Understanding where and when pathogens occur in the environment has implications for reservoir population health and infection risk. In reservoir hosts, infection status and pathogen shedding are affected by processes interacting across different scales: from landscape features affecting host location and transmission to within‐host processes affecting host immunity and infectiousness. While uncommonly done, simultaneously incorporating processes across multiple scales may improve pathogen shedding predictions. In Australia, the black flying fox Pteropus alecto is a natural host for the zoonotic Hendra virus, which is hypothesized to cause latent infections in bats. Re‐activation and virus shedding may be triggered by poor host condition, leading to virus excretion through urine. Here, we developed a statistical modeling approach that combined data at multiple spatial and temporal scales to capture ecological and biological processes potentially affecting virus shedding. We parameterized these models using existing datasets and compared model performance to under‐roost virus shedding data from 2011–2014 in 23 roosts across a 1200‐km transect. Our approach enabled comparisons among multiple model structures to determine which variables at which scales are most influential for accurate predictions of virus shedding in space and time. We identified environmental predictors and temporal lags of these features that were important for determining where reservoirs are located and multiple independent proxies for reservoir condition. The best‐performing multiscale model delineated periods of low and high virus prevalence, reflecting observed shedding patterns from pooled under‐roost samples. Incorporating regional indicators of food scarcity enhanced model accuracy while incorporating other stress indicators at local scales confounded this signal. This multiscale modeling approach enabled the combination of processes from different ecological scales and identified environmental variables influencing Hendra virus shedding, highlighting how integrating data across scales may improve risk forecasts for other pathogen systems.

Understanding where and when pathogens occur in the environment has implications for reservoir population health and infection risk. In reservoir hosts, infection status and pathogen shedding are affected by processes interacting across different scales: from landscape features affecting host location and transmission to within-host processes affecting host immunity and infectiousness. While uncommonly done, simultaneously incorporating processes across multiple scales may improve pathogen shedding predictions. In Australia, the black flying fox (Pteropus alecto) is a natural host for the zoonotic Hendra virus, which is hypothesized to cause latent infections in bats. Re-activation and virus shedding may be triggered by poor host condition, leading to virus excretion through urine. Here, we developed a statistical modeling approach that combined data at multiple spatial and temporal scales to capture ecological and biological processes potentially affecting virus shedding. We parameterized these models using existing datasets and compared model performance to under-roost virus shedding data from 2011-2014 in 23 roosts across a 1200-km transect. Our approach enabled comparisons among multiple model structures to determine which variables at which scales are most influential for accurate predictions of virus shedding in space and time. We identified environmental predictors and temporal lags of these features that were important for determining where reservoirs are located and multiple independent proxies for reservoir condition. The best-performing multi-scale model delineated periods of low and high virus prevalence, reflecting observed shedding patterns from pooled under-roost samples. Incorporating regional indicators of food scarcity enhanced model accuracy while incorporating other stress indicators at local scales confounded this signal. This multiscale modeling approach enabled the combination of processes from different ecological scales and identified environmental variables influencing Hendra virus shedding, highlighting how integrating data across scales may improve risk forecasts for other pathogen systems. ### Competing Interest Statement The authors have declared no competing interest.

The Allee effect is a density‐dependent phenomenon in which individual fitness increases as population density increases at low population densities. Over the past few decades, a growing number of studies have identified Allee effects in populations using experimental approaches and statistical modeling techniques. These studies have investigated multiple Allee mechanisms (e.g. mate‐finding, predation, resource limitation), across a range of systems and taxa (e.g. plants, vertebrates, invertebrates). This meta‐analysis aims to synthesize studies that experimentally manipulated population density and measured either per capita population growth or fitness components, with the goal of determining whether the ‘magnitude' of the Allee effect (defined here as the positive correlation between population density and population growth or fitness) varies with Allee mechanism across taxonomic groups. A total of 2305 studies were screened, and 62 of these studies met our meta‐analysis inclusion criteria. Within these 62 studies, 155 effect sizes encompassing nine different Allee mechanisms were identified across five broad taxa. When grouped by Allee mechanism and taxa, the magnitude of the Allee effect differed across mechanisms, whereas taxonomic group was less useful at explaining variation in the magnitude of Allee effects. Of the nine Allee mechanisms identified, interspecific competition was associated with the largest Allee effects, followed by fear, pollen limitation and mate limitation. These findings suggest that Allee effects may be more dependent on mechanism than taxa and may function similarly within different taxonomic groups. However, as the majority of experimental Allee effect studies included in this meta‐analysis focused on plants and invertebrates, more research is needed on Allee effects in other taxonomic groups to confirm this conclusion. This first quantitative synthesis of Allee effect research in ecology offers novel insight into how Allee mechanisms affect the manifestation of Allee effects in populations, providing important information for ecologists and conservationists.

