Ecology

The world’s population is rapidly urbanizing, particularly along coastlines, where population density is now three times higher than the global average1,2. According to the National Oceanic and Atmospheric Administration (NOAA), almost 40% of the United States (U.S.) population resides in coastal zones with population density being over five times greater in coastal shoreline counties than the national average. As a result, human encroachment on coastal ecosystems is significantly modifying natural landscapes and reducing intact coastal habitat. Along densely populated coastlines, residential development often involves unsustainable land-use planning and armoring of shorelines, where natural habitats such as saltmarshes, mangroves, seagrasses and oyster reefs, are replaced with artificial structures, including vertical bulkheads, seawalls, boat ramps, and other gray infrastructures3. In areas with dense residential development between 50–90% of shorelines can be armored, whereby, on average, 14% of all U.S. shorelines have been modified from their natural conditions and replaced with artificial structures3. This transition represents an extensive loss of natural coastal habitats and the critical ecosystem services they provide.As more ecologically harmful infrastructure is developed to meet the demands of human population growth, urbanization concurrently alters ecosystem services and functions by negatively impacting biodiversity, ecological conditions and environmental quality, specifically through a decrease in native habitat, increased water pollution, and creation of impervious surfaces4. Urbanization may also lead to less resilient and adaptable coastal communities against natural hazards and climate change threats, such as sea level rise and hurricanes. This is because in urban areas, ecosystem functioning is reduced and associated services are lost, resulting in increasing risk of shoreline erosion, saltwater intrusion, storm surges, and coastal flooding2,5.These human-environment interactions in coastal ecosystems can lead to, and at the same time be derived by, decisions that will shape the future structure, function, and sustainability of coastal ecosystems6. These social decisions (e.g., large-scale policies or individual level choices) can have long-lasting consequences for both the environment and society, especially as coastal development increases. Decisions that modify and change the biophysical nature of the environment (e.g., waterfront residents’ decision to use artificial structures for storm protection and shoreline stabilization) impact its ecological functionality7. At the same time, these alterations may change the degree of connectivity that individual humans have to their environments, which might extend to broader societies’ ecological knowledge8,9.Few studies provide evidence that the removal and lack of natural environments in urbanized environments reduces individuals’ environmental connectedness and ecological knowledge, and subsequently lowers pro-environmental behaviors10,11,12. This is of critical importance since a general lack of environmental connectedness, and in particular, a lack of ecological knowledge is a phenomenon often used to explain the non-appreciation of, and deleterious behaviors toward, the natural environment, even though many studies theorize these relationships opposed to empirically test them (e.g., see refs. 13,14 and the discussion in ref. 15 about “nature-deficit disorder”). Furthermore, if there exists a general lack of ecological knowledge, social decisions at the individual level that reflect these limited perceptions (e.g., utilitarian land-use decisions12 or waterfront homeowners’ preference to install a bulkhead) can often cascade to larger societal impacts through domino-effects, where individual decisions trigger similar, reactive decisions by neighbors leading to broader societal patterns16. For example, Gittman et al.17 found that one of the stronger predictors of an individual decision to have an armored shoreline was presence of armoring on a neighboring parcel. When considered across a community scale, such societal patterns can alter natural coastal habitats significantly.In this current study, we investigate the relationship between residents’ knowledge, or mental models, of human-environment interactions, their self-reported pro-environmental behavior, and how these perceptions and behaviors are associated with urbanization. A mental model is the cognitive internal representation of a system in the external world that articulates causal relationships among system components (i.e., abstract concepts)18,19. Mental models that represent causal knowledge can be graphically obtained through cognitive mapping techniques in the form of directed graphs, which are networks in which nodes represent concepts (i.e., system components) and graph edges (arrows) represent the causal relationships between