expected under novel conditions are often unknown. To support climate-resilient provenancing efforts, we develop a trait prediction model that integrates the effect of genetic variation with fine-scale temperature variation. We train our model on multiple field plantings of Arabidopsis thaliana and predict two relevant fitness traits-days-to-bolting and fecundity-across the species' European range. Prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation explained by temperature differences between plantings. Projection under future climate predicted a decline in fecundity, although this response was heterogeneous across the range. In response, we identified novel genotypes that could be introduced to genetically offset the fitness decay. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.

Multiple-region, N-mixture community model to assess associations of riparian area, fragmentation, and species richness.

The associations of habitat area and fragmentation with species richness long have been major topics within community ecology. Recent discussion has focused on properly assessing fragmentation independent of habitat area (fragmentation per se), and on whether fragmentation has significant negative or positive associations with species richness. We created a novel, multiple-region, N-mixture community model (MNCM) to examine the relations of riparian area and fragmentation with species richness of breeding birds in mountain ranges within the Great Basin, Nevada, USA. Our MNCM accounts for imperfect detection in count data at the survey-point level while allowing comparisons of species richness among regions in which those points are embedded. We used individual canyons within mountain ranges as regions in our model and measured riparian area and the Normalized Landscape Shape Index, a metric of fragmentation that is independent of total riparian area. We found that riparian area, but not its fragmentation, was a primary predictor of canyon-level species richness of both riparian obligates and all species. The relationship between riparian area and riparian obligate species richness was nonlinear: canyons with ≥25 ha woody riparian vegetation had relatively high species richness, whereas species richness was considerably lower in canyons with <25 ha. Our MNCM can be used to calculate other metrics of diversity that require abundance estimates. For example, Simpson's evenness of riparian obligate species had a weak negative association with riparian area and was not associated with fragmentation. Projections of future riparian contraction suggested that decreases in species richness are likely to be greatest in canyons that currently have moderate (~10-25 ha) amounts of riparian vegetation. Our results suggest that if a goal of management is to maximize the species richness of breeding birds in montane riparian areas in the Great Basin, it may be more effective to focus on total habitat area than on fragmentation of patches within canyons, and that canyons with at least moderate amounts of riparian vegetation should be prioritized.

Patterns of species richness, abundance and individual-size distributions in native stream-fish assemblages invaded by exotic and translocated fishes.

Predicting the impacts of species introductions long has attracted the attention of ecologists yet there still is limited insight into how impacts on native assemblages vary with the degree of shared evolutionary context. Here, we used data from 535 stream-fish surveys from 15 catchments in north-eastern Spain (99,700 km2) to explore whether the relative effects on native fishes differ between fish introductions from two different ecoregions (i.e., evolutionary contexts), namely, catchments within Iberian Peninsula (i.e., 'translocated species') and catchments beyond Iberian Peninsula (i.e., 'exotic fishes'). We used hierarchical Bayesian models to relate taxon richness, abundance, and the individual-size distributions (ISDs) of native fishes to the presence, abundance, and weighted trophic level (TL) of translocated and exotic fishes, conditional on geographic and habitat covariates. Environmental covariates dominated the percentage of explained variance (≥ 65%) for all responses. Translocated fishes accounted for more of the explained variance than did exotic fishes for ISDs and abundance, but not for native fish species richness. The presence of translocated fishes was associated with lower abundance and richness of native fishes, with individuals being smaller in the presence of translocated fishes of higher TL. The presence of exotic fishes was associated with a greater abundance and richness of native fishes, with individuals generally being larger in the presence of exotic fishes. Our study suggests that translocated fishes could be as problematic as exotic fishes when angling and water transfers among catchments to deal with climate change may increase the establishment of translocated fishes. We also discuss the difficulties of using fish body size as species-blind, transferable assemblage-level trait in fish monitoring.

Lifetime movement history is associated with variable growth of a potamodromous freshwater fish.

