Conservation Biology最新文献

Conservation literature addresses a broad spectrum of interdisciplinary questions and benefits. Conservation science benefits most when a diverse range of authors are represented, particularly those from countries where much conservation work is focused. In other disciplines, it is well known that barriers and biases exist in the academic publishing sphere, which can affect research dissemination and an author's career development. We used a discrete choice experiment to determine how 7 journal attributes affect authors' choices of where to publish in conservation. We targeted authors directly by contacting authors published in 18 target journals and indirectly via communication channels for conservation organizations. We only included respondents who had previously published in a conservation-related journal. We used a multinomial logit model and a latent class model to investigate preferences for all respondents and distinct subpopulations. We identified 3 demographic groups across 1038 respondents (older authors from predominantly middle-income countries, younger authors from predominantly middle-income countries, and younger authors from high-income countries) who had published in conservation journals. Each group exhibited different publishing preferences. Only 2 attributes showed a consistent response across groups: cost to publish negatively affected journal choice, including authors in high-income countries, and authors had a consistent preference for double-blind review. Authors from middle-income countries were willing to pay more for society-owned journals, unlike authors from high-income countries. Journals with a broad geographical scope that were open access and that had relatively high impact factors were preferred by 2 of the 3 demographic groups. However, journal scope and open access were more important in dictating journal choice than impact factor. Overall, different demographics had different preferences for journals and were limited in their selection based on attributes such as open access policy. However, the scarcity of respondents from low-income countries (2% of respondents) highlights the pervasive barriers to representation in conservation research. We recommend journals offer double-blind review, reduce or remove open access fees, investigate options for free editorial support, and better acknowledge the value of local-scale single-species studies. Academic societies in particular must reflect on how their journals support conservation and conservation professionals.

Biodiversity is confronted globally by multiple stressors. Environmental policies must regulate these stressors to achieve targets, but how should that be done when the outcomes of limits on one stressor are contingent on other stressors, about which there is imperfect knowledge? Deriving regulatory frameworks that incorporate these contingencies is an emerging challenge at the science-policy interface. To be fit for implementation, these frameworks need to facilitate the inherently sociopolitical process of policy implementation and account transparently for uncertainty, such that practitioners and other stakeholders can more realistically anticipate the range of potential outcomes to policy. We developed an approach to quantify stressor limits that explicitly accounts for multistressor contingencies. Using an invertebrate data set collected over 30 years throughout New Zealand, we combined ecological and ecotoxicological models to predict biodiversity loss as a function of one stressor, treating multistressor contingencies as a form of uncertainty about the outcomes of limits on that stressor. We transparently accounted for that uncertainty by presenting regulatory limits as bands bounded between optimistic and pessimistic views that practitioners may have about the local context within which limits are applied. In addition to transparently accounting for uncertainties, our framework also leaves room for practitioners to build stakeholder consensus when refining limits to suit different local contexts. A criticism of this open, transparent approach is that it creates too much scope for choosing limits that are lenient on polluters, paralyzing on-the-ground management of multiple stressors, but we demonstrate that this is not necessarily the case.

Expert judgment underpins assessment of threatened ecosystems. However, experts are often narrowly defined, and variability in their judgments may be substantial. Models built from structured elicitation with large diverse expert panels can contribute to more consistent and transparent decision-making. We conducted a structured elicitation under a broad definition of expertise to examine variation in judgments of ecosystem viability and collapse in a critically endangered ecosystem. We explored whether variation in judgments among 83 experts was related to affiliation and management expertise and assessed performance of an average model based on common ecosystem indicators. There were systematic differences among individuals, much of which were not explained by affiliation or expertise. However, of the individuals affiliated with government, those in conservation and environmental departments were more likely to determine a patch was viable than those in agriculture and rural land management. Classification errors from an average model, in which all individuals were weighted equally, were highest among government agriculture experts (27%) and lowest among government conservation experts (12%). Differences were mostly cases in which the average model predicted a patch was viable but the individual thought it was not. These differences arose primarily for areas that were grazed or cleared of mature trees. These areas are often the target of restoration, but they are also valuable for agriculture. These results highlight the potential for conflicting advice and disagreement about policies and actions for conserving and restoring threatened ecosystems. Although adoption of an average model can improve consistency of ecosystem assessment, it can fail to capture and convey diverse opinions held by experts. Structured elicitation and models of ecosystem viability play an important role in providing data-driven evidence of where differences arise among experts to support engagement and discussion among stakeholders and decision makers and to improve the management of threatened ecosystems.