the concepts20. We combine methods from social science, data science, and network science to conduct an analysis using mental models of coastal residents along an urbanization gradient to better understand the interconnections among urbanization, people’s knowledge of human-environment interactions, and their pro-environmental behavior.We surveyed residents across eight coastal states in the northeast U.S., including Maine, New Hampshire, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, and Delaware. We used a fuzzy cognitive mapping (FCM) approach21 to elicit mental models of coastal ecosystems with a focus on environmental connectedness, ecosystem health, human wellbeing, climate, and sustainable coasts (see Methods). Here, we propose a concept, Urbanized Knowledge Syndrome (UKS), which represents recurring patterns in urban dwellers’ mental models about natural ecosystems – their internal understanding of how humans and environment interact. Here, syndrome should not be interpreted as a set of medical signs and symptoms which are associated with a particular disease or disorder. These recurring patterns include (1) diminished systems thinking (e.g., complexity of mental models decreases as degree of urban development increases) and (2) the erosion of cognitive diversity (i.e., diversity of mental models among residents decreases as degree of urban development increases). These patterns demonstrate a type of thinking that is simplified to some extent or otherwise limited or focused on fewer social-ecological relationships than exist in reality.Systems thinking – a holistic view that considers factors and interactions and how they result in a possible outcome – is an important skillset that helps people better understand complex systems and adapt to changes22. Individuals with higher degrees of systems thinking are more likely to consider interdependencies, identify leverage points to intervene within the system and produce desired outcomes23, better anticipate system function and emergence of patterns of behavior24, and avoid unintended consequences18. As such, systems thinking may help coastal residents develop mental models that enable more nuanced reasoning about diverse causal pathways between humans and natural coastal ecosystems25,26,27,28, which may lead to behaviors that are driven by more predominant cognition of complex feedbacks, trade-offs, and reciprocal interdependencies between humans and nature. In contrast, bounded systems thinking (or linear thinking) may lead humans to develop limited cognition of their surrounding world, reduce their ability to accurately and adequately perceive the complexity of the environment they inhabit and interact with22, and thus may give rise to counterproductive behaviors and decisions27,29. For example, a simple causal relationship might be that seawall construction increases coastal protection as a form of structural defense to control shoreline erosion; whereas a more complex relationship might be that seawalls lead to alterations in hydrodynamic processes, which reduces erosion locally and accelerates coastal erosion downstream30, and at the same time, shoreline armoring can also lead to losses of natural coastal habitats and their critical ecosystem functions3.While cities are beneficial to human development, working as engines of socioeconomic change, cultural transformation, and technological innovation, their psychological influences on people and how these influences drive urban residents’ perceptions and behavior must be noted. Firstly, the salience of ecosystem services is limited for inhabitants of more urbanized areas, as compared to rural areas. Exposure to nature provides multiple opportunities for cognitive development which increases the potential for stewardship of the environment and for a stronger recognition of ecosystem functions13. Urban residents, however, are more routinely exposed to built environment and gray infrastructure, such as armored shorelines and artificial structures along coastlines, as opposed to natural environment, and thus their local experience of, and connection to, ecosystem services can be limited31.Secondly, urbanization generally comes with complex technology and commerce, allowing individuals to meet their needs quickly and through many choices with less appreciation of, and first hand experience with, provisioning ecosystem services (e.g., food comes from many grocery stores not a farm or garden; fish comes over a counter not across a dock or the end of a spear; and potable water comes from a pipeline not a spring or well). This may cause the development of a wider gap in human perceptions of benefits received from natural ecosystems32, fostering the emergence of societies that are increasingly disconnected and seemingly independent from ecosystem services31.Finally, residents of urbanized areas may be exposed to a set of social norms, information, and perspectives that