Directional or stabilising selection should drive the expression of a dominant movement phenotype within a population. Widespread persistence of multiple movement phenotypes within wild populations, however, suggests that individuals that move (movers) and those that do not (residents) can have commensurate performance. The costs and benefits of mover and resident phenotypes remain poorly understood. Here, we explored how the presence and timing of movements are correlated with annual somatic growth rates, a useful proxy for performance because it is easily measured and rapidly reflects environmental changes. We used otolith growth measurements and stable isotope analyses to recreate growth and among-reach movement histories of a partially migrating, long-lived freshwater fish, golden perch Macquaria ambigua. We compared the association between movement and growth at two temporal scales: (a) short-term (annual) differences in growth, in the years preceding, during or following movement; and (b) long-term (lifetime) differences in growth. Overall, 59% of individuals performed at least one among-reach movement, with these individuals subsequently more likely to move repeatedly throughout their lives. Movers grew faster than residents, with this difference most pronounced in the juvenile and early adult stages, when most movements occurred. Annual growth did not, however, change immediately prior to or following a specific movement event. Among-individual variation in growth was initially higher for residents than for movers but decreased with age, at a faster rate for residents than for movers, such that levels conformed after 5 years of age. Our results indicate that lifetime movement is linked to faster growth in the early years of a fish's life. These faster growing movers are likely to be larger at a given age, leading to numerous potential benefits. However, the persistence of resident phenotypes suggests that there is likely a cost-benefit trade-off to moving. The presence of multiple movement phenotypes may contribute to the resilience of populations by buffering against naturally and anthropogenically exacerbated environmental variability.

Using multiple sources during reintroduction of a locally extinct population benefits survival and reproduction of an endangered freshwater fish.

Through using different sources, population reintroductions can create genetically diverse populations at low risk of harmful inbreeding and well equipped for adaptation to future environments. Genetic variation from one source can mask locally nonoptimal alleles from another, thereby enhancing adaptive potential and population persistence. We assessed the outcomes in survival, growth and reproduction of using two differentiated sources (genetically diverse Yarra and moderately diverse Dartmouth) for translocations and stocking to reintroduce the endangered Australian freshwater Macquarie perch Macquaria australasica into the Ovens River. For stocking, same- and different-population parents ("cross-types") were used during hatchery production. Genetic samples and data on individual fish were collected over three years of monitoring the Ovens. We genetically assigned Ovens fish to their broodstock parents and tested whether cross-type and genetic dissimilarity between parents are associated with offspring survival, and whether cross-type and parental dissimilarity or individual genetic diversity are associated with somatic growth rates of stocked fish. We genetically identified translocated fish and assessed local recruit ancestry. Of 296 Ovens fish, 31.1% were inferred to be stocked, 1.3% translocated and 67.6% locally born. Cross-type strongly predicted survival of stocked offspring: those with two Yarra parents had the highest survival, followed by offspring with two-population, then Dartmouth, ancestry. Of the Ovens recruits, 59.5% had Yarra, 33.5% two-population and 7.0% Dartmouth ancestry, despite 67% of stocked and 98% of translocated fish originating from Dartmouth. Offspring with two Yarra parents grew faster than offspring of Dartmouth or two-population ancestry. Although Dartmouth fish appear to be less fit in the Ovens compared to Yarra fish, possibly due to deleterious variation or genetic or plastic maladaptation, they contribute to the reintroduced population through local interbreeding with Yarra fish and relatively high survival of stocked offspring of two-population ancestry. Thus, combining compatible stocks is likely to benefit restoration of other wildlife populations.

Elevated river discharge enhances the immigration of juvenile catadromous and amphidromous fishes into temperate coastal rivers.

Anthropogenic alterations to river flow regimes threaten freshwater biodiversity globally, with potentially disproportionate impacts on species that rely on flow cues to trigger critical life history processes, such as migration for diadromous fishes. This study investigates the influence of river discharge on the abundance of juvenile fish moving into rivers by four temperate catadromous or amphidromous species (common galaxias Galaxias maculatus, spotted galaxias Galaxias truttaceus, climbing galaxias Galaxias brevipinnis and the threatened Australian grayling Prototroctes maraena). Fyke netting or fishway trapping was used to catch juvenile fish moving from estuaries into freshwater in five coastal waterways in south-eastern Australia during the spring migratory period. There was a positive relationship between the probability of high catch rates and mean discharge in September. We also found a positive relationship between discharge and the number of recruits captured 22-30 days later in a flow stressed system. In addition, day-of-year had a strong influence on catch rates, with the peak abundance of juveniles for three species most likely to occur midway through the sampling period (spotted galaxias in October, climbing galaxias in late October and Australian grayling in late October and early November). Our study shows that higher magnitudes of river discharge were associated with increased catches of juvenile catadromous and amphidromous fishes. With a limited supply of environmental water, environmental flows used to enhance immigration of these fishes may be best targeted to maintain small amounts of immigration into freshwater populations in waterways or years when discharges are low and stable. When there are natural, large discharge volumes, relatively large numbers of juvenile fish can be expected to enter coastal waterways and during these times environmental flows may not be required to promote immigration.