Groundwater extraction compromises the function of groundwater-dependent ecosystems, such as freshwater wetlands. Identifying whether groundwater conservation restores wetland hydrology is a first step toward rehabilitating impaired wetlands. In the Tampa Bay region of Florida (U.S.), groundwater extraction rates have been declining since 1998, partly in response to desiccation of wetlands and waterbodies. This study uses monthly water-level data from 152 depressional wetlands over 28 years (1991-2018) to identify trends in wetland inundation, determine whether those trends vary among wetlands historically exposed to different rates of groundwater extraction, and describe relationships between the timing and extent of cutbacks in groundwater extraction and the timing and extent of changes in wetland inundation. Many wetlands (57 %) exhibited increased inundation in response to cutbacks in groundwater extraction, indicating that water conservation measures are inducing recovery. Further, increased inundation began in most wetlands immediately upon, or within two years of, the time extraction cutbacks occurred, although some recovering wetlands exhibited longer lags. An additional 26 % of wetlands had steady-state water levels with inundation similar to that of reference wetlands, potentially revealing a population of wetlands hydrologically unimpaired by nearby groundwater extraction. Another subset of wetlands (14 %) with steady-state water depths exhibited increasing deviations from basin-full water levels, suggesting subsidence of the wetland basin. Active intervention beyond cutbacks in groundwater extraction may be necessary to restore this subset, whereas passive restoration (reducing extraction) appears adequate for the majority of impacted wetlands. Rising water levels may amplify surface-water connections among wetlands, with ecological and biogeochemical consequences both for individual wetlands and for the whole wetlandscape. As a host of human activities continue to rely on groundwater extraction, this study demonstrates the potential for, as well as variability in, hydrological recovery across a wetland-rich, lowrelief landscape following the enactment of water conservation policies.

Background: The ViSIG system, a clinical decision support system for adult intensive care units, was previously shown to be associated with significant reductions in length of stay, readmissions, and duration of mechanical ventilation. However, the health-economic benefits of ViSIG have yet to be fully understood. Infections and acute kidney injury are two measures of clinical significance with key financial implications, and their reduction could further strengthen the published association of ViSIG use with improved outcomes. Further, it is important to demonstrate the economic benefit of ViSIG for its widespread adoption by ICUs. Objective: To investigate the potential health-economic benefits associated with using a previously validated clinical decision support system, ViSIG. Study Design and Methods: In this study, we analyzed data from a previous analysis of ViSIG. The study cohort consisted of six ICUs at two hospitals, with a total of 2,256 admissions in the 'Blinded' Phase and 1,890 admissions in the 'Visible' Phase. We compared the frequency of new infections and Acute Kidney Injury (AKI) between the two phases and used multivariable mixed models to adjust for each patient’s severity of illness. To estimate the economic benefit of ViSIG, we calculated the mean total charges per patient for each phase and the change in patient throughput to arrive at a net gain/loss annual total charges per bed. Results: In the Visible Phase, infections declined by 40.5% (p<0.001). When adjusted for patient severity of illness, the Visible Phase had an odds ratio of 0.52 (95% confidence interval = 0.43, 0.62). There was a 24.5% relative decrease in the occurrence of AKI (p<0.001) and a 35% adjusted decrease from Blinded to Visible Phase (p<0.001). The mean total charges were $59,886 and $55,821 for the Blinded and Visible Phases, respectively. However, due to a reduced ICULOS in the Visible Phase, there was a 14.2% increase in admissions during the 90-day period. This resulted in an estimated mean increased revenue of $3,872 per patient at ICUs using ViSIG. Conclusion: ViSIG was associated with a reduction in two major clinical events: new infections and progression to AKI. This lessened clinical burden may be associated with reduced ICULOS, which resulted in increased patient throughput and, thus, increased revenues. Future studies need to measure specific clinician actions to characterize a cause-and-effect relationship.

Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Here, we use a long-term (14-year) comparative study of 152 depressional wetlands in west-central Florida to identify sources of variation in wetland inundation during two key time periods, 2005-2009 and 2010-2018. These time periods are separated by the enactment of water conservation policies in 2009, which included regional reductions in groundwater extraction. We investigated the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Results show that water levels were lower and hydroperiods were shorter in wetlands of all vegetation classes during the first (2005-2009) time period, which corresponded with low rainfall conditions and high rates of groundwater extraction. Under water conservation policies enacted in the second (2010-2018) time period, median wetland water depths increased 1.35 m and median hydroperiods increased from 46 % to 83 %. Water-level variation was additionally less sensitive to groundwater extraction. The increase in inundation differed among vegetation classes with some wetlands not displaying signs of hydrological recovery. After accounting for effects of several explanatory factors, inundation still varied considerably among wetlands, suggesting a diversity of hydrological regimes, and thus ecological function, among individual wetlands across the landscape. Policies seeking to balance human water demand with the preservation of depressional wetlands would benefit by recognizing the heightened sensitivity of wetland inundation to groundwater extraction during periods of low precipitation.

The United States has thousands of invasive species, representing a sizable, but unknown burden to the national economy. Given the potential economic repercussions of invasive species, quantifying these costs is of paramount importance both for national economies and invasion management. Here, we used a novel global database of invasion costs (InvaCost) to quantify the overall costs of invasive species in the United States across spatiotemporal, taxonomic, and socioeconomic scales. From 1960 to 2020, reported invasion costs totaled $4.52 trillion (USD 2017). Considering only observed, highly reliable costs, this total cost reached $1.22 trillion with an average annual cost of $19.94 billion/year. These costs increased from $2.00 billion annually between 1960 and 1969 to $21.08 billion annually between 2010 and 2020. Most costs (73%) were related to resource damages and losses ($896.22 billion), as opposed to management expenditures ($46.54 billion). Moreover, the majority of costs were reported from invaders from terrestrial habitats ($643.51 billion, 53%) and agriculture was the most impacted sector ($509.55 billion). From a taxonomic perspective, mammals ($234.71 billion) and insects ($126.42 billion) were the taxonomic groups responsible for the greatest costs. Considering the apparent rising costs of invasions, coupled with increasing numbers of invasive species and the current lack of cost information for most known invaders, our findings provide critical information for policymakers and managers.

Species distribution models (SDMs) are widely used to gain ecological understanding and guide conservation decisions. These models are developed with a wide variety of algorithms - from statistic-based approaches to more recent machine learning algorithms - but one property they all have in common is the use of predictors that strongly simplify the temporal variability of driving factors. On the other hand, recent architectures of deep learning neural networks allow dealing with fully explicit spatiotemporal dynamics and thus fitting SDMs without the need to simplify the temporal and spatial dimension of predictor data. We present a deep learning-based SDM approach that uses time series of spatial data as predictors, and compare it with conventional modelling approaches, using five well known invasive species. Deep learning approaches provided consistently high performing models while also avoiding the use of pre-processed predictor sets, that can obscure relevant aspects of environmental variation.### Competing Interest StatementThe authors have declared no competing interest.

The reported costs of invasive alien species from the global database InvaCost are heterogeneous and cover different spatio-temporal scales. A standard procedure for aggregating invasive species cost estimates is necessary to ensure the repeatability and comparativeness of studies. We introduce here the invacost r package, an open-source software designed to query and analyse the InvaCost database. We illustrate this package and its framework with cost data associated with invasive alien invertebrates. First, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the heterogeneous nature of monetary cost data for invasive species, processes to harmonize the data and the inherent biases associated with such data. Third, it readily provides complementary methods to investigate the costs of invasive species at different scales, all the while accounting for econometric statistical issues. This tool will be useful for scientists working on invasive alien species, by (a) facilitating access to and use of this multi-disciplinary data resource and (b) providing a standard procedure which will facilitate reproducibility and comparability among studies, one of the major critics of this topic until now. It should facilitate further interdisciplinary works including economists and invasion ecology researchers.

