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Social media data are transforming sustainability science. However, challenges from restrictions in data accessibility and ethical concerns regarding potential data misuse have threatened this nascent field. Here, we review the literature on the use of social media data in environmental and sustainability research. We find that they can play a novel and irreplaceable role in achieving the UN Sustainable Development Goals by allowing a nuanced understanding of human-nature interactions at scale, observing the dynamics of social-ecological change, and investigating the co-construction of nature values. We reveal threats to data access and highlight scientific responsibility to address trade-offs between research transparency and privacy protection, while promoting inclusivity. This contributes to a wider societal debate of social media data for sustainability science and for the common good.

Analyzing the spatial and temporal properties of information flow with a multi-century perspective could illuminate the sustainability of human resource-use strategies. This paper uses historical and archaeological datasets to assess how spatial, temporal, cognitive, and cultural limitations impact the generation and flow of information about ecosystems within past societies, and thus lead to tradeoffs in sustainable practices. While it is well understood that conflicting priorities can inhibit successful outcomes, case studies from Eastern Polynesia, the North Atlantic, and the American Southwest suggest that imperfect information can also be a major impediment to sustainability. We formally develop a conceptual model of Environmental Information Flow and Perception (EnIFPe) to examine the scale of information flow to a society and the quality of the information needed to promote sustainable coupled natural-human systems. In our case studies, we assess key aspects of information flow by focusing on food web relationships and nutrient flows in socio-ecological systems, as well as the life cycles, population dynamics, and seasonal rhythms of organisms, the patterns and timing of species’ migration, and the trajectories of human-induced environmental change. We argue that the spatial and temporal dimensions of human environments shape society's ability to wield information, while acknowledging that varied cultural factors also focus a society's ability to act on such information. Our analyses demonstrate the analytical importance of completed experiments from the past, and their utility for contemporary debates concerning managing imperfect information and addressing conflicting priorities in modern environmental management and resource use.

Social-ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human-nature interdependencies in a changing world.

The sustainability of marine communities is critical for supporting many biophysical processes that provide ecosystem services that promote human well-being. It is expected that anthropogenic disturbances such as climate change and human activities will tend to create less energetically-efficient ecosystems that support less biomass per unit energy flow. It is debated, however, whether this expected development should translate into bottom-heavy communities (with small basal species being the most abundant and large apex predators the least abundant) or top-heavy communities (where more biomass is supported at higher trophic levels with species having larger body sizes). Here, we combine ecological theory and empirical data to demonstrate that protection from harvesting promotes top-heavy energetically-efficient structures in marine communities. First, we use metabolic scaling theory to show that protected communities are expected to display stronger top-heavy structures than harvested communities. Similarly, we show theoretically that communities with high energy transfer efficiency display stronger top-heavy structures than communities with low transfer efficiency. Next, we use, as a natural experiment, the structures observed within fully protected marine areas compared to harvested areas across 299 geographical sites worldwide that vary in stress from thermal events and adjacent human activity. Using a nonparametric causal-inference analysis, we find a strong, positive, causal effect between protection from harvesting and top-heavy structures. Our work corroborates ecological theory on community development and provides a framework for additional research into the restorative effects of protected areas.

While environmental reconstruction has been a staple in the study of past societies, underused tools from ecology, such as food webs, can enable a more thorough understanding of the human place within ecosystems. Drawing on two recent studies, this article describes the types of questions that can be addressed using this approach. The authors demonstrate how food webs that include archaeological data can provide insights into the effects of extinctions, invasion and ecosystem change on communities, and can address key questions of the structure and dynamics of past societies. This article highlights examples of best practice for the compilation of archaeo-ecological networks, and suggest ways of developing a synthetic understanding of past environments.

We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast two methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG) and Citizen Voting Age Population (CVAP), and two algorithms for performing EI, King’s EI and EI:RxC. Using data from two different elections we identify circumstances in which different combinations of methods produce divergent results, comparing against ground-truth data where available. We first find that BISG outperforms CVAP at estimating racial composition when precincts are sufficiently homogeneous. Next, in a statewide election, we find that all combinations of methods yield similar results. However, county-level analyses and results from a non-partisan school board election reveal that BISG and CVAP produce divergent estimates of Black preferences in elections with low-turnout and few precincts. Our results suggest that methodological choices can meaningfully alter conclusions about RPV, particularly in smaller, low-turnout elections.