Lifetime Fitness Costs of Inbreeding and Being Inbred in a Critically Endangered Bird.

Reduced fitness as a result of inbreeding is a major threat facing many species of conservation concern [1-4]. However, few case studies for assessing the magnitude of inbreeding depression in the wild means that its relative importance as a risk factor for population persistence remains under-appreciated [5]. The increasing availability and affordability of genomic technologies provide new opportunities to address knowledge gaps around the magnitude and manifestation of inbreeding depression in wild populations [6-12]. Here, we combine over three decades of individual lifetime reproductive data and genomic data to estimate the relative lifetime and short-term fitness costs of both being inbred and engaging in inbreeding in the last wild population (<250 individuals remaining) of an iconic and critically endangered bird: the helmeted honeyeater Lichenostomus melanops cassidix. The magnitude of inbreeding depression was substantial: the mean predicted lifetime reproductive success of the most inbred (homozygosity = 0.82) individuals was on average 87%-90% lower than that of the least inbred (homozygosity = 0.75). For individual reproductive events and lifetime measures, we provide rare empirical evidence that pairing with a genetically dissimilar individual can reduce fitness costs associated with being an inbred individual. By comparing lifetime and short-term fitness measures, we demonstrate how short-term measures of reproductive success that are associated with only weak signatures of inbreeding depression can still underlie stronger lifetime effects. Our study represents a valuable case study, highlighting the critical importance of inbreeding depression as a factor influencing the immediate viability of populations in threatened species management.

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.

Severe consequences of habitat fragmentation on genetic diversity of an endangered Australian freshwater fish: A call for assisted gene flow.

Genetic diversity underpins the ability of populations to persist and adapt to environmental changes. Substantial empirical data show that genetic diversity rapidly deteriorates in small and isolated populations due to genetic drift, leading to reduction in adaptive potential and fitness and increase in inbreeding. Assisted gene flow (e.g. via translocations) can reverse these trends, but lack of data on fitness loss and fear of impairing population "uniqueness" often prevents managers from acting. Here, we use population genetic and riverscape genetic analyses and simulations to explore the consequences of extensive habitat loss and fragmentation on population genetic diversity and future population trajectories of an endangered Australian freshwater fish, Macquarie perch Macquaria australasica. Using guidelines to assess the risk of outbreeding depression under admixture, we develop recommendations for population management, identify populations requiring genetic rescue and/or genetic restoration and potential donor sources. We found that most remaining populations of Macquarie perch have low genetic diversity, and effective population sizes below the threshold required to retain adaptive potential. Our simulations showed that under management inaction, smaller populations of Macquarie perch will face inbreeding depression within a few decades, but regular small-scale translocations will rapidly rescue populations from inbreeding depression and increase adaptive potential through genetic restoration. Despite the lack of data on fitness loss, based on our genetic data for Macquarie perch populations, simulations and empirical results from other systems, we recommend regular and frequent translocations among remnant populations within catchments. These translocations will emulate the effect of historical gene flow and improve population persistence through decrease in demographic and genetic stochasticity. Increasing population genetic connectivity within each catchment will help to maintain large effective population sizes and maximize species adaptive potential. The approach proposed here could be readily applicable to genetic management of other threatened species to improve their adaptive potential.

Integrated models to support multiobjective ecological restoration decisions.

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state-and-transition, species-distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade-offs in management outcomes through integrated modeling and structured decision-support approaches has wide application for situations in which trade-offs exist between competing conservation objectives.

Linking structure and function in food webs: maximization of different ecological functions generates distinct food web structures.