The global increase in biological invasions is placing growing pressure on the management of ecological and economic systems. However, the effectiveness of current management expenditure is difficult to assess due to a lack of standardised measurement across spatial, taxonomic and temporal scales. Furthermore, there is no quantification of the spending difference between pre-invasion (e.g. prevention) and post-invasion (e.g. control) stages, although preventative measures are considered to be the most cost-effective. Here, we use a comprehensive database of invasive alien species economic costs (InvaCost) to synthesise and model the global management costs of biological invasions, in order to provide a better understanding of the stage at which these expenditures occur. Since 1960, reported management expenditures have totalled at least US$95.3 billion (in 2017 values), considering only highly reliable and actually observed costs — 12-times less than damage costs from invasions ($1130.6 billion). Pre-invasion management spending ($2.8 billion) was over 25-times lower than post-invasion expenditure ($72.7 billion). Management costs were heavily geographically skewed towards North America (54%) and Oceania (30%). The largest shares of expenditures were directed towards invasive alien invertebrates in terrestrial environments. Spending on invasive alien species management has grown by two orders of magnitude since 1960, reaching an estimated $4.2 billion per year globally (in 2017 values) in the 2010s, but remains 1–2 orders of magnitude lower than damages. National management spending increased with incurred damage costs, with management actions delayed on average by 11 years globally following damage reporting. These management delays on the global level have caused an additional invasion cost of approximately $1.2 trillion, compared to scenarios with immediate management. Our results indicate insufficient management — particularly pre-invasion — and urge better investment to prevent future invasions and to control established alien species. Recommendations to improve reported management cost comprehensiveness, resolution and terminology are also made.

Invasive alien species (IAS) are a major driver of global biodiversity loss, hampering conservation efforts and disrupting ecosystem functions and services. While accumulating evidence documented ecological impacts of IAS across major geographic regions, habitat types and taxonomic groups, appraisals for economic costs remained relatively sparse. This has hindered effective cost-benefit analyses that inform expenditure on management interventions to prevent, control, and eradicate IAS. Terrestrial invertebrates are a particularly pervasive and damaging group of invaders, with many species compromising primary economic sectors such as forestry, agriculture and health. The present study provides synthesised quantifications of economic costs caused by invasive terrestrial invertebrates on the global scale and across a range of descriptors, using the InvaCost database. Invasive terrestrial invertebrates cost the global economy US$ 712.44 billion over the investigated period (up to 2020), considering only high-reliability source reports. Overall, costs were not equally distributed geographically, with North America (73%) reporting the greatest costs, with far lower costs reported in Europe (7%), Oceania (6%), Africa (5%), Asia (3%), and South America (< 1%). These costs were mostly due to invasive insects (88%) and mostly resulted from direct resource damages and losses (75%), particularly in agriculture and forestry; relatively little (8%) was invested in management. A minority of monetary costs was directly observed (17%). Economic costs displayed an increasing trend with time, with an average annual cost of US$ 11.40 billion since 1960, but as much as US$ 165.01 billion in 2020, but reporting lags reduced costs in recent years. The massive global economic costs of invasive terrestrial invertebrates require urgent consideration and investment by policymakers and managers, in order to prevent and remediate the economic and ecological impacts of these and other IAS groups.

Deforestation alters wildlife communities and modifies human-wildlife interactions, often increasing zoonotic spillover potential. When deforested land reverts to forest, species composition differences between primary and regenerating (secondary) forest could alter spillover risk trajectory. We develop a mathematical model of land-use change, where habitats differ in their relative spillover risk, to understand how land reversion influences spillover risk. We apply this framework to scenarios where spillover risk is higher in deforested land than mature forest, reflecting higher relative abundance of highly competent species and/or increased human-wildlife encounters, and where regenerating forest has either very low or high spillover risk. We find the forest regeneration rate, the spillover risk of regenerating forest relative to deforested land, and how rapidly regenerating forest regains attributes of mature forest determine landscape-level spillover risk. When regenerating forest has a much lower spillover risk than deforested land, reversion lowers cumulative spillover risk, but instaneous spillover risk peaks earlier. However, when spillover risk is high in regenerating and cleared habitats, landscape-level spillover risk remains high, especially when cleared land is rapidly abandoned then slowly regenerates to mature forest. These results suggest that proactive wildlife management and awareness of human exposure risk in regenerating forests could be important tools for spillover mitigation.

