Systems thinking offers a powerful way to address complex social–ecological challenges, yet many approaches remain difficult to implement in data-poor, participatory management settings. We developed and applied a practical decision-support framework that integrates participatory causal loop diagramming, stochastic token diffusion modeling, and network centrality analysis. We applied the framework to small-scale octopus ( Octopus mimus ) fisheries in Peru and Chile, where sustainability is challenged by data limitations, informal harvesting, market pressures, and compliance. Through a series of participatory workshops, stakeholders co-developed causal maps, identified shared sustainability goals, evaluated potential interventions, and interpreted network-based analyses. Across both fisheries, stakeholders converged on octopus abundance and fisher compliance as the primary sustainability goals. Token diffusion simulations indicated that traceability interventions produced the strongest and most rapid improvements in fisher compliance, whereas octopus abundance responded through multiple intervention pathways. Network centrality analyses identified several practical monitoring candidates, including landing price, fishing effort, total landings, debt, fishers’ concern for sustainability, and processing quality. The analyses also revealed country-specific dynamics, including stronger propagation of intervention effects in Chile and distinct leverage points associated with product stockpiling. Our findings demonstrate how integrating participatory systems mapping with lightweight network analyses can help stakeholders evaluate interventions, identify monitoring priorities, and support adaptive decision-making in data-poor fisheries.

We evaluated the impact of a philanthropic program investing in the conservation of sites along the Pacific Americas Flyway, which spans >16,000 km of coastline and is used by millions of shorebirds. Using a quasi-experimental, mixed methods approach, we estimated what would have happened to shorebird populations at 17 wintering sites without the sustained and additional investment they received. We modeled shorebird populations across the entire flyway and at sites with and without investment. Combining shorebird abundance estimates with a land-cover classification model, we used the synthetic control method to create counterfactuals for shorebird trends at the treatment sites. We found no evidence of an overall effect across three outcome variables. Species- and site-level treatment effects were heterogeneous, with a few cases showing evidence of a positive effect, including a site with a high level of overall investment. Results suggest six shorebirds declined across the entire flyway, including at many Latin American sites. However, the percentage of flyway populations present at the sites remained stable, and the percentage at the treatment sites was higher (i.e., investment sites) than at control sites. Multiple mechanisms behind our results are possible, including that investments have yet to mitigate impacts and negative impacts at other sites are driving declines at the treatment sites. A limitation of our evaluation is the sole focus on shorebird abundance and the lack of data that prohibits the inclusion of other outcome variables. Monitoring infrastructure is now in place to design a more robust and a priori shorebird evaluation framework across the entire flyway. With this framework, it will prove easier to prioritize limited dollars to result in the most positive conservation outcomes.

The majority of species at risk of being listed under the United States (U.S.) Endangered Species Act (ESA) relies on habitat located on privately owned land (Turner & Rylander, 1998). There are nu...

As a critical stage in the life cycle of ant colonies, nest establishment depends on external and internal factors. This study investigates the effect of the number of queens on queen and worker behavior during nest establishment in invasive Argentine ants (Linepitema humile) and native Mediterranean Tapinoma nigerrimum. We set up experimental colonies with the same number of workers but with one or six queens. At different time points, we recorded the positions of queens and workers inside and outside the nest. Our results highlight the influence of the number of queens on the position of queens and workers with between-species differences. Queens of both species entered the nests more quickly when there were six queens. During nest establishment, more workers were inside nests with six queens for both species, with this effect being greater for T. nigerrimum. Once nests were established, fewer workers of both species were engaged in nest maintenance and feeding in nests with six queens; T. nigerrimum had fewer workers engaged in patrolling. These results suggest that the number of queens is a key factor driving queen and worker behavior during and after nest establishment with different species responses.