Trophic interactions are central to ecosystem functioning, but the link between food web structure and ecosystem functioning remains obscure. Regularities (i.e. consistent patterns) in food web structure suggest the possibility of regularities in ecosystem functioning, which might be used to relate structure to function. We introduce a novel, genetic algorithm approach to simulate food webs with maximized throughput (a proxy for ecosystem functioning) and compare the structure of these simulated food webs to real empirical food webs using common metrics of food web structure. We repeat this analysis using robustness to secondary extinctions (a proxy for ecosystem resilience) instead of throughput to determine the relative contributions of ecosystem functioning and ecosystem resilience to food web structure. Simulated food webs that maximized robustness were similar to real food webs when connectance (i.e. levels of interaction across the food web) was high, but this result did not extend to food webs with low connectance. Simulated food webs that maximized throughput or a combination of throughput and robustness were not similar to any real food webs. Simulated maximum-throughput food webs differed markedly from maximum-robustness food webs, which suggests that maximizing different ecological functions can generate distinct food web structures. Based on our results, food web structure would appear to have a stronger relationship with ecosystem resilience than with ecosystem throughput. Our genetic algorithm approach is general and is well suited to large, realistically complex food webs. Genetic algorithms can incorporate constraints on structure and can generate outputs that can be compared directly to empirical data. Our method can be used to explore a range of maximization or minimization hypotheses, providing new perspectives on the links between structure and function in ecological systems.

Thermodynamics predicts density-dependent energy use in organisms and ecological communities.

Linking our knowledge of organisms to our knowledge of ecological communities and ecosystems is a key challenge for ecology. Individual size distributions (ISDs) link the size of individual organisms to the structure of ecological communities, so that studying ISDs might provide insight into how organism functioning affects ecosystems. Similarly shaped ISDs among ecosystems, coupled with allometric links between organism size and resource use, suggest the possibility of emergent resource-use patterns in ecological communities. We drew on thermodynamics to develop a maximization principle that predicted both organism and community energy use. These predictions highlighted the importance of density-dependent metabolic rates and were able to explain nonlinear relationships between community energy use and community biomass. We analyzed data on fish community energy use and biomass and found evidence of nonlinear scaling, which was predicted by the thermodynamic principle developed here and is not explained by other theories of ISDs. Detailed measurements of organism energy use will clarify the role of density dependence in driving metabolic rates and will further test our derived thermodynamic principle. Importantly, our study highlights the potential for fundamental links between ecology and thermodynamics.

Is there an ecological basis for species abundance distributions?

Community ecologists have attempted to explain species abundance distribution (SAD) shape for more than 80 years, but usually without relating SAD shape explicitly to ecological variables. We explored whether the scale (total assemblage abundance) and shape (assemblage evenness) of avifaunal SADs were related to ecological covariates. We used data on avifaunas, in-site habitat structure and landscape context that were assembled from previous studies; this amounted to 197 transects distributed across 16,000 km(2) of the box-ironbark forests of southeastern Australia. We used Bayesian conditional autoregressive models to link SAD scale and shape to these ecological covariates. Variation in SAD scale was relatable to some ecological covariates, especially to landscape vegetation cover and to tree height. We could not find any relationships between SAD shape and ecological covariates. SAD shape, the core component in SAD theory, may hold little information about how assemblages are governed ecologically and may result from statistical processes, which, if general, would indicate that SAD shape is not useful for distinguishing among theories of assemblage structure.

Estimating global arthropod species richness: refining probabilistic models using probability bounds analysis.

A key challenge in the estimation of tropical arthropod species richness is the appropriate management of the large uncertainties associated with any model. Such uncertainties had largely been ignored until recently, when we attempted to account for uncertainty associated with model variables, using Monte Carlo analysis. This model is restricted by various assumptions. Here, we use a technique known as probability bounds analysis to assess the influence of assumptions about (1) distributional form and (2) dependencies between variables, and to construct probability bounds around the original model prediction distribution. The original Monte Carlo model yielded a median estimate of 6.1 million species, with a 90 % confidence interval of [3.6, 11.4]. Here we found that the probability bounds (p-bounds) surrounding this cumulative distribution were very broad, owing to uncertainties in distributional form and dependencies between variables. Replacing the implicit assumption of pure statistical independence between variables in the model with no dependency assumptions resulted in lower and upper p-bounds at 0.5 cumulative probability (i.e., at the median estimate) of 2.9-12.7 million. From here, replacing probability distributions with probability boxes, which represent classes of distributions, led to even wider bounds (2.4-20.0 million at 0.5 cumulative probability). Even the 100th percentile of the uppermost bound produced (i.e., the absolutely most conservative scenario) did not encompass the well-known hyper-estimate of 30 million species of tropical arthropods. This supports the lower estimates made by several authors over the last two decades.

