Threshold responses of Amazonian stream fishes to timing and extent of deforestation

Deforestation is a primary driver of biodiversity change through habitat loss and fragmentation. Stream biodiversity may not respond to deforestation in a simple linear relationship. Rather, threshold responses to extent and timing of deforestation may occur. Identification of critical deforestation thresholds is needed for effective conservation and management. We tested for threshold responses of fish species and functional groups to degree of watershed and riparian zone deforestation and time since impact in 75 streams in the western Brazilian Amazon. We used remote sensing to assess deforestation from 1984 to 2011. Fish assemblages were sampled with seines and dip nets in a standardized manner. Fish species (n = 84) were classified into 20 functional groups based on ecomorphological traits associated with habitat use, feeding, and locomotion. Threshold responses were quantified using threshold indicator taxa analysis. Negative threshold responses to deforestation were common and consistently occurred at very low levels of deforestation (<20%) and soon after impact (<10 years). Sensitive species were functionally unique and associated with complex habitats and structures of allochthonous origin found in forested watersheds. Positive threshold responses of species were less common and generally occurred at >70% deforestation and >10 years after impact. Findings were similar at the community level for both taxonomic and functional analyses. Because most negative threshold responses occurred at low levels of deforestation and soon after impact, even minimal change is expected to negatively affect biodiversity. Delayed positive threshold responses to extreme deforestation by a few species do not offset the loss of sensitive taxa and likely contribute to biotic homogenization.     

Using spatially explicit,time-dependent analysis to understand how social factors influence conservation outcomes

Conservation across human-dominated landscapes requires an understanding of the social and ecological factors driving outcomes. Studies that link conservation outcomes to social and ecological factors have examined temporally static patterns. However, there may be different social and ecological processes driving increases and decreases in conservation outcomes that can only be revealed through temporal analyses. Through a case study of the invasion of Falcataria moluccana in Hawaii, we examined the association of social factors with increases and decreases in invader distributions over time and space. Over 7 years, rates of invader decrease varied substantially (66–100%) relative to social factors, such as building value, whether land was privately or publically owned, and primary residence by a homeowner, whereas rates of increase varied only slightly (<0.1–3.6%) relative to such factors. These findings suggest that links between social factors and invasion in the study system may be driven more by landowners controlling existing invasive species, rather than by landowners preventing the spread of invasive species. We suggest that spatially explicit, time-dependent analyses provide a more nuanced understanding of the way social factors influence conservation outcomes. Such an understanding can help managers develop outreach programs and policies targeted at different types of landowners in human-dominated landscapes.     

A novel application of cultural consensus models to evaluate conservation education programs

Conservation professionals recognize the need to evaluate education initiatives with a flexible approach that is culturally appropriate. Cultural‐consensus theory (CCT) provides a framework for measuring the extent to which beliefs are communally held and has long been applied by social scientists. In a conservation‐education context, we applied CCT and used free lists (i.e., a list of items on a topic stated in order of cultural importance) and domain analysis (analysis of how free lists go together within a cultural group) to evaluate a conservation education program in which we used a children's picture book to increase knowledge about and empathy for a critically endangered mammal, the Javan slow loris (Nycticebus javanicus). We extracted free lists of keywords generated by students (n = 580 in 18 schools) from essays they wrote before and after the education program. In 2 classroom sessions conducted approximately 18 weeks apart, we asked students to write an essay about their knowledge of the target species and then presented a book and several activities about slow loris ecology. Prior to the second session, we asked students to write a second essay. We generated free lists from both essays, quantified salience of terms used, and conducted minimal residuals factor analysis to determine presence of cultural domains surrounding slow lorises in each session. Students increased their use of words accurately associated with slow loris ecology and conservation from 43% in initial essays to 76% in final essays. Domain coherence increased from 22% to 47% across schools. Fifteen factors contributed to the domain slow loris. Between the first and second essays, factors that showed the greatest change were feeding ecology and slow loris as a forest protector, which increased 7‐fold, and the humancentric factor, which decreased 5‐fold. As demonstrated by knowledge retention and creation of unique stories and conservation opinions, children achieved all six levels of Bloom's taxonomy of learning domains. Free from the constraints of questionnaires and surveys, CCT methods provide a promising avenue to evaluate conservation education programs.     