Social media are being increasingly used to inform visitor use management in parks and protected areas. We review the state of the scientific literature to understand the ways social media has been, and can be, used to measure visitation, spatial patterns of use, and visitors’ experiences in parks and protected areas. Geotagged social media are a good proxy for actual visitation; however, the correlations observed by previous studies between social media and other sources of visitation data vary substantially. Most studies using social media to measure visitation aggregate data across many years, with very few testing the use of social media as a visitation proxy at smaller temporal scales. No studies have tested the use of social media to estimate visitation in near real-time. Studies have used geotags and GPS tracks to understand spatial patterns of where visitors travel within parks, and how that may relate to other variables (e.g., infrastructure), or differ by visitor type. Researchers have also found the text content, photograph content, and geotags from social media posts useful to understand aspects of visitors’ experiences, such as behaviors, preferences, and sentiment. The most cited concern with using social media is that this data may not be representative of all park users. Collectively, this body of research demonstrates a broad range of applications for social media. We synthesize our findings by identifying gaps and opportunities for future research and presenting a set of best practices for using social media in parks and protected areas.

Prevention of aquatic invasive species is a fundamental management challenge. With hundreds of millions of people participating in fishing trips each year, understanding angler movements that transmit invasive species can provide critical insight into the most effective locations and scales at which to apply preventative measures. Recent evidence suggests that mobile technologies provide new opportunities to understand different types of angler movement behaviour beyond what is possible with infrequently and sparsely conducted in-person boat surveys and mail questionnaires. Here we capitalise on data provided by ReelSonar’s iBobber, a sonar-enabled bobber with over 5 M recorded fishing locations, globally. By quantifying geographic patterns of fishing activities and assessing how these patterns change seasonally, we explore angler behaviour across the entire continental United States in terms of fishing frequency and distance travelled between sites and characterise the attributes of fished ecosystems. We found that iBobber users (anglers) undertook 66,918 trips to 20,049 different water-bodies over a two-year period. Anglers who use iBobber were more likely to visit larger, deeper and more urbanised water-bodies and these water-bodies were over five times more likely to be a reservoir compared to a lake. Inter-water-body travel road distances averaged 93 km (SD = 277 km; range < 1–300 km) and nearly half of these movements occurred over a timespan of two days or less, a timeframe that we show falls well within the desiccation tolerance window of many prevalent plant and animal invasive species. Our study offers novel insight into spatiotemporal patterns of angler behaviour well beyond the geographical and temporal extent of conventional ground-collected approaches and carries important implications for predicting and preventing future transmission of aquatic invasive species via recreational fishing.

Natural disturbances such as wildfires are increasing in severity and frequency. Although the ecological impacts of disturbance are well documented, we have limited understanding of how disturbances and associated management responses influence recreation use patterns. This reflects, in part, difficulty in quantifying recreation use across different land ownerships with inconsistent, or non-existent, recreation monitoring practices. In this study, we use visitation models based on social media to examine how recreation use changed after a wildfire and site closures in a large, mixed-ownership landscape. We find that wildfire and associated closures resulted in visitation loss to the recreation system as a whole and little site-to-site displacement within the system in the two years following the wildfire. Our study highlights the importance, when considering how wildfire and management may alter recreation use patterns, of considering the many factors that influence substitution behavior, including the relative locations of visitor origins, disturbances, and substitute sites.