Putting the "ecology" into environmental flows: ecological dynamics and demographic modelling.

There have been significant diversions of water from rivers and streams around the world; natural flow regimes have been perturbed by dams, barriers and excessive extractions. Many aspects of the ecological 'health' of riverine systems have declined due to changes in water flows, which has stimulated the development of thinking about the maintenance and restoration of these systems, which we refer to as environmental flow methodologies (EFMs). Most existing EFMs cannot deliver information on the population viability of species because they: (1) use habitat suitability as a proxy for population status; (2) use historical time series (usually of short duration) to forecast future conditions and flow sequences; (3) cannot, or do not, handle extreme flow events associated with climate variability; and (4) assume process stationarity for flow sequences, which means the past sequences are treated as good indicators of the future. These assumptions undermine the capacity of EFMs to properly represent risks associated with different flow management options; assumption (4) is untenable given most climate-change predictions. We discuss these concerns and advocate the use of demographic modelling as a more appropriate tool for linking population dynamics to flow regime change. A 'meta-species' approach to demographic modelling is discussed as a useful step from habitat based models towards modelling strategies grounded in ecological theory when limited data are available on flow-demographic relationships. Data requirements of demographic models will undoubtedly expose gaps in existing knowledge, but, in so doing, will strengthen future efforts to link changes in river flows with their ecological consequences.

Quantifying uncertainty in estimation of tropical arthropod species richness.

There is a bewildering range of estimates for the number of arthropods on Earth. Several measures are based on extrapolation from species specialized to tropical rain forest, each using specific assumptions and justifications. These approaches have not provided any sound measure of uncertainty associated with richness estimates. We present two models that account for parameter uncertainty by replacing point estimates with probability distributions. The models predict medians of 3.7 million and 2.5 million tropical arthropod species globally, with 90% confidence intervals of [2.0, 7.4] million and [1.1, 5.4] million, respectively. Estimates of 30 million or greater are predicted to have <0.00001 probability. Sensitivity analyses identified uncertainty in the proportion of canopy arthropod species that are beetles as the most influential parameter, although uncertainties associated with three other parameters were also important. Using the median estimates suggests that in spite of 250 years of taxonomy and around 855,000 species of arthropods already described, approximately 70% await description.

Cocoa Pod Borer (Conopomorpha cramerella Snellen) in Papua New Guinea: biosecurity models for New Ireland and the autonomous region of Bougainville.

Cocoa Pod Borer (Conopomorpha cramerella Snellen) (CPB) is an important pest of cocoa. Following its emergence as a pest in East New Britain, Papua New Guinea, in 2006, it was considered relevant to assess its potential spread to other cocoa growing regions. Its likelihood of introduction to the islands of Bougainville and New Ireland from East New Britain Province, Papua New Guinea, was modeled using Monte Carlo simulation. This dispersal model was based around different scenarios, identifying trends rather than explicitly attempting to encapsulate true values. The model suggested that CPB is far more likely to establish on New Ireland than on Bougainville. More important, incertitude resulting from incomplete knowledge of the amount and frequency of cocoa transported between islands had a significant effect on model outputs. Quarantine and agriculture officials will be able to refine these parameter values, and then use the relevant scenarios from those presented here as a guide to develop quarantine procedures. In addition, a contingency model was employed to estimate the optimal sampling effort to use following an incursion of CPB into Bougainville or New Ireland and the seemingly successful implementation of an initial eradication program. The model suggests that at a 1% infestation level, sampling should continue for 2.5-2.7 years (90% CI) after claiming eradication, and this estimate changed little for higher infestation levels. Through modeling variations in sampling intensity, the model also suggested that determining the full spread of CPB is more important than increased sampling within one region.