Abstract The success of conservation efforts for imperiled and endangered wildlife species relies on private landowners, yet a definitive model of landowner cooperation remains elusive. We use a case study to explore the multiple pathways by which demographics, rootedness, resource dependence, environmental attitudes, social influence, and program structure intersect to jointly explain participation in a federally funded cost‐share program to help prevent the Lesser Prairie‐Chicken from being listed under the U.S. Endangered Species Act. We conducted structured interviews across three ecoregions with 64 participants and 22 nonparticipants. We analyzed the data using fuzzy‐set qualitative comparative analysis, an approach that identifies the multiple combinations of conditions related to engagement in the program. We found that two concepts, landowner characteristics and social influence, were most commonly associated with participation while profiles representing typical landowner tropes performed poorly. Finally, the positive effect of encouragement by agency representatives suggests that agency staff play a central role in determining participation. It also suggests landowners' decision processes may not be as deliberative as the literature on private lands conservation suggests. The results of our case study suggest new avenues for research that explicitly consider the role of heuristics in decisions to participate.

Over the past decade, seafood mislabeling has been increasingly documented, raising public concern over the identity, safety, and sustainability of seafood. Negative outcomes from seafood mislabeling are suspected to be substantial and pervasive as seafood is the world's most highly traded food commodity. Here we provide empirical systems-level evidence that enabling conditions exist for seafood mislabeling in the United States (US) to lead to negative impacts on marine populations and support consumption of products from poorly managed fisheries. Using trade, production, and mislabeling data, we determine that substituted products are more likely to be imported than the product listed on the label. We also estimate that about 60% of US mislabeled apparent consumption associated with the established pairs involves products that are exclusively wild caught. We use these wild-caught pairs to explore population and management consequences of mislabeling. We find that, compared to the product on the label, substituted products come from fisheries with less healthy stocks and greater impacts of fishing on other species. Additionally, substituted products are from fisheries with less effective management and with management policies less likely to mitigate impacts of fishing on habitats and ecosystems compared with the label product. While we provide systematic evidence of environmental impacts from food fraud, our results also highlight the current challenges with production, trade, and mislabeling data, which increase the uncertainty surrounding seafood mislabeling consequences. More integrated, holistic, and collaborative approaches are needed to understand mislabeling impacts and design interventions to minimize mislabeling.

While illegal, unreported, and unregulated (IUU) fishing is a premier issue facing ocean sustainability, characterizing it is challenging due to its clandestine nature. Current approaches can be resource intensive and sometimes controversial. Using Chile as an example, we present a structured process leveraging existing capacity, fisheries officers, that provides a monitoring tool to produce transparent and stand-alone estimates on the level, structure, and characteristics of illegal fishing. We provide a national illegal fishing baseline for Chile, estimating illegal activity for 20 fisheries, representing ~ 70% of annual national landings. For four fisheries, we also estimate the relative importance of illegal activities across sectors, stakeholders, and infrastructure. While providing new information, our results also confirm previous evidence on the general patterns of illegality. Our approach provides an opportunity for government agencies to formalize their institutional knowledge, while accounting for potential biases and reducing fragmentation of knowledge that can prevent effective enforcement. Estimating illegal activity directly from fisheries enforcement officers is complementary to existing approaches, providing a cost-effective, rapid, and rigorous method to measure, monitor, and inform solutions to reduce IUU fishing.

Seafood mislabeling is receiving increased attention by civil society, and programs and policies to address it are being implemented widely. Yet, evidence for the causes of mislabeling are largely limited to anecdotes and untested hypotheses. Mislabeling is commonly assumed to be motivated by the desire to label a lesser value product as a higher value one. Using price data from mislabeling studies, Δmislabel is estimated (i.e., the difference between the price of a labeled seafood product and its substitute when it was not mislabeled) and a meta-analysis is conducted to evaluate the evidence for an overall mislabeling for profit driver for seafood fraud. Evidence is lacking; rather, Δmislabel is highly variable. Country nor location in the supply chain do not account for the observed heterogeneity. The Δmislabel of substitute species, however, provides insights. Some species, such a sturgeon caviar, Atlantic Salmon, and Yellowfin Tuna have a positive Δmislabel, and may have the sufficient characteristics to motivate mislabeling for profit. Atlantic Bluefin Tuna and Patagonian Toothfish have a negative Δmislabel, which could represent an incentive to mislabel in order to facilitate market access for illegally-landed seafood. Most species have price differentials close to zero—suggesting other incentives may be influencing seafood mislabeling. Less than 10% of studies report price information; doing so more often could provide insights into the motivations for fraud. The causes of mislabeling appear to be diverse and context dependent, as opposed to being driven primarily by one incentive.