encourage anthropocentric values and thinking including human exemptionalism (“the tendency to see human systems as exempt from the constraints of natural environment”33) and human exceptionalism (“the tendency to see humans as biologically unique and discontinuous with the rest of the animal world”34), therefore limiting their understanding of the importance and substantiality of reciprocal interdependencies between humans and natural environment13,34. These urbanization aspects may spark what we call ‘limits to systems thinking’ in the social-ecological realm.Therefore, we hypothesize (H1) that in more urbanized areas, mental models are predominantly characterized by linear thinking of coastal ecosystems, as opposed to systems thinking, where components are connected mostly by simple causal patterns. This class of mental models is associated with limited cognition of synergies and trade-offs, emergence of global patterns from local relationships, reciprocal interdependencies, and feedback loops between humans and natural ecosystems, which may lead to a gap in residents’ perception of nonlinear complex structures. To test our hypothesis, we analyze the structure of causal relationships using the network structure and graph-theoretic metrics of cognitive maps (i.e., graphical representations of mental models). We use cluster analysis to identify predominant classes of mental models about coastal ecosystems. Distinct clusters of mental models represent archetypal cognitions that individuals develop to perceive human-environment interdependencies13,27. We then use network analyses to measure the complexity of causal structures in cognitive maps and determine the overall degree of systems thinking in each cluster (see Methods). Finally, we investigate the association between urbanization and the degree of systems thinking across those clusters.The second important feature that helps systems adapt to changes is diversity, ranging from ecosystems35 to economic systems36. There is also evidence that these same relationships between diversity and adaptability hold true for cultural knowledge systems, governance systems, and among diverse communities and social institutions that function more effectively as resilient collectives28,37,38.In contrast, as cultural homogenization theories explain, survival in cities depends on fitting in and adopting practices that are considered socially normal by the dominant culture39. Although cities are magnets for people from all corners of the world with seemingly more diverse composition of race and ethnicity compared to rural areas40, assimilation of diverse values, beliefs, cultural knowledge, and social norms into a universal, governing culture—sometimes referred to as “cultural colonialism” or “cultural normalization” – is a major component of urban societies41. This cultural normalization among urban dwellers is exacerbated by dominant exposure to the universal language and education system, greater access to the Internet, social media and news outlets, and market-driven policies and global standardizations for laws and finance41.In addition, an important characteristic of urbanization is the centralization of the population into cities, “where neighborhoods in different regions have similar patterns of roads, residential lots, commercial areas, and aquatic features”42. Such physical and environmental homogenization across urban areas, which is visually evident, is influenced by monocentric land-management and policies, economic pressures for land development and use, engineering necessities, codes and standards, and preferences for particular aesthetics and recreations. Prior studies have shown that this homogenization extends to ecological structure, meaning that across urbanized areas, similar built environment and landscape structures can lead to homogenized ecological characteristics, function, and the range of ecosystem services they can supply42,43.Here, we argue that homogenization in cultural, physical, and ecological systems also extends to residents’ perceptions and understanding of human-environment interactions. We, therefore, hypothesize (H2) that increased urbanization is associated with more homogenized mental models of coastal ecosystems. To test our second hypothesis, we measure the structural dissimilarity of individuals’ mental models (i.e., cognitive maps) using some of the widely used methods for comparing graphs44. We measure the mean of pairwise cognitive distances (i.e., a quantitative metric that represents the mean of graph dissimilarity between any two individual cognitive maps) and compare this metric across clusters of mental models, and thus, explore the correspondence between urbanization and mental model homogenization (i.e., testing the hypothesis that urbanization is associated with more similar mental models in terms of causal structures represented in cognitive maps) (see Methods). More