Environmental management increasingly relies on information about ecosystem services for decision-making. Compared with regulating and provisioning services, cultural ecosystem services (CES) are particularly challenging to characterize and measure at management-relevant spatial scales, which has hindered their consideration in practice. Social media are one source of spatially explicit data on where environments support various types of CES, including physical activity. As tools for automating social media content analysis with artificial intelligence (AI) become more commonplace, studies are promoting the potential for AI and social media to provide new insights into CES. Few studies, however, have evaluated what biases are inherent to this approach and whether it is truly reproducible. This study introduces and applies a novel and open-source convolutional neural network model that uses computer vision to recognize recreational activities in the content of photographs shared as social media. We train a model to recognize 12 common recreational activities to map one aspect of recreation in a national forest in Washington, USA, based on images uploaded to Flickr. The image classifier performs well, overall, but varies by activity type. The model, which is trained with data from one region, performs nearly as well in a novel region of the same national forest, suggesting that it is broadly applicable across similar public lands. By comparing the results from our CNN model with an on-site survey, we find that there are apparent biases in which activities visitors choose to photograph and post to social media. After considering potential issues with underlying data and models, we map activity diversity and find that natural features (such as rivers, lakes and higher elevations) and some built infrastructure (campgrounds, trails, roads) support a greater diversity of activities in this region. We make our model and training weights available in open-source software, to facilitate reproducibility and further model development by researchers who seek to understand recreational values at management-relevant scales—and more broadly provide an example of how to build, test and apply AI to understand recreation and other types of CESs.

Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies.

Larger geographical areas contain more species—an observation raised to a law in ecology. Less explored is whether biodiversity changes are accompanied by a modification of interaction networks. We use data from 32 spatial interaction networks from different ecosystems to analyse how network structure changes with area. We find that basic community structure descriptors (number of species, links and links per species) increase with area following a power law. Yet, the distribution of links per species varies little with area, indicating that the fundamental organization of interactions within networks is conserved. Our null model analyses suggest that the spatial scaling of network structure is determined by factors beyond species richness and the number of links. We demonstrate that biodiversity–area relationships can be extended from species counts to higher levels of network complexity. Therefore, the consequences of anthropogenic habitat destruction may extend from species loss to wider simplification of natural communities., This work was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41 and 394 ANR-11-IDEX-002-02) to J.M.M., by a Region Midi-Pyrenees project (CNRS 121090) to J.M.M., and by the FRAGCLIM Consolidator Grant (726176) to J.M.M. from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program. The study was also supported by Spanish MICINN projects CGL2009-12646, CSD2008-0040 and CGL2013-41856 to J.B. and A.R. C.E. was funded through the São Paulo Research Foundation (FAPESP 2015/15172-7). V.A.G.B. was funded by National Funds through FCT—Foundation for Science and Technology under the Project UIDB/05183/2020. W.T. received funding from the ERA-Net BiodivERsA—Belmont Forum, with the national funder Agence National pour la Recherche (FutureWeb: ANR-18-EBI4–0009 and BearConnect: ANR-16-EBI3-0003).

Collecting well-resolved empirical trophic networks requires significant time, money and expertise, yet we are still lacking knowledge on how sampling effort and bias impact the estimation of network structure. Filling this gap is a critical first step towards creating accurate representations of ecological networks and for teasing apart the impact of sampling compared to ecological and evolutionary processes that are known to create spatio-temporal variation in network structure. We use a well-sampled spatial dataset of lake food webs to examine how sample effort influences network structure. Specifically, we predict asymptotic network properties (ANPs) for our dataset by comparing lake-specific network metrics with increasing sampling effort. We then contrast three sampling strategies - random, smallest lake to largest lake or largest lake to smallest lake - to assess which strategy best captures the regional metaweb (i.e. network of all potential interactions) network properties. We demonstrate metric-specific relationships between sample effort and network metrics, often diverging from the ANPs. For example, low sample effort can contribute to much lower and poorer estimates of closeness centralization, as compared to approximations of modularity with similar sample efforts. In fact, many network metrics (e.g. connectance) have a quadratic relationship with sample effort indicating a sampling 'sweet spot', which represents optimal sample effort for a close approximation of the ANP. Further, we find that sampling larger lakes followed by smaller lakes is a more optimal sampling strategy for capturing metaweb properties in this lentic ecosystem. Overall, we provide clear ways to better understand the impacts of sampling bias in food-web studies which may be particularly critical given the rapid increase in studies comparing food webs across space and time.

