When and how to use Q methodology to understand perspectives in conservation research

Understanding human perspectives is critical in a range of conservation contexts, for example, in overcoming conflicts or developing projects that are acceptable to relevant stakeholders. The Q methodology is a unique semiquantitative technique used to explore human perspectives. It has been applied for decades in other disciplines and recently gained traction in conservation. This paper helps researchers assess when Q is useful for a given conservation question and what its use involves. To do so, we explained the steps necessary to conduct a Q study, from the research design to the interpretation of results. We provided recommendations to minimize biases in conducting a Q study, which can affect mostly when designing the study and collecting the data. We conducted a structured literature review of 52 studies to examine in what empirical conservation contexts Q has been used. Most studies were subnational or national cases, but some also address multinational or global questions. We found that Q has been applied to 4 broad types of conservation goals: addressing conflict, devising management alternatives, understanding policy acceptability, and critically reflecting on the values that implicitly influence research and practice. Through these applications, researchers found hidden views, understood opinions in depth and discovered points of consensus that facilitated unlocking difficult disagreements. The Q methodology has a clear procedure but is also flexible, allowing researchers explore long‐term views, or views about items other than statements, such as landscape images. We also found some inconsistencies in applying and, mainly, in reporting Q studies, whereby it was not possible to fully understand how the research was conducted or why some atypical research decisions had been taken in some studies. Accordingly, we suggest a reporting checklist.     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

Understanding the distribution of bushmeat hunting effort across landscapes by testing hypotheses about human foraging

Mitigating the massive impacts of defaunation on natural ecosystems requires understanding and predicting hunting effort across the landscape. But such understanding has been hindered by the difficulty of assessing the movement patterns of hunters in thick forests and across complex terrain. We statistically tested hypotheses about the spatial distribution of hunting with circuit theory and structural equation models. We used a data set of >7000 known kill locations in Guyana and hunter movement models to test these methods. Comparing models with different resistance layers (i.e., different estimates of how terrain and land cover influence human movement speed) showed that rivers, on average, limited movement rather than serving as transport arteries. Moreover, far more kills occurred close to villages than in remote areas. This, combined with the lack of support for structural equation models that included latent terms for prey depletion driven by past overhunting, suggests that kill locations in this system tended to be driven by where hunters were currently foraging rather than by influences of historical harvest. These analyses are generalizable to a variety of ecosystems, species, and data types, providing a powerful way of enhancing maps and predictions of hunting effort across complex landscapes.     

Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.     

Interventions for Reducing Extinction Risk in Chytridiomycosis‐Threatened Amphibians

Wildlife diseases pose an increasing threat to biodiversity and are a major management challenge. A striking example of this threat is the emergence of chytridiomycosis. Despite diagnosis of chytridiomycosis as an important driver of global amphibian declines 15 years ago, researchers have yet to devise effective large‐scale management responses other than biosecurity measures to mitigate disease spread and the establishment of disease‐free captive assurance colonies prior to or during disease outbreaks. We examined the development of management actions that can be implemented after an epidemic in surviving populations. We developed a conceptual framework with clear interventions to guide experimental management and applied research so that further extinctions of amphibian species threatened by chytridiomycosis might be prevented. Within our framework, there are 2 management approaches: reducing Batrachochytrium dendrobatidis (the fungus that causes chytridiomycosis) in the environment or on amphibians and increasing the capacity of populations to persist despite increased mortality from disease. The latter approach emphasizes that mitigation does not necessarily need to focus on reducing disease‐associated mortality. We propose promising management actions that can be implemented and tested based on current knowledge and that include habitat manipulation, antifungal treatments, animal translocation, bioaugmentation, head starting, and selection for resistance. Case studies where these strategies are being implemented will demonstrate their potential to save critically endangered species. Intervenciones para Reducir el Riesgo de Extinción en Anfibios Amenazados por la Quitridiomicosis     

Applications of Very High‐Resolution Imagery in the Study and Conservation of Large Predators in the Southern Ocean