Applying the dark diversity concept to nature conservation

Linking diversity to biological processes is central for developing informed and effective conservation decisions. Unfortunately, observable patterns provide only a proportion of the information necessary for fully understanding the mechanisms and processes acting on a particular population or community. We suggest conservation managers use the often overlooked information relative to species absences and pay particular attention to dark diversity (i.e., a set of species that are absent from a site but that could disperse to and establish there, in other words, the absent portion of a habitat‐specific species pool). Together with existing ecological metrics, concepts, and conservation tools, dark diversity can be used to complement and further develop conservation prioritization and management decisions through an understanding of biodiversity relativized by its potential (i.e., its species pool). Furthermore, through a detailed understanding of the population, community, and functional dark diversity, the restoration potential of degraded habitats can be more rigorously assessed and so to the likelihood of successful species invasions. We suggest the application of the dark diversity concept is currently an underappreciated source of information that is valuable for conservation applications ranging from macroscale conservation prioritization to more locally scaled restoration ecology and the management of invasive species.     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.     

Quantifying the burden of managing wildlife diseases in multiple host species

Mitigation of infectious wildlife diseases is especially challenging where pathogens affect communities of multiple host species. Although most ecological studies recognize the challenge posed by multiple-species pathogens, the implications for management are typically assessed only qualitatively. Translating the intuitive understanding that multiple host species are important into practice requires a quantitative assessment of whether and how secondary host species should also be targeted by management and the effort this will require. Using a multiple-species compartmental model, we determined analytically whether and how intensively secondary host species should be managed to prevent outbreaks in focal hosts based on the reproduction number of individual host species and between-species transmission rates. We applied the model to the invasive pathogenic fungus Batrachochytrium salamandrivorans in a 2-host system in northern Europe. Avoiding a disease outbreak in the focal host (fire salamanders [Salamandra salamandra]) was impossible unless management also heavily targeted the secondary host (alpine newts [Ichthyosaura alpestris]). Preventing an outbreak in the community required targeted removal of at least 80% of each species. This proportion increased to 90% in the presence of an environmental reservoir of B. salamandrivorans and when the proportion of individuals removed could not be adjusted for different host species (e.g., when using traps that are not species specific). We recommend the focus of disease-mitigation plans should shift from focal species to the community level and calculate explicitly the management efforts required on secondary host species to move beyond the simple intuitive understanding that multiple host species may all influence the system. Failure to do so may lead to underestimating the magnitude of the effort required and ultimately to suboptimal or futile management attempts.     

Targeted gene flow and rapid adaptation in an endangered marsupial

Targeted gene flow is an emerging conservation strategy. It involves translocating individuals with favorable genes to areas where they will have a conservation benefit. The applications for targeted gene flow are wide-ranging but include preadapting native species to the arrival of invasive species. The endangered carnivorous marsupial, the northern quoll (Dasyurus hallucatus), has declined rapidly since the introduction of the cane toad (Rhinella marina), which fatally poisons quolls that attack them. There are, however, a few remaining toad-invaded quoll populations in which the quolls survive because they know not to eat cane toads. It is this toad-smart behavior we hope to promote through targeted gene flow. For targeted gene flow to be feasible, however, toad-smart behavior must have a genetic basis. To assess this, we used a common garden experiment, comparing offspring from toad-exposed and toad-naïve parents raised in identical environments, to determine whether toad-smart behavior is heritable. Offspring from toad-exposed populations were substantially less likely to eat toads than those with toad-naïve parents. Hybrid offspring showed similar responses to quolls with 2 toad-exposed parents, indicating the trait may be dominant. Together, these results suggest a heritable trait and rapid adaptive response in a small number of toad-exposed populations. Although questions remain about outbreeding depression, our results are encouraging for targeted gene flow. It should be possible to introduce toad-smart behavior into soon to be affected quoll populations.     