The COVID-19 pandemic and the resulting economic recession have negatively affected many people’s physical, social, and psychological health. Parks and green-spaces may have ameliorated the negative effects of the pandemic by creating opportunities for outdoor recreation and nature exposure, while other public activities and gatherings were restricted due to risk of disease transmissions. Using park visitation estimates derived from 140K Instagram images shared in four US metropolitan areas, this study investigates trends in park use over the span of the COVID-19 pandemic. We find that while COVID-related stay-at-home orders are associated with shifts in park visitation, the cities that we analysed do not follow uniform trends. Our analyses suggest that future research may be able to explain variability in park visitation based on local factors such as park location and the socio-demographics of visitors. However, the research community does not currently have access to the volume and resolution of data that is necessary to study the issue. There is an urgent need for the CSCW community and social computing researchers to address this data gap if we are to understand the impacts of the pandemic, plan for urban resiliency, and ensure equitable access to parks and other shared resources.

Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature–physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature’s potential contribution to creating more equitable, green, livable cities with active inhabitants.

Author(s): Baiser, B; Gravel, D; Cirtwill, AR; Dunne, JA; Fahimipour, AK; Gilarranz, LJ; Grochow, JA; Li, D; Martinez, ND; McGrew, A; Poisot, T; Romanuk, TN; Stouffer, DB; Trotta, LB; Valdovinos, FS; Williams, RJ; Wood, SA; Yeakel, JD | Abstract: Aim: How do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well-studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann's rule, the island rule and Rapoport's rule] are associated with the architecture of food webs. Location: Global. Time period: Current. Major taxa studied: All taxa. Methods: We discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda. Results: We derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport's rule), while for others (e.g., Bergmann's and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport's rule, we found support for the prediction that species turnover in food webs is inversely related to latitude. Main conclusions: The macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.

Urban nature has the potential to improve air and water quality, mitigate flooding, enhance physical and mental health, and promote social and cultural well-being. However, the value of urban ecosystem services remains highly uncertain, especially across the diverse social, ecological and technological contexts represented in cities around the world. We review and synthesize research on the contextual factors that moderate the value and equitable distribution of ten of the most commonly cited urban ecosystem services. Our work helps to identify strategies to more efficiently, effectively and equitably implement nature-based solutions. The value of ecosystem services in cities around the world is highly uncertain. This Review focuses on ten of the most commonly cited urban ecosystem services and presents a synthesis of the scholarship on the factors that moderate the value and equitable distribution of such services.

Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

The ornamental aquarium pet trade is a leading pathway for the introduction of aquatic invasive species. In addition to purchasing live organisms in stores, hobbyists are engaging more with alternative informal online marketplaces that enable peer-to-peer selling of aquarium organisms via auctions. Although growing in popularity, little is known regarding the global extent of informal marketplaces, including the taxonomy of species that are traded, their economic value, and the geographic routes by which live organisms are transported. In this study we use an automated web crawler to collect data on completed auctions between 2011 and 2017 from the largest informal market for aquarium hobbyists, AquaBid, to understand the market dynamics and trade flows of the informal retail market online. During the 7-year study period, the AquaBid website facilitated the estimated trade of 539,548 live freshwater animals, 579,700 fish eggs, and 31,431 plant assortments/bunches among 24,409 unique users who collectively placed 444,132 bids on 192,227 auctions, representing a total sale value of $6,015,030 USD. Source (seller) and recipient (buyer) locations of live organisms were distributed across 39 countries but concentrated largely in major cities of the United States and select European and southeast Asian countries. Our study is among the first to quantify geographic routes of live organism transport between specific locations on the landscape and demonstrates the highly diffuse and non-centralized nature of the informal aquarium trade. Evaluating the emerging challenges represented by informal online retail marketplaces is critical to create policy and regulatory solutions that minimize the transport of prohibited invasive species. Electronic supplementary material The online version of this article (10.1007/s10750-020-04407-7) contains supplementary material, which is available to authorized users.