The Southern Ocean is one of the most rapidly changing ecosystems on the planet due to the effects of climate change and commercial fishing for ecologically important krill and fish. Because sea ice loss is expected to be accompanied by declines in krill and fish predators, decoupling the effects of climate and anthropogenic changes on these predator populations is crucial for ecosystem‐based management of the Southern Ocean. We reviewed research published from 2007 to 2014 that incorporated very high‐resolution satellite imagery to assess distribution, abundance, and effects of climate and other anthropogenic changes on populations of predators in polar regions. Very high‐resolution imagery has been used to study 7 species of polar animals in 13 papers, many of which provide methods through which further research can be conducted. Use of very high‐resolution imagery in the Southern Ocean can provide a broader understanding of climate and anthropogenic forces on populations and inform management and conservation recommendations. We recommend that conservation biologists continue to integrate high‐resolution remote sensing into broad‐scale biodiversity and population studies in remote areas, where it can provide much needed detail. Aplicaciones de Imágenes de Muy Alta Resolución en el Estudio y Conservación de Grandes Depredadores en el Océano Antártico     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.     

Quantifying population declines based on presence‐only records for red‐list assessments

Accurate trend estimates are necessary for understanding which species are declining and which are most in need of conservation action. Imperfect species detection may result in unreliable trend estimates because this may lead to the overestimation of declines. Because many management decisions are based on population trend estimates, such biases could have severe consequences for conservation policy. We used an occupancy‐modeling framework to estimate detectability and calculate nationwide population trends for 14 Swiss amphibian species both accounting for and ignoring imperfect detection. Through the application of International Union for Conservation of Nature Red List criteria to the different trend estimates, we assessed whether ignoring imperfect detection could affect conservation policy. Imperfect detection occurred for all species and detection varied substantially among species, which led to the overestimation of population declines when detectability was ignored. Consequently, accounting for imperfect detection lowered the red‐list risk category for 5 of the 14 species assessed. We demonstrate that failing to consider species detectability can have serious consequences for species management and that occupancy modeling provides a flexible framework to account for observation bias and improve assessments of conservation status. A problem inherent to most historical records is that they contain presence‐only data from which only relative declines can be estimated. A move toward the routine recording of nonobservation and absence data is essential if conservation practitioners are to move beyond this toward accurate population trend estimation.     

The role of digital data entry in participatory environmental monitoring

Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.     

三峡蓄水期间汉丰湖消落区营养状态时间变化

为探明三峡蓄水后汉丰湖消落区水质营养状态的变化特征,于2013年10月至2014年2月对水质进行连续观察,测定了水质物理参数、营养盐与叶绿素(Chl-a)的质量浓度.结果表明,水体中营养盐与Chl-a质量浓度的增加,在淹水后营养程度有升高现象,2014年2月与2013年10月相比,TN、TP、高锰酸盐指数与Chl-a质量浓度分别增加了4.7、1.0、0.2、3.27倍,TN、TP质量浓度均超过藻类生长限值,随滞留时间延长易造成水体富营养化,应引起重视.Chl-a单因子评价反映出水质由贫营养向富营养演变.TN/TP结果表明,TN、TP分别在不同时间内制约着藻类的生长;2013年10~12月与2014年2月,藻类生长受TN限制;2014年1月,藻类生长受TP限制.Chl-a与p H、DO、NH+4-N、NO-3-N、TN、高锰酸盐指数及TP呈显著正相关,而与SD、水温呈显著负相关;蓄水期间,水质受到了同一污染源的影响.因子分析结果表明,汉丰湖消落区水质主要受p H、DO、NO-3-N、TN的影响,同时Chl-a、TP、NH+4-N与好氧性有机物的污染不可忽视;在蓄水稳定初期水体具有自净能力,随蓄水滞留时间的延长,水质污染程度整体上呈现逐步恶化的趋势,应加以控制;三峡蓄水期间,南河、东河营养程度相对较高,应加强治理.