Global cooperation among diverse organizations to reduce illegal fishing in the Southern Ocean

Illegal, unreported, and unregulated (IUU) fishing is prevalent globally and has detrimental effects on commercial fish stocks and nontarget species. Effective monitoring and enforcement aimed at reducing the level of IUU fishing in extensive, remote ocean fisheries requires international collaboration. Changes in trade and vessel activities further complicate enforcement. We used a web-based survey of governmental and nongovernmental organizations engaged in reducing IUU fishing in the Southern Ocean to collect information on interorganizational collaborations. We used social-network analyses to examine the nature of collaborations among the identified 117 organizations engaged in reducing IUU fishing. International collaboration improved the ability to control and manage harvest of commercially important toothfish (Dissostichus spp.) stocks and reduced bycatch of albatrosses (Diomedeidae) and petrels (Procellariidae) in longlines of IUU fishing vessels. The diverse group of surveyed organizations cooperated frequently, thereby making a wide range of resources available for improved detection of suspected IUU vessels and trade flows, cooperation aimed at prosecuting suspected offenders or developing new policy measures. Our results suggest the importance of a central agency for coordination and for maintaining commonly agreed-upon protocols for communication that facilities collaboration. Despite their differences, the surveyed organizations have developed common perceptions about key problems associated with IUU fishing. This has likely contributed to a sustained willingness to invest in collaborations. Our results show that successful international environmental governance can be accomplished through interorganizational collaborations. Such cooperation requires trust, continuous funding, and incentives for actors to participate.     

Focus and social contagion of environmental organization advocacy on Twitter

Agriculture, overexploitation, and urbanization remain the major threats to biodiversity in the Anthropocene. The attention these threats garner among leading environmental nongovernmental organizations (eNGOs) and the wider public is critical in fostering the political will necessary to reverse biodiversity declines worldwide. I analyzed the advocacy of leading eNGOs on Twitter by scraping account timelines, screening content for advocacy relating to biodiversity threats and, for prevalent threats, further screening content for positive and negative emotional language with a sentiment lexicon. Twitter advocacy was dominated by the major threats of climate change and overexploitation and the minor threat of plastic pollution. The major threats of agriculture, urbanization, invasions, and pollution were rarely addressed. Content relating to overexploitation and plastic pollution was more socially contagious than other content. Increasing emotional negativity further increased social contagion, whereas increasing emotional positivity did not. Scientists, policy makers, and eNGOs should consider how narrowly focused advocacy on platforms like Twitter will contribute to effective global biodiversity conservation.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Optimal Management of a Multispecies Shorebird Flyway under Sea‐Level Rise

Every year, millions of migratory shorebirds fly through the East Asian–Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea‐level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species. Gestión Óptima de una Ruta Migratoria de Múltiples Especies de Aves Costeras Sometida a Incremento del Nivel del Mar     

Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

Leveraging social media and deep learning to detect rare megafauna in video surveys

Deep learning has become a key tool for the automated monitoring of animal populations with video surveys. However, obtaining large numbers of images to train such models is a major challenge for rare and elusive species because field video surveys provide few sightings. We designed a method that takes advantage of videos accumulated on social media for training deep-learning models to detect rare megafauna species in the field. We trained convolutional neural networks (CNNs) with social media images and tested them on images collected from field surveys. We applied our method to aerial video surveys of dugongs (Dugong dugon) in New Caledonia (southwestern Pacific). CNNs trained with 1303 social media images yielded 25% false positives and 38% false negatives when tested on independent field video surveys. Incorporating a small number of images from New Caledonia (equivalent to 12% of social media images) in the training data set resulted in a nearly 50% decrease in false negatives. Our results highlight how and the extent to which images collected on social media can offer a solid basis for training deep-learning models for rare megafauna detection and that the incorporation of a few images from the study site further boosts detection accuracy. Our method provides a new generation of deep-learning models that can be used to rapidly and accurately process field video surveys for the monitoring of rare megafauna.     