With the advent of DNA forensics, research on seafood fraud has increased drastically. The documentation of mislabeling has raised concern over the identity, value, and safety of seafood. However, the general characterization of mislabeling is limited. We conduct a Bayesian meta-analysis to estimate global mislabeling rates and their uncertainty across several factors. While the effort to document mislabeling is impressive, it is highly skewed toward certain taxa and geographies. For most products, including all invertebrates, there is insufficient data to produce useful estimates. For others, the uncertainty of estimates has been underappreciated. Mislabeling is commonly characterized by study-level means. Doing so often overestimates mislabeling, masks important product information, and is of limited utility—particularly given that studies often lack adequate sampling designs for parameter estimation. At the global level, overall mislabeling rates do not differ statistically across supply chain locations, product forms, or countries. Product-level estimates are the most informative. The majority of products, for which there is sufficient data, have mislabeling estimates lower than commonly reported. The most credible average mislabeling rate at the product-level is 8% (95% HDI: 4–14%). Importantly, some products have high estimates, which should be priorities for research and interventions. Estimates must be combined with other data in order to understand the extent and potential consequences of mislabeling, which is likely to vary drastically by product. Our meta-analysis, which can be updated with new data, provides a foundation for prioritizing research to inform programs and policies to reduce seafood fraud.

Allee effects have important implications for many aspects of basic and applied ecology. The benefits of aggregation of conspecific individuals are central to Allee effects, which have led to the widely held assumption that social species are more prone to Allee effects. Robust evidence for this assumption, however, remains rare. Furthermore, previous research on Allee effects has failed to adequately address the consequences of the different levels of organisation within social species' populations. Here, we review available evidence of Allee effects and model the role of demographic and behavioural factors that may combine to dampen or strengthen Allee effects in social species. We use examples across various species with contrasting social structure, including carnivores, bats, primates and eusocial insects. Building on this, we provide a conceptual framework that allows for the integration of different Allee effects in social species. Social species are characterised by nested levels of organisation. The benefits of cooperation, measured by mean individual fitness, can be observed at both the population and group levels, giving rise to "population level" and "group level" Allee effects respectively. We also speculate on the possibility of a third level, reporting per capita benefits for different individuals within a group (e.g. castes in social insects). We show that group size heterogeneity and intergroup interactions affect the strength of population-level demographic Allee effects. Populations with higher group size heterogeneity and in which individual social groups cooperate demonstrate the weakest Allee effects and may thus provide an explanation for why extinctions due to Allee effects are rare in social species. More adequately accounting for Allee effects in social species will improve our understanding of the ecological and evolutionary implications of cooperation in social species.

Charisma is a term commonly used in conservation biology to describe species. However, as the term "charismatic species" has never been properly defined, it needs to be better characterized to fully meet its potential in conservation biology. To provide a more complete depiction, we collected information from four different sources to define the species currently considered to be the most charismatic and to understand what they represent to the Western public. First, we asked respondents of two separate surveys to identify the 10 animal species that they considered to be the most charismatic and associate them with one to six traits: Rare, Endangered, Beautiful, Cute, Impressive, and Dangerous. We then identified the wild animals featured on the website homepages of the zoos situated in the world's 100 largest cities as well as on the film posters of all Disney and Pixar films, assuming in both cases that the most charismatic species were generally chosen to attract viewers. By combining the four approaches, we set up a ranked list of the 20 most charismatic animals. The majority are large exotic, terrestrial mammals. These species were deemed charismatic, mainly because they were regarded as beautiful, impressive, or endangered, although no particular trait was discriminated, and species were heterogeneously associated with most of the traits. The main social characteristics of respondents did not have a significant effect on their choices. These results provide a concrete list of the most charismatic species and offer insights into the Western public's perception of charismatic species, both of which could be helpful to target new species for conservation campaigns.