The concept of invasion is useful across a broad range of contexts, spanning from the fine scale landscape of cancer tumors up to the broader landscape of ecosystems. Invasion biology provides extraordinary opportunities for studying the mechanistic basis of contemporary evolution at the molecular level. Although the field of invasion genetics was established in ecology and evolution more than 50 years ago, there is still a limited understanding of how genomic level processes translate into invasive phenotypes across different taxa in response to complex environmental conditions. This is largely because the study of most invasive species is limited by information about complex genome level processes. We lack good reference genomes for most species. Rigorous studies to examine genomic processes are generally too costly. On the contrary, cancer studies are fortified with extensive resources for studying genome level dynamics and the interactions among genetic and non-genetic mechanisms. Extensive analysis of primary tumors and metastatic samples have revealed the importance of several genomic mechanisms including higher mutation rates, specific types of mutations, aneuploidy or whole genome doubling and non-genetic effects. Metastatic sites can be directly compared to primary tumor cell counterparts. At the same time, clonal dynamics shape the genomics and evolution of metastatic cancers. Clonal diversity varies by cancer type, and the tumors’ donor and recipient tissues. Still, the cancer research community has been unable to identify any common events that provide a universal predictor of “metastatic potential” which parallels findings in evolutionary ecology. Instead, invasion in cancer studies depends strongly on context, including order of events and clonal composition. The detailed studies of the behavior of a variety of human cancers promises to inform our understanding of genome level dynamics in the diversity of invasive species and provide novel insights for management.

Abstract Invasive alien fishes have caused pernicious ecological impacts on aquatic ecosystems. However, there has not been a global appraisal of associated economic impacts. Here, we compiled reported economic impacts of invasive alien fishes using the most comprehensive global database of invasion costs (InvaCost). We analyze how fish invasion costs are distributed geographically and temporally, as well as which socioeconomic sectors are most impacted. Fish invasions have caused the economic loss of at least US$32.8 billion globally (2017 value), from only 26 reported species (of 128 known invasive alien fish species). North America had the highest costs (> 99%), followed by Europe and Asia, with no costs reported in Africa, Oceania nor South America. Very few costs from invasive fish in the marine realm were reported (0.1%). Most costs are related to resource damages and losses (97%), with relatively little spent on management; mainly impacting the fisheries sector (93%). However, when only considering empirically observed costs (without predictions), most costs were incurred by authorities and stakeholders through management, indicating that damage costs from invasive fishes are often extrapolated and/or difficult to quantify. Fish invasion costs increase markedly over time, from US$0.57 billion/year in the 1980s to US$1 billion/year in the 2000s. Fish invasions have been relatively well studied; however, economic costs have been lower than expected based on overall numbers of alien species. Accordingly, although costs are increasing, improved reporting is required to better understand how fish invasion costs are distributed across time, space and economic sectors.

Invasive alien fishes have had pernicious ecological and economic impacts on both aquatic ecosystems and human societies. However, a comprehensive and collective assessment of their monetary costs is still lacking. In this study, we collected and reviewed reported data on the economic impacts of invasive alien fishes using InvaCost, the most comprehensive global database of invasion costs. We analysed how total (i.e. both observed and potential/predicted) and observed (i.e. empirically incurred only) costs of fish invasions are distributed geographically and temporally and assessed which socioeconomic sectors are most affected. Fish invasions have potentially caused the economic loss of at least US$37.08 billion (US2017 value) globally, from just 27 reported species. North America reported the highest costs (>85% of the total economic loss), followed by Europe, Oceania and Asia, with no costs yet reported from Africa or South America. Only 6.6% of the total reported costs were from invasive alien marine fish. The costs that were observed amounted to US$2.28 billion (6.1% of total costs), indicating that the costs of damage caused by invasive alien fishes are often extrapolated and/or difficult to quantify. Most of the observed costs were related to damage and resource losses (89%). Observed costs mainly affected public and social welfare (63%), with the remainder borne by fisheries, authorities and stakeholders through management actions, environmental, and mixed sectors. Total costs related to fish invasions have increased significantly over time, from <US$0.01 million/year in the 1960s to over US$1 billion/year in the 2000s, while observed costs have followed a similar trajectory. Despite the growing body of work on fish invasions, information on costs has been much less than expected, given the overall number of invasive alien fish species documented and the high costs of the few cases reported. Both invasions and their economic costs are increasing, exacerbating the need for improved cost reporting across socioeconomic sectors and geographic regions, for more effective invasive alien fish management.