A guide to using the internet to monitor and quantify the wildlife trade

The unrivaled growth in e-commerce of animals and plants presents an unprecedented opportunity to monitor wildlife trade to inform conservation, biosecurity, and law enforcement. Using the internet to quantify the scale of the wildlife trade (volume and frequency) is a relatively recent and rapidly developing approach that lacks an accessible framework for locating relevant websites and collecting data. We produced an accessible guide for internet-based wildlife trade surveillance. We detailed a repeatable method involving a systematic internet search, with search engines, to locate relevant websites and content. For data collection, we highlight web-scraping technology as an efficient way to collect data in an automated fashion at regularly timed intervals. Our guide is applicable to the multitude of trade-based contexts because researchers can tailor search keywords for specific taxa or derived products and locations of interest. We provide information for working with the diversity of websites used in wildlife trade. For example, to locate relevant content on social media (e.g., posts or groups), each social media platform should be examined individually via the site's internal search engine. A key advantage of using the internet to study wildlife trade is the relative ease of access to an increasing amount of trade-related data. However, not all wildlife trade occurs online and it may occur on unobservable sections of the internet.     

Linkages between measures of biodiversity and community resilience in Pacific Island agroforests

Designing agroecosystems that are compatible with the conservation of biodiversity is a top conservation priority. However, the social variables that drive native biodiversity conservation in these systems are poorly understood. We devised a new approach to identify social–ecological linkages that affect conservation outcomes in agroecosystems and in social‐ecological systems more broadly. We focused on coastal agroforests in Fiji, which, like agroforests across other small Pacific Islands, are critical to food security, contain much of the country's remaining lowland forests, and have rapidly declining levels of native biodiversity. We tested the relationships among social variables and native tree species richness in agroforests with structural equation models. The models were built with data from ecological and social surveys in 100 agroforests and associated households. The agroforests hosted 95 native tree species of which almost one‐third were endemic. Fifty‐eight percent of farms had at least one species considered threatened at the national or international level. The best‐fit structural equation model (R² = 47.8%) showed that social variables important for community resilience—local ecological knowledge, social network connectivity, and livelihood diversity—had direct and indirect positive effects on native tree species richness. Cash‐crop intensification, a driver of biodiversity loss elsewhere, did not negatively affect native tree richness within parcels. Joining efforts to build community resilience, specifically by increasing livelihood diversity, local ecological knowledge, and social network connectivity, may help conservation agencies conserve the rapidly declining biodiversity in the region.     

Speaking across boundaries to explore the potential for interdisciplinarity in ecosystem services knowledge production

Conservation is likely to be most successful if it draws on knowledge from across the natural and social sciences. The ecosystem services concept has been called a boundary object in that it facilitates development of such interdisciplinary knowledge because it offers a common platform for researchers, policy makers, and practitioners. However, a question that remains is to what extent the interdisciplinary knowledge needed is provided by disciplinary diversity within the field. We asked where is knowledge on ecosystem services produced, how interdisciplinary is this knowledge, and which disciplines facilitate the greatest disciplinary integration? We defined interdisciplinarity as the extent to which published research draws on knowledge that crosses disciplinary borders and used citations as a quantitative indicator of communication among disciplines, based on journal classification. We used disciplinary diversity, richness, and heterocitation as measures of interdisciplinarity and betweenness centrality as a measure of disciplinary integration. Our data set contained 22,153 publications on ecosystem services, published from 1983 to 2018. We found that ecosystem services research matured; average yearly output growth was 33.8%, more than the 8–9% growth in scientific output across all fields. Over time, the network clustering coefficient, measuring connectedness of individual disciplines, rose from 0.388 to 0.727, suggesting increased density in the network of citations. Researchers in the field published more articles (3566 in 2018 alone) across more disciplines (77 unique disciplines in 2018). However, this growth was not mirrored by an increase in the diversity (stable at 0.7–0.9) or richness (averaging 0.35 unique disciplines per citation) of citation patterns. Heterocitation scores, or out-of-group citations, for arts, humanities, social sciences, and law ranged from 56% to 64%, which was lower than we expected, although this may serve to protect the integrity of social science disciplines and attract broader engagement from within. Ultimately, a small number of productive disciplines are central to supporting disciplinary integration. However, opportunities exist for conservation practice to draw on a broader field of research, to realize the potential that the diverse body of knowledge of interdisciplinary work offers.