†Both authors contributed equally to this work. 1Estación Biológica de Doñana, CSIC, Sevilla, Spain 2Ecologie Systématique Evolution, CNRS, Univ. Paris-Sud, AgroParisTech, Université Paris-Saclay, Orsay, France 3Salmon and Trout Research Centre, Game and Wildlife Conservation Trust, East Stoke, UK 4Powdermill Nature Reserve, Carnegie Museum of Natural History, Rector, PA, USA 5Department of Ecology, Institute of Entomology, Biology Centre CAS, České Budějovice, Czech Republic

Conservation LettersVolume 10, Issue 6 p. 783-785 CORRESPONDENCEOpen Access Research on Seafood Fraud Deserves Better C. Josh Donlan, Corresponding Author C. Josh Donlan jdonlan@advancedconservation.org Advanced Conservation Strategies, Córdoba, Spain, 14011 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853 USA Correspondence C. J. Donlan, Advanced Conservation Strategies, Córdoba, Spain, 14011 E-mail: jdonlan@advancedconservation.orgSearch for more papers by this authorGloria M. Luque, Gloria M. Luque Advanced Conservation Strategies, Córdoba, Spain, 14011Search for more papers by this authorChris Wilcox, Chris Wilcox CSIRO Oceans and Atmosphere Business Unit, Hobart, TAS, 7000 AustraliaSearch for more papers by this authorStefan Gelcich, Stefan Gelcich Advanced Conservation Strategies, Córdoba, Spain, 14011 Center of Applied Ecology and Sustainability & Centro de Conservacion Marina, Pontificia Universidad Catolica de Chile, Santiago, Chile, 8331150Search for more papers by this authorGeorge W. Koch, George W. Koch Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011 USA Center for Ecosystem, Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorBruce A. Hungate, Bruce A. Hungate Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011 USA Center for Ecosystem, Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this author C. Josh Donlan, Corresponding Author C. Josh Donlan jdonlan@advancedconservation.org Advanced Conservation Strategies, Córdoba, Spain, 14011 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853 USA Correspondence C. J. Donlan, Advanced Conservation Strategies, Córdoba, Spain, 14011 E-mail: jdonlan@advancedconservation.orgSearch for more papers by this authorGloria M. Luque, Gloria M. Luque Advanced Conservation Strategies, Córdoba, Spain, 14011Search for more papers by this authorChris Wilcox, Chris Wilcox CSIRO Oceans and Atmosphere Business Unit, Hobart, TAS, 7000 AustraliaSearch for more papers by this authorStefan Gelcich, Stefan Gelcich Advanced Conservation Strategies, Córdoba, Spain, 14011 Center of Applied Ecology and Sustainability & Centro de Conservacion Marina, Pontificia Universidad Catolica de Chile, Santiago, Chile, 8331150Search for more papers by this authorGeorge W. Koch, George W. Koch Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011 USA Center for Ecosystem, Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this authorBruce A. Hungate, Bruce A. Hungate Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011 USA Center for Ecosystem, Science and Society, Northern Arizona University, Flagstaff, AZ, 86011 USASearch for more papers by this author First published: 21 February 2017 https://doi.org/10.1111/conl.12356Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Stawitz et al. present an analysis of seafood mislabeling and make inferences about its financial and ecological implications. We applaud the authors for tackling this important topic. As presented, however, we have reservations about the research which call into question the main conclusions. First, based on the data and results that are presented, there appear to be errors and some of the conclusions are not supported. Second, there may be a bias in the analyses that favors the conclusions. Third, details are lacking regarding the analyses, challenging their verification. We briefly describe some of the issues. At least, two main conclusions are not supported by the data and analyses presented. The authors claim that mislabeling results in the consumption of fish with less endangered conservation status, and thus “mislabeling may not mislead people into eating less sustainable seafood.” First, the authors fail to mention that many substitute species come from aquaculture, where IUCN status is less relevant and environmental impacts are commonplace (e.g., Salmo salar