A Bayesian-weighted approach to predicting the number of newly discovered rare species

In natural ecological communities, most species are rare and thus susceptible to extinction. Consequently, the prediction and identification of rare species are of enormous value for conservation purposes. How many newly found species will be rare in the next field survey? We took a Bayesian viewpoint and used observed species abundance information in an ecological sample to develop an accurate way to estimate the number of new rare species (e.g., singletons, doubletons, and tripletons) in an additional unknown sample. A similar method has been developed for incidence-based data sets. Five seminumerical tests (3 abundance cases and 2 incidence cases) showed that our proposed Bayesian-weight estimator accurately predicted the number of new rare species with low relative bias and low relative root mean squared error and, accordingly, high accuracy. Finally, we applied the proposed estimator to 6 conservation-directed empirical data sets (3 abundance cases and 3 incidence cases) and found the prediction of new rare species was quite accurate; the 95% CI covered the true observed value very well in most cases. Our estimator performed similarly to or better than an unweighted estimator derived from Chao et al. and performed consistently better than the naïve unweighted estimator. We recommend our Bayesian-weight estimator for conservation applications, although the unweighted estimator of Chao et al. may be better under some circumstances. We provide an R package RSE (r are s pecies e stimation) at https://github.com/ecomol/RSE for implementation of the estimators.     

Untapped potential of collective intelligence in conservation and environmental decision making

Environmental decisions are often deferred to groups of experts, committees, or panels to develop climate policy, plan protected areas, or negotiate trade-offs for biodiversity conservation. There is, however, surprisingly little empirical research on the performance of group decision making related to the environment. We examined examples from a range of different disciplines, demonstrating the emergence of collective intelligence (CI) in the elicitation of quantitative estimates, crowdsourcing applications, and small-group problem solving. We explored the extent to which similar tools are used in environmental decision making. This revealed important gaps (e.g., a lack of integration of fundamental research in decision-making practice, absence of systematic evaluation frameworks) that obstruct mainstreaming of CI. By making judicious use of interdisciplinary learning opportunities, CI can be harnessed effectively to improve decision making in conservation and environmental management. To elicit reliable quantitative estimates an understanding of cognitive psychology and to optimize crowdsourcing artificial intelligence tools may need to be incorporated. The business literature offers insights into the importance of soft skills and diversity in team effectiveness. Environmental problems set a challenging and rich testing ground for collective-intelligence tools and frameworks. We argue this creates an opportunity for significant advancement in decision-making research and practice.     

Emotion as a source of moral understanding in conservation

Recent debates around the meaning and implications of compassionate conservation suggest that some conservationists consider emotion a false and misleading basis for moral judgment and decision making. We trace these beliefs to a long-standing, gendered sociocultural convention and argue that the disparagement of emotion as a source of moral understanding is both empirically and morally problematic. According to the current scientific and philosophical understanding, reason and emotion are better understood as partners, rather than opposites. Nonetheless, the two have historically been seen as separate, with reason elevated in association with masculinity and emotion (especially nurturing emotion) dismissed or delegitimated in association with femininity. These associations can be situated in a broader, dualistic, and hierarchical logic used to maintain power for a dominant male (White, able-bodied, upper class, heterosexual) human class. We argue that emotion should be affirmed by conservationists for the novel and essential insights it contributes to conservation ethics. We consider the specific example of compassion and characterize it as an emotional experience of interdependence and shared vulnerability. This experience highlights conservationists’ responsibilities to individual beings, enhancing established and widely accepted beliefs that conservationists have a duty to protect populations, species, and ecosystems (or biodiversity). We argue compassion, thus understood, should be embraced as a core virtue of conservation.