Recent years have seen an increase in the use of social media data for various decision-making purposes in the context of Urban Computing and smart cities, including management of public parks. Parks are critical natural assets that promote the health and well-being of urban residents, making it important that managers consider information about the quantity and character of park use in location and design decisions. However, as policy and management decisions rely on more autonomous methods, a critical concern that arises is the extent to which such analysis is fair and inclusive. In this article, we examine the biases that exist in data that are commonly used for the purpose of quantifying recreational use of urban parks. More precisely, we demonstrate the biases that exist in different sources of social media by comparing posts that are shared on Instagram and Flickr from ten urban parks in Seattle, WA. We compare the extent to which these platforms differ in terms of the information they capture about the number of people that visit the selected locations. We then demonstrate how further biases may be imposed when leveraging artificial intelligence to detect the count and demographics of park visitors, by comparing against an intercept survey of visitors.

Outdoor and nature-based recreation provides countless social benefits, yet public land managers often lack information on the spatial and temporal extent of recreation activities. Social media is a promising source of data to fill information gaps because the amount of recreational use is positively correlated with social media activity. However, despite the implication that these correlations could be employed to accurately estimate visitation, there are no known transferable models parameterized for use with multiple social media data sources. This study tackles these issues by examining the relative value of multiple sources of social media in models that estimate visitation at unmonitored sites and times across multiple destinations. Using a novel dataset of over 30,000 social media posts and 286,000 observed visits from two regions in the United States, we compare multiple competing statistical models for estimating visitation. We find social media data substantially improve visitor estimates at unmonitored sites, even when a model is parameterized with data from another region. Visitation estimates are further improved when models are parameterized with on-site counts. These findings indicate that while social media do not fully substitute for on-site data, they are a powerful component of recreation research and visitor management.

Understanding why some parks are used more regularly or intensely than others can inform ways in which urban parkland is developed and managed to meet the needs of a rapidly expanding urban population. Although geolocated social media (GSM) indicators have been used to examine park visitation rates, studies applying this approach are generally limited to flagship parks, national parks, or a small subset of urban parks. Here, we use geolocated Flickr and Twitter data to explore variation in use across New York City's 2143 diverse parks and model visitation based on spatially-explicit park characteristics and facilities, neighborhood-level accessibility features and neighborhood-level demographics. Findings indicate that social media activity in parks is positively correlated with proximity to public transportation and bike routes, as well as particular park characteristics such as water bodies, athletic facilities, and impervious surfaces, but negatively associated with green space and increased proportion of minority ethnicity and minority race in neighborhoods in which parks are located. Contrary to previous studies which describe park visitation as a form of nature-based recreation, our findings indicate that the kinds of green spaces present in many parks may not motivate visitation. From a social equity perspective, our findings may imply that parks in high-minority neighborhoods are not as accessible, do not accommodate as many visitors, and/or are of lower quality than those in low-minority neighborhoods. These implications are consistent with previous studies showing that minority populations disproportionately experience barriers to park access. In applying GSM data to questions of park access, we demonstrate a rapid, big data approach for providing information crucial for park management in a way that is less resource-intensive than field surveys.

Globally, most wildlife lives outside of protected areas, creating potential conflicts between the needs of wildlife and the needs of humans. East African savannas epitomize this challenge, providing habitat for wildlife such as giraffes and elephants as well as for people and their livestock. Conflicts over land use are common, leading to the assumption of a necessary trade-off between wildlife and livestock management. Here, we show that the integration of livestock and wildlife in a large region of central Kenya can have ecological benefits, reducing the abundance of ticks and improving forage. These ecological benefits can be complemented by economic ones when property owners derive income both from wildlife through tourism and from livestock through meat and dairy production. Our results suggest that under specific ecological, economic and social conditions, integrating livestock with wildlife can provide benefits for the environment and for human well-being in African savannas. Most wildlife lives outside protected areas, creating potential conflicts with humans. This study assesses potential trade-offs between wildlife and livestock management in an East African savanna, finding potential ecological and economic benefits from integrating the two.