and Pangasius spp.). In fact, it is perplexing that S. salar (Atlantic salmon) is included as a mislabeled species in the conservation status analysis, since it is commonly a substitute and rarely a mislabeled species (Cline 2012; Warner et al. 2015). Second, there are apparent errors. From our analysis of the data sources (Table S1 in Stawitz et al. 2016), 11 studies included salmon, and only two samples labeled as Atlantic salmon were mislabeled, substituted by Oncorhynchus mykiss in both cases (Filonzi et al. 2010). The former is listed as least concern and the latter has not been assessed (IUCN 2016). Yet, the authors’ figure 2 (with no sample sizes reported) shows Atlantic salmon having an IUCN status of near threatened being substituted by a species with a status of least concern. It is unclear why Atlantic salmon (i.e., an aquaculture product) was included in the IUCN analysis, but several studies targeting wild Pacific salmon (Oncorhynchus spp.) were omitted, some of which revealed high levels of mislabeling (e.g., Cline 2012; Warner et al. 2015). Third, we suspect that outliers might be driving the authors’ conclusion that true species are of improved conservation status. In figures 2 and S7, there are more seafood pairs that are in the opposite direction (i.e., true species are of diminished conservation status), and there are a few pairs with large differences in the direction of the author's conclusion (i.e., grouper and toothfish). This may be influencing the overall conclusion; the authors state that the rest of the data (not included in figure 2) “had no difference in IUCN status between labeled and true items.” A bias may be present in the analyses favoring the conclusion that “mislabeling results in the sale of items of better conservation status and nearly equivalent price.” In choosing substitutes to compare for cost and IUCN status, the authors exclude data-deficient species from their analysis. Thus, substitutes they consider may be drawn from a subset that is likely more valuable, better managed, and information rich. Similar biases have been documented in other systems, where research output was strongly biased toward well-funded settings and more common species (Roberts et al. 2016). Thus, the author's substitutes may overrepresent the value and conservation status of substitutes in general, in the direction of the authors’ conclusions. It might be possible to avoid this potential bias by using life history-based indicators of sustainability. Given the data presented, we cannot quantify the bias (e.g., the authors fail to report the mean IUCN status of labeled and true fish species, and their table S3 reports the number of mislabeling cases, while it is the species that is of interest). The second problematic conclusion is the claim that “distributors had the highest probability of serving mislabeled items (mean = 0.184).” The authors suggest that efforts to reduce mislabeling should be prioritized “at points in the chain-of-custody beyond ports, where the majority of mislabeling occurred.” In figure 5 (with no sample sizes reported), however, the variability across purchase locations is substantial, and no pairwise tests are presented. Further, the statistical model is suspect. The authors first describe the model using a binomial distribution and response variable (i.e., mislabeled or not). They then present the model with mislabeled probability as the response variable (equation #1). The fact that the best performing model (i.e., source) has an AICw of 0.99 and that other models that include the same fixed effect have AICw of 0 suggests that all models in the set could be quite poor. Yet, there is no reporting on the goodness of fit or over dispersion in the final model—both standard practices, nor reporting on the deviance explained and the AIC of the null model which would allow evaluation of the explanatory power of the model. As important, the above conclusion is based on an unbalanced dataset for five species, including the rarely mislabeled Atlantic salmon. This dataset also appears to contain errors. It includes a single study focused on distributors, which includes only 10 samples covering three of the five focal species, of which none were mislabeled (table 1 in Cawthorn et al. 2012). It is unclear where the 18% probability of