Populations and communities fluctuate in their overall numbers through time, and the magnitude of fluctuations in individual species may scale to communities. However, the composite variability at the community scale is expected to be tempered by opposing fluctuations in individual populations, a phenomenon often called the portfolio effect. Understanding population variability, how it scales to community variability, and the spatial scaling in this variability are pressing needs given shifting environmental conditions and community composition. We explore evidence for portfolio effects using null community simulations and a large collection of empirical community time series from the BioTIME database. Additionally, we explore the relative roles of habitat type and geographic location on population and community temporal variability. We find strong portfolio effects in our theoretical community model, but weak effects in empirical data, suggesting a role for shared environmental responses, interspecific competition, or a litany of other factors. Furthermore, we observe a clear latitudinal signal - and differences among habitat types - in population and community variability. Together, this highlights the need to develop realistic models of community dynamics, and hints at spatial, and underlying environmental, gradients in variability in both population and community dynamics.

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.

White-nose syndrome has caused massive mortality in multiple bat species and spread across much of North America, making it one of the most destructive wildlife diseases on record. This has also resulted in it being one of the most well-documented wildlife disease outbreaks, making it possible to look for changes in the pattern of spatial spread over time. We fit a series of spatial interaction models to the United States county-level observations of the pathogenic fungus, Pseudogymnoascus destructans , that causes white-nose syndrome. Models included the distance between caves, cave abundance, measures of winter length and winter onset, and species richness of all bats and hibernating bats only. We found that the best supported models included all of these factors, but that the particular structure and most informative covariates changed over the course of the outbreak, with winter length displacing winter onset as the most informative measure of winter conditions, and evidence for the effects total species richness and hibernation varying from year to year. We also found that weather had detectable effects on spread. While the effect sizes for cave abundance and species richness were relatively stable over the length of the outbreak, distance became less important as time went on. These findings indicate that although models produced early in the outbreak captured important and consistent aspects of the spatial spread of white-nose syndrome, there were also changes over time in the factors associated with spread, suggesting that forecasts may be improved by iterative model refinement.

Invasive species can have severe impacts on ecosystems, economies, and human health. Though the economic impacts of invasions provide important foundations for management and policy, up-to-date syntheses of these impacts are lacking. To produce the most comprehensive estimate of invasive species costs within North America (including the Greater Antilles) to date, we synthesized economic impact data from the recently published InvaCost database. Here, we report that invasions have cost the North American economy at least US$ 1.26 trillion between 1960 and 2017. Economic costs have climbed over recent decades, averaging US$ 2 billion per year in the early 1960s to over US$ 26 billion per year in the 2010s. Of the countries within North America, the United States (US) had the highest recorded costs, even after controlling for research effort within each country ($5.81 billion per cost source in the US). Of the taxa and habitats that could be classified in our database, invasive vertebrates were associated with the greatest costs, with terrestrial habitats incurring the highest monetary impacts. In particular, invasive species cumulatively (from 1960–2017) cost the agriculture and forestry sectors US$ 527.07 billion and US$ 34.93 billion, respectively. Reporting issues (e.g., data quality or taxonomic granularity) prevented us from synthesizing data from all available studies. Furthermore, very few of the known invasive species in North America had reported economic costs. Therefore, while the costs to the North American economy are massive, our US$ 1.26 trillion estimate is likely very conservative. Accordingly, expanded and more rigorous economic cost reports are necessary to provide more comprehensive invasion impact estimates, and then support data-based management decisions and actions towards species invasions.