Green space and parks in urban environments provide a range of ecosystem services and public benefits. However, planners and park managers can lack tools and resources to gather local information on how parks are used and what makes them desirable places for recreation and a wide variety of uses. Traditional survey methods to monitor park use and user preferences can be costly, time consuming, and challenging to apply at scale. Here, we overcome this limitation by using geotagged social media data to assess patterns of visitation to urban and peri-urban green space across park systems in the metropolitan area of the Twin Cities, Minnesota, USA. We find that parks with nearby water features, more amenities, greater accessibility from the presence of trails, and that are located within neighborhoods with higher population density, are associated with higher rates of visitation. As cities grow and shifts in demographics occur, more responsive management of public green space will become increasingly important to ensure urban parks provide ecosystem services and meet users’ needs. Using social media data to rapidly assess park use at a lower cost than traditional surveys has the potential to inform public green space management with targeted information on user behavior and values of urban residents.

Geologic mapping, a magnetotelluric survey, well data, and earlier reports are integrated to guide further development of the Fang geothermal system. The Fang Hot Springs originally flowed ~ 20 l s−1 of 90–99 °C water from a 10-hectare area of crystalline rocks presumed to be of Triassic age. Four wells 92–500 m deep now flow ~ 20 l s−1 of 110–115 °C water and generate 115–250 kWe from the 1989 Ormat binary power plant. Wells are not pumped nor is the spent water re-injected. Temperatures of 130 °C occur in some wells and water chemistry indicates reservoir temperatures of 150 °C. The springs now flow ~ 10 l s−1. The Fang geothermal area is at the west end of the active left-lateral strike-slip Mae Chan fault (MCF). MCF transitions to extensional faulting along the western boundary of the Cenozoic Fang basin. The hot waters emanate from crystalline rocks 0.7 km north of the MCF. Permeable fractures may be tensile fractures at the right-stepping fault tip. The less permeable MCF fault core and Cenozoic sediments of the Fang basin to the SW are not considered to be drilling targets. Unrelated to the fracture system is the Doi Kia detachment fault which places Paleozoic sediments over crystalline rock with a low-angle contact. Electrical resistivity surveys detect low resistivity ( 100 Ωm. Low resistivity is caused mostly by conductive minerals of hydrothermal alteration, and not by the geothermal water of resistivity 5.6 Ωm. No deep resistivity anomaly is detected beneath the seeps or producing wells, although resolution of past surveys would not have imaged narrow zones of alteration. High-resolution resistivity surveys focused on detecting the deeper fracture system are recommended over the hot well area and south over the area underlain by crystalline rocks. Future development should focus on drilling wells (≤ 500 m) with diameters large enough to install submersible pumps to increase flows. Development of several MWe may be possible and should include a designed re-injection well system to sustain pump levels.

We present the outcome of an in silico high throughput screen (HTS) and optimization of a small molecule Unc-51-Like Kinase 1 (ULK1) inhibitor hit, SR-17398, with an indazole core. Docking studies guided design efforts that led to inhibitors with increased activity vs ULK1 (IC50 < 50 nM). The most advanced molecules in this inhibitor series (3a and 3g) hold promise for further development into selective ULK1 molecular probes to interrogate the biology of ULK1 and to assess whether selectively targeting autophagy is an effective anticancer strategy.

A growing number of nations aim to design coastal plans to reduce conflicts in space and safeguard ecosystems that provide important benefits to people and economies. Critics of coastal and ocean planning point to a complicated process with many actors, objectives, and uncertain outcomes. This paper explores one such decision-making process in Belize, which combines ecosystem service modeling, stakeholder participation, and spatial planning to design the country’s first integrated coastal zone management plan, officially approved by the government in August 2016. We assessed risk to three coastal-marine habitats posed by eight human uses and quantified current and future delivery of three ecosystem services: protection from storms, catch and revenue from lobster fishing, and tourism expenditures to identify a preferred zoning scheme. We found that a highly adaptive team of planners, scientists, and analysts can overcome common planning obstacles, including a dearth of data describing the health of the coastal zone and the many uses it supports, complicated legal and political landscapes, and limited in-country technical capacity. Our work in Belize serves as an example for how to use science about the ways in which nature benefits people to effectively and transparently inform coastal and ocean planning decisions around the world.EDITED BY Joao Rodrigues