mislabeling comes from: Cawthorn et al. report an approximately 9% mislabeling rate for all their 108 samples at the distributor level. Similarly, the port mislabeling rate is based on two studies with contradictory results: one from the United States reporting a mislabeling rate of 15% and one from Taiwan reporting a mislabeling rate of 70% from 34 samples for 17 seafood products (US Food and Drug Administration 2014; Chang et al. 2016). But the former study includes only two of the focal species and the latter study contains none. Table 1. Examples of issues with the dataset, presentation, and the use of statistical methodologies in Stawitz et al. 2016 Dataset is opaque and inconsistent. The authors do not justify why they included a U.S. FDA report and a study from “an undergraduate genetics course” in their dataset but chose to exclude the many other mislabeling studies in the gray literature, which includes hundreds of samples barcoded by professional laboratories (e.g., Warner et al. 2015). The sample dataset is not presented by species or source. Opaque data presentation with errors. Many figures do not include standard information (or are presented opaquely), such as sample sizes and measures of variances (e.g., figures 2, 5, S2, and S3). Others appear to have errors (e.g., figure 4 reports sample sizes of log(6), i.e., 1,000,000; in figure 2, arrow thicknesses, which represent sample sizes, are the same). Lack of justification and appropriateness of statistical methodologies. Conclusions rely on bootstrapping; however, summary statistics of raw data nor its underlying distribution is presented. Regarding the multidimensional scaling, no information is reported on the potential complications with unequal sample sizes, which are likely to be significant, nor the appropriateness of its use (e.g., stress value or a Shepard plot to show the preservation of original dissimilarities in the reduced number of dimensions). While we commend the authors for their efforts to go beyond seafood mislabeling documentation, the above issues are not minor and are even more concerning given additional issues with the data, analyses, and reporting (Table 1). Combined with the potential bias, the inferences of the manuscript are not supported, as currently presented. Seafood fraud is a nascent topic, one in which natural and human systems are interacting in complex ways that are likely resulting in place-based consequences. To characterize the system dynamics and provide insights into the financial and ecological implications of seafood fraud, a more careful and cautious approach is required. We urge the authors to formally address these issues and revisit the conclusions of their research. References Cawthorn, D.M., Steinman, H.A. & Witthuhn, R.C. (2012). DNA barcoding reveals a high incidence of fish species misrepresentation and substitution on the South African market. Food Res. Int., 46, 30- 40. Chang, C.-H., Lin, H.-Y., Ren, Q., Lin, Y.-S. & Shao, K.-T. (2016). DNA barcode identification of fish products in Taiwan: government-commissioned authentication cases. Food Control, 66, 38- 43. Cline, E. (2012). Marketplace substitution of Atlantic salmon for Pacific salmon in Washington State detected by DNA barcoding. Food Res. Int., 45, 388- 393 Filonzi, L., Chiesa, S., Vaghi, M. & Nonnis Marzano, F. (2010). Molecular barcoding reveals mislabeling of commercial fish products in Italy. Food Res. Int., 43, 1383- 1388. IUCN. (2016). The IUCN Red List of threatened species. Version 2016-2. www.iucnredlist.org. Downloaded on Nov. 19, 2016. Roberts, B.E., Harris, W.E., Hilton, G.M. & Marsden, S. (2016). Taxonomic and geographic bias in conservation biology research: a systematic review of wildfowl demography studies. Plos One, 11(5), e0153908. Stawitz, C.C., Siple, M.C., Munsch, S.H. & Lee, Q. (2016). Financial and ecological implications of global seafood mislabeling. Conserv. Lett, in press. US Food and Drug Administration. (2014). CFSAN sampling for seafood species labeling in wholesale seafood. US Food and Drug Administration College Park, MD. Warner, K., Mustain, P., Carolin, C. et al. (2015). Oceana reveals mislabeling of America's favorite fish: salmon. Oceana, Washington, D.C. Citing Literature Volume10, Issue6November/December 2017Pages 783-785 ReferencesRelatedInformation