Farmers at a Great Green Wall site in Niger. Researchers have found that the project is not always benefiting the most vulnerable people.Credit: Boureima Hama/AFP/Getty

It’s now 15 years since the African Union gave its blessing to Africa’s Great Green Wall, one of the world’s most ambitious ecological-restoration schemes. The project is intended to combat desertification across the width of Africa, and spans some 8,000 kilometres, from Senegal to Djibouti. Its ambition is staggering: it aims to restore 100 million hectares of degraded land by 2030, capturing 250 million tonnes of carbon dioxide and creating 10 million jobs in the process. But it continues to struggle.An assessment two years ago by independent experts commissioned by the United Nations stated that somewhere between 4% and 20% of the restoration target had been achieved (go.nature.com/39zqgkr). That figure has not changed, according to the latest edition of Global Land Outlook (go.nature.com/3kdjtw5) from the UN Convention to Combat Desertification (UNCCD), out last week. Equally concerning is the fact that funding for the project continues to lag. Africa’s governments and international donors need to find around US$30 billion to reach the 100-million-hectare target. So far, $19 billion has been raised.A pandemic — and now a cost-of-living crisis — has placed demands on all governments, and that means countries might be expected to reduce their green-wall commitments. But the project continues to be weighed down by other difficulties, including the complex system through which it is funded and governed, as well as how its success is measured. These problems can and must be fixed, otherwise it will struggle to achieve its goals.One potential solution — improved metrics — comes from an analysis published last year by Matthew Turner at the University of Wisconsin–Madison and his colleagues (M. D. Turner et al. Land Use Policy 111, 105750; 2021). The researchers explored limitations in the Great Green Wall project metrics by assessing the impact of World Bank funding from 2006 to 2020. As their work indicates, definitions of success depend on which measure is used.In Niger, for example, green-wall projects could be said to be succeeding if measured by the area of eroded soil that has been recovered or by the number of trees that have been planted. But the authors report that these gains were not necessarily benefiting the most vulnerable people. In places, women were being excluded from employment in green-wall projects, and in some cases, local administrations looked to privatize restored land that might instead have been owned by everyone in a community.Broader problems with metrics are highlighted in the UN’s latest land-degradation report. This estimates that nearly half of the land that has been pledged for restoration worldwide will be planted predominantly with fast-growing trees and plants. This will provide only a fraction of the ecosystem services produced by forests that are allowed to naturally regenerate, including significantly less carbon storage, groundwater recharge and wildlife habitat.The Great Green Wall project also needs more predictable funding and more transparent governance. The project was conceived by Africa’s leaders for the benefit of the continent’s people, on the basis of warnings from scientists about the risks of desertification and land degradation. The original idea was not brought to Africa by international donors, as is often the case in international science-based development projects. But it still relies on donor financing, and lots of it — and that brings other problems, among them coordination challenges.The project is the responsibility of an organization set up by the African Union called the Pan African Agency of the Great Green Wall, based in Nouakchott, Mauritania. But some donors, such as the European Union and the World Bank, are not providing most of their Great Green Wall funding through this agency. Instead, they often deal directly with individual governments, because this gives them more control over how their money is spent. It is unfair to expect the Pan African Agency to coordinate a raft of donors doing one-on-one deals with individual countries. Bypassing the Pan African Agency also creates a problem for transparency, because it makes it harder for the African Union to determine precisely who is funding what.In January 2021, at an international biodiversity summit hosted by France, Emmanuel Macron, the French president, announced that the Great Green Wall would receive an extra $14 billion in funding for 5 years. He also said that a new body, called the Great Green Wall Accelerator, based in Bonn, Germany, would be responsible for pulling together funding pledges and tracking progress against targets. This is well-intentioned, but the accelerator needs to coordinate its work with the Pan African Agency. It is not yet clear how this will happen.A potentially more transformative