Because wildlife and livestock compete for grazing resources, biodiversity conservation and livestock ranching typically have been portrayed as conflicting uses of African savannas. Here, we offer an alternative perspective by describing a savanna ecosystem in central Kenya where wildlife and livestock exhibit a suite of potential positive interactions. For example, treating livestock with an acaricide offers the unintended benefit of removing ticks from the landscape, a result that has now been shown to occur at both large and small scales. When humans derive financial benefits both from wildlife (through tourism) and from livestock (through food production), they may achieve greater economic stability than when income is derived solely from one source. The integrated management of wildlife and livestock can simultaneously improve human health and wildlife conservation. Optimization of human and wildlife benefits will require the management of ecological and socioeconomic trade-offs when conflicts occur between stakeholders.

Land managers rely on visitation data to inform policy and management decisions. However, visitation data is often costly and burdensome to obtain, and provides a limited depth of information. In this paper, we assess the validity of using crowd-sourced, online photographs to infer information about the habits and preferences of recreational visitors by comparing empirical data from the National Park Service to photograph data from the online platform Flickr for 38 National Parks in the western United States. Using multiple regression analysis, we find that the number of photos posted monthly in a park can reliably indicate the number of visitors to a park in a given month. Through additional statistical testing we also find that the home locations of photo-takers, provided voluntarily on an online profile, accurately show the home origins of park visitors. Together, these findings validate a new method for measuring recreational visitation, opening an opportunity for land managers worldwide to track and understand visitation by augmenting current data collection methods with crowd-sourced, online data that is easy and inexpensive to obtain. In addition, it enables future research on how visitation rates change with changes in access, management or infrastructure, weather events, or ecosystem health, and facilitates valuation research, such as travel cost studies.

The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish—that is, all possible potential feeding links between co-occurring species—is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.Using a global interaction dataset, the authors quantify the distribution of trophic interactions among marine fish, finding a high degree of geographic connectivity but low spatial modularity., C.A. was supported by a MELS-FQRNT Postdoctoral Fellowship and a Ressources Aquatique Québec (RAQ) fellowship during the conception and writing of this manuscript. T.P., D.G. and D.B.S. acknowledge financial support by the CIEE through their working group programme. M.B.A. is funded through FCT project No. PTDC/AAG-MAA/3764/2014. A.R.C. is funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) PGS-D scholarship. D.G., T.P., M.-J.F., P.A. and S.J.L. are supported by NSERC Discovery Grants. T.P. also acknowledges a FRQNT New Investigator award and a Université de Montréal starting grant. D.B.S. acknowledges support from the Royal Society of New Zealand (via Marsden Fast-Start No. UOC-1101 and a Rutherford Discovery Fellowship).

Marine spatial planning (MSP) processes seek to better manage ocean spaces by balancing ecological, social and economic objectives using public and participatory processes. To meet this challenge, MSP approaches and tools have evolved globally, from local to national scales. At two International Marine Conservation Congresses (2016 and 2018), MSP practitioners and researchers from diverse geographic, technical and socio-economic contexts met to share advances in practical approaches and spatial tools to achieve multi-objective MSP. Here we share the lessons learned and commonalities that emerged from studies conducted in Belize, Canada, South Africa, Seychelles, the United Kingdom and the United States on a number of topics related to advancing MSP. We identify seven important themes that we believe are broadly relevant to any multi-objective MSP process: (1) indigenous and local knowledge should inform planning goals and objectives; (2) transparent and evidence-based approaches can reduce user conflict; (3) simple ecosystem service models and scenarios can facilitate multi-objective planning; (4) trade-off analyses can help balance diverse objectives; (5) ecosystem services may assist planning for high value-data poor Blue Economy sectors; (6) game theoretic decision rules can help to deliver fair, equitable and win–win spatial allocation solutions; and (7) strategic mapping products can facilitate decision making amongst stakeholders from different sectors. Some of these themes are evident in MSP processes that have been completed in the previous decade, but the fast-evolving field of MSP is addressing increasingly more complex objectives, and practitioners need to respond with practical approaches and spatial tools that can address this complexity.