Protected areas (PAs) are an essential tool for conservation amid the global biodiversity crisis. Optimizing PAs to represent species at risk of extinction is crucial. Vertebrate representation in PAs is assessed using species distribution databases from the International Union for Conservation of Nature (IUCN) and the Global Biodiversity Information Facility (GBIF). Evaluating and addressing discrepancies and biases in these data sources are vital for effective conservation strategies. Our objective was to gain insights into the potential constraints (e.g., differences and biases) of these global repositories to objectively depict the diversity of threatened vertebrates in the global system of PAs. We assessed differences in species richness (SR) of threatened vertebrates as reported by IUCN and GBIF in PAs globally and then compared how biased this information was with reports from independent sources for a subset of PAs. Both databases showed substantial differences in SR in PAs (t = -62.35, p ≤ 0.001), but differences varied among regions and vertebrate groups. When these results were compared with data from independent assessments, IUCN overestimated SR by 575% on average and GBIF underestimated SR by 63% on average, again with variable results among regions and groups. Our results indicate the need to improve analyses of the representativeness of threatened vertebrates in PAs such that robust and unbiased assessments of PA effectiveness can be conducted. The scientific community and decision makers should consider these regional and taxonomic disparities when using IUCN and GBIF distributional data sources in PA assessment. Overall, supplementing information in these databases could lead to more robust and reliable analyses. Additional efforts to acquire more comprehensive and unbiased data on species distributions to support conservation decisions are clearly needed.

Accelerating rate of human impact and environmental change severely affects marine biodiversity and increases the urgency to implement the Convention on Biological Diversity (CBD) 30×30 plan for conserving 30% of sea areas by 2030. However, area-based conservation targets are complex to identify in a 3-dimensional (3D) ocean where deep-sea features such as seamounts have been seldom studied mostly due to challenging methodologies to implement at great depths. Yet, the use of emerging technologies, such as environmental DNA combined with modern modeling frameworks, could help address the problem. We collected environmental DNA, echosounder acoustic, and video data at 15 seamounts and deep island slopes across the Coral Sea. We modeled 7 fish community metrics and the abundances of 45 individual species and molecular operational taxonomic units (MOTUs) in benthic and pelagic waters (down to 600-m deep) with boosted regression trees and generalized joint attribute models to describe biodiversity on seamounts and deep slopes and identify 3D protection solutions for achieving the CBD area target in New Caledonia (1.4 million km²). We prioritized the identified conservation units in a 3D space, based on various biodiversity targets, to meet the goal of protecting at least 30% of the spatial domain, with a focus on areas with high biodiversity. The relationship between biodiversity protection targets and the spatial area protected by the solution was linear. The scenario protecting 30% of each biodiversity metric preserved almost 30% of the considered spatial domain and accounted for the 3D distribution of biodiversity. Our study paves the way for the use of combined data collection methodologies to improve biodiversity estimates in 3D structured marine environments for the selection of conservation areas and for the use of biodiversity targets to achieve area-based international targets.

Many citizen scientists are highly motivated to help address the current extinction crisis. Their work is making valuable contributions to protecting species by raising awareness, identifying species occurrences, assessing population trends, and informing direct management actions, such as captive breeding. However, clear guidance is lacking about how to use existing citizen science data sets and how to design effective citizen science programs that directly inform extinction risk assessments and resulting conservation actions based on the International Union for Conservation of Nature (IUCN) Red List criteria. This may be because of a mismatch between what citizen science can deliver to address extinction risk and the reality of what is needed to inform threatened species listing based on IUCN criteria. To overcome this problem, we examined each IUCN Red List criterion (A-E) relative to the five major types of citizen science outputs relevant to IUCN assessments (occurrence data, presence-absence observations, structured surveys, physical samples, and narratives) to recommend which outputs are most suited to use when applying the IUCN extinction risk assessment process. We explored real-world examples of citizen science projects on amphibians and fungi that have delivered valuable data and knowledge for IUCN assessments. We found that although occurrence data are routinely used in the assessment process, simply adding more observations of occurrence from citizen science information may not be as valuable as inclusion of more nuanced data types, such as presence-absence data or information on threats from structured surveys. We then explored the characteristics of citizen science projects that have already delivered valuable data to support assessments. These projects were led by recognized experts who champion and validate citizen science data, thereby giving greater confidence in its accuracy. We urge increased recognition of the value of citizen science data within the assessment process.

Finding effective pathogen mitigation strategies is one of the biggest challenges humans face today. In the context of wildlife, emerging infectious diseases have repeatedly caused widespread host morbidity and population declines of numerous taxa. In areas yet unaffected by a pathogen, a proactive management approach has the potential to minimize or prevent host mortality. However, typically critical information on disease dynamics in a novel host system is lacking, empirical evidence on efficacy of management interventions is limited, and there is a lack of validated predictive models. As such, quantitative support for identifying effective management interventions is largely absent, and the opportunity for proactive management is often missed. We considered the potential invasion of the chytrid fungus, Batrachochytrium salamandrivorans (Bsal), whose expected emergence in North America poses a severe threat to hundreds of salamander species in this global salamander biodiversity hotspot. We developed and parameterized a dynamic multistate occupancy model to forecast host and pathogen occurrence, following expected emergence of the pathogen, and evaluated the response of salamander populations to different management scenarios. Our model forecasted that taking no action is expected to be catastrophic to salamander populations. Proactive action was predicted to maximize host occupancy outcomes relative to wait-and-see reactive management, thus providing quantitative support for proactive management opportunities. The eradication of Bsal was unlikely under all the evaluated management options. Contrary to our expectations, even early pathogen detection had little effect on Bsal or host occupancy outcomes. Our results provide quantitative support that proactive management is the optimal strategy for promoting persistence of disease-threatened salamander populations. Our approach fills a critical gap by defining a framework for evaluating management options prior to pathogen invasion and can thus serve as a template for addressing novel disease threats that jeopardize wildlife and human health.