solution was proposed two years ago by a group of UN-appointed experts. They recommended that a single trust fund be set up that all donors could contribute to and through which they could decide funding priorities together. Regrettably, this has not happened, and observers say it is not likely to happen in the current climate.This month, the international community will come together in Abidjan, Côte d’Ivoire, for the 15th conference of the parties to the UNCCD. The green wall’s funders and participating countries will all be there. If a single trust fund is off the table, they must work together to find a better way to coordinate their green-wall project activities. It is also essential that they study the findings of Turner and colleagues’ review. Along with a focus on existing metrics, the Great Green Wall needs evaluation criteria that take better account of the needs of all people in participating countries, particularly the most vulnerable. More

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Study siteThe study area covers the campus of Suranaree University of Technology (SUT) and its surrounding landscape in Muang, Nakhon Ratchasima, Thailand (14.879° N, 102.018° E; Fig. 1). The university campus covers about 11.2 km2, and comprises a matrix of human modified lands interspersed with mixed deciduous forest fragments (at the onset of this study we identified there were 37 mixed deciduous forest fragments on campus, mean = 7.36 ± 1.48 ha, range = 0.45–45.6 ha [note, “±” is used for standard error throughout the text]). More than 15,000 students are enrolled at SUT, and there are numerous multi-story classrooms, laboratory and workshop buildings, residential housing, parking areas, eating and sports facilities, an elementary school, and a large hospital on the university campus. During the first term of the 2019 school year, 7622 students, as well as numerous SUT staff, lived in on-campus residential areas. The landscape surrounding the university is primarily dominated by agriculture, though there are also patches of less-disturbed areas as well as several densely populated villages and suburban housing divisions among the monoculture plots of upland crops (e.g., cassava, maize, and eucalyptus).Figure 1Study site map illustrating the land-use types spanning the area where the Malayan kraits (Bungarus candidus) were tracked in Muang Nakhon Ratchasima, Nakhon Ratchasima province, Thailand. Map created using QGIS v.3.8.2 (https://qgis.org/) in combination with Inkscape v.1.1.0 (https://inkscape.org/).Full size imageThe study site is located within the Korat Plateau region with an altitude range of 205–285 m above sea level. Northeast Thailand has a tropical climate, and the average daily temperature from 1 January 2018 to 31 December 2020 in Muang Nakhon Ratchasima was 28.29 °C, with daily averages ranging from 19.3 to 34.1 °C38. The region receives an average annual rainfall ranging from 1270 to 2000 mm39. There are three distinct seasons in northeast Thailand: cold, wet, and hot, each are classified by annual changes in temperature and rainfall. Cold season is typically between mid-October and mid-February, hot season is generally from mid-February to May, while the highly unpredictable rainfall of the wet season is predominantly concentrated between the months May to October39,40.Due to the representation of agriculture, semi-urban, and suburban areas with patches of more natural areas all within a relatively small area, we determined the university campus provided an ideal setting to examine how land-use features and human activity influence the movements of B. candidus. Additionally, past studies have indicated northeast Thailand hosts the most bites by B. candidus in Thailand29,33, making sites like ours ideal.Study animalsWe opportunistically sampled Malayan kraits captured as a result of notifications from locals and ad-hoc encounters during transit due to low detectability in visual encounter surveys, in addition to those discovered through unstandardized visual encounter surveys. Upon capture, we collected morphometric data, including snout-vent length (SVL), tail length (TL), mass, and sex (Table 1, Supp. Table 1). We measured body lengths with a tape measure, measured body mass with a digital scale, and determined sex via cloacal probing, all while the snakes were anesthetized via inhaling vaporized isoflurane. We then housed individuals with an SVL > 645 mm and mass > 50 g in plastic boxes (with refugia and water) prior to surgical transmitter implantation by a veterinarian from the Nakhon Ratchasima Zoo. We attempted to minimize the time snakes were in captivity awaiting implantation; however, delays arose due to the veterinarian’s availability, the snake being mid-ecdysis, or the snake having a bolus that needed to pass through the digestive tract before implantation (n = 21 implantations, mean = 5.02 ± 0.61 days, range = 0.60–13.02 days). The Nakhon