The Allee effect is a theoretical model predicting low growth rates and the possible extinction of small populations. Historically, studies of the Allee effect have focused on demography. As a result, underlying processes other than the direct effect of population density on fitness components are not generally taken into account. There has been heated debate about the potential of genetic processes to drive small populations to extinction, but recent studies have shown that such processes clearly impact small populations over short time scales, and some may generate Allee effects. However, as opposed to the ecological Allee effect, which is underpinned by cooperative interactions between individuals, genetically driven Allee effects require a change in genetic structure to link the decline in population size with a decrease in fitness components. We therefore define the genetic Allee effect as a two-step process whereby a decrease in population size leads to a change in population genetic structure, and in turn, to a decrease in individual fitness. We describe potential underlying mechanisms, and review the evidence for this original type of component Allee effect, using published examples from both plants and animals. The possibility of considering demogenetic feedback in light of genetic Allee effects clarifies the analysis and interpretation of demographic and genetic processes, and the interplay between them, in small populations.

While invasive species eradications are at the forefront of biodiversity conservation, ant eradication failures are common. We reviewed ant eradications worldwide to assess the practice and identify knowledge gaps and challenges. We documented 316 eradication campaigns targeting 11 species, with most occurring in Australia covering small areas (<10ha). Yellow crazy ant was targeted most frequently, while the bigheaded ant has been eradicated most often. Of the eradications with known outcomes, 144 campaigns were successful, totaling approximately 9500ha, of which 8300ha were from a single campaign that has since been partially re-invaded. Three active ingredients, often in combination, are most commonly used: fipronil, hydramethylnon, and juvenile hormone mimics. Active ingredient, bait, and method varied considerably with respect to species targeted, which made assessing factors of eradication success challenging. We did, however, detect effects by active ingredient, number of treatments, and method on eradication success. Implementation costs increased with treatment area, and median costs were high compared to invasive mammal eradications. Ant eradications are in a phase of increased research and development, and a logical next step for practitioners is to develop best practices. A number of research themes that seek to integrate natural history with eradication strategies and methodologies would improve the ability to eradicate ants: increasing natural history and taxonomic knowledge, increasing the efficacy of active ingredients and baits, minimizing and mitigating non-target risks, developing better tools to declare eradication success, and developing alternative eradication methodologies. Invasive ant eradications are rapidly increasing in both size and frequency, and we envisage that eradicating invasive ants will increase in focus in coming decades given the increasing dispersal and subsequent impacts.

File List simulations_AAE.R (MD5: 07353083dc23901674bf0a4e1a66451c) Description The code included simulations_AAE.R allow conducting the analyses of the Tibetan antelope population taking into account an Anthropogenic Allee Effect. All other script that were used in our analyses are available on request.

Many ants are among the most globally significant invasive species. They have caused the local decline and extinction of a variety of taxa ranging from plants to mammals. They disturb ecosystem processes, decrease agricultural production, damage infrastructure and can be a health hazard for humans. Overall, economic costs caused by invasive ants amount to several billion US $ annually. There is general consensus that the future distributions of invasive species are likely to expand with climate change, however this dogma remains poorly tested. Here we model suitable area globally for 15 of the worst invasive ant species, both currently and with predicted climate change (in 2080), globally, regionally and within the world's 34 biodiversity hotspots. Surprisingly, the potential distribution of only five species was predicted to increase (up to 35.8 %) with climate change, with most declining by up to 63.3 %. The ant invasion hotspots are predominantly in tropical and subtropical regions of South America, Africa, Asia and Oceanic islands, and particularly correspond with biodiversity hotspots. Contrary to general expectations, climate change and invasive ant species will not systematically act synergistically. However, ant invasions will likely remain as a major global problem, especially where invasion hotspots coincide with biodiversity hotspots.

Conservation practitioners are increasingly embracing evidence‐based and return on investment (ROI) approaches. Much evidence now exists that documents island biodiversity impacts by invasive mammals. The technical ability to eradicate invasive mammals from islands has increased exponentially; consequently, strategic planning focused on maximizing the ROI is now a limiting factor for island restoration. We use a regional ROI approach to prioritize eradications on islands for seabird conservation in British Columbia, Canada. We do so by integrating economic costs of interventions and applying a resource allocation approach. We estimate the optimal set of islands for eradication under two conservation objectives each with a series of increasing thresholds of population sizes and breeding locations. Our approach (1) identified the most cost‐effective interventions, (2) determined whether or not those interventions were nested with increasing thresholds, and (3) helped justify larger investments when appropriate. More often than not, conservation decisions are made at a regional scale, and decision‐makers often must make choices on how to allocate funds across a number of potential conservation actions. A regional, ROI framework can serve as a decision‐support tool for organizations engaging in discrete interventions in order to maximize benefits for the minimum cost.