Ratchasima Zoo veterinarian implanted radio transmitters (1.8 g BD-2 or 3.6 g SB-2 Holohil Inc, Carp, Canada) into the coelomic cavity using procedures described by Reinert and Cundall41, while the snake was anesthetized. We assigned each individual an ID according to sex and individual detection number (e.g., M02 = a male was the second B. candidus individual documented during the study). We released the implanted individuals as close as possible to their capture locations (mean = 65.31 m ± 13.7 m, range = 0–226.42 m), though on six occasions we moved individuals ≥ 100 m because the individual came from either residential areas or a busy road (all but one were moved  800 mm; thus, nine of the males were adults and four were juveniles (though two of the males had an SVL > 720 mm, and therefore likely sub-adults). The single telemetered female was an adult.Individual tracking durations varied (mean = 106.46 ± 15.36 days, range = 28.5–222.77 days; Supp. Fig. 1), as many individuals were lost due to unexpected premature transmitter failures (n = 5) or unsuccessful recapture efforts due to individuals sheltering under large buildings as the transmitter reached the end of its battery life (n = 4). We only recorded one confirmed mortality in the study, M01, who was killed by a motorized vehicle when crossing a road (n = 1). Another three individuals were lost due to unknown reasons, which may have been due to premature transmitter failure, mortality, or the animal moving beyond radio signal despite extensive search efforts. Thus, we only successfully recaptured and re-implanted five individuals (M01 once, M02 twice, M07 once, M27 once, and M33 twice). Transmitter batteries generally lasted approximately 90–110 days, so we aimed to replace transmitters after ≥ 90 days of use. At the end of the study, only one individual was successfully recaptured to remove the transmitter.Data collectionWe used very high frequency radio-telemetry to locate each telemetered individual on average every 24.20 h (SE ± 0.41, 0.17–410.0 h; see Supp. Fig. 2 for distribution of tracking time lags). We aimed to locate each individual’s shelter locations once each day during the daylight (06:00–18:00 h); however, we were occasionally (n = 34 days) unable to locate a snake for several consecutive days when we were unable to obtain radio signal due to an individual having moved far away or deep underneath a large structure. There were also a few occasions where we were unable to track snakes due to prolonged and heavy rainfall (n = 4 days), as the moisture damages equipment, or other reasons (n = 4 days). We additionally located snakes nocturnally (18:00–06:00 h) ad hoc and in an attempt to observe nocturnal behaviors and movement pathways when animals were active. We defined fixes as any time a telemetered individual was located, and relocations (i.e., moves) as the occasions where we located an individual > 5 m from its previous known location.Each day we manually honed in on signal via a radio receiver to locate individuals (as described by Amelon et al.42, and recorded locations in Universal Transverse Mercator (UTM; 47 N World Geodetic System 84) coordinate reference system with a handheld global positioning system (GPS) unit (Garmin 64S GPS, Garmin International, Inc., Olathe, Kansas) directly above the sheltered snake. We generally approached within one meter of sheltering snakes during daylight to precisely record shelter locations and identify shelter type. Since we could not visually confirm snake locations, we methodically eliminated all possible locations where the snake could possibly be while at close range with the minimum possible gain on the radio receiver.Telemetered kraits tended to be inactive and sheltering underground during the daylight, thus we were confident that our diurnal location checks would not affect their movements. However, in some cases we resorted to determining an individual’s location via triangulation, where multiple lines cast from different vantage points towards the snake intersect on the snake’s location on the GPS, allowing us to determine the animal’s coordinate location from approximately 10–30 m away. This helped ensure that we recorded locations with greater accuracy when snakes sheltered underneath large buildings, as it allowed us to move away from large structures that hindered the GPS accuracy. This technique was also implemented during some nocturnal location checks when a snake was believed to be active among dense vegetation, in an attempt to prevent disturbance of the animals’ natural behavior. While we did hope to gain visual observations of active individuals during the night, we exercised more caution during nocturnal location checks, typically maintaining a minimum distance of approximately 5 m in attempt to lessen the chances of disturbing an active individual’s behavior. If the animal was active we recorded the animal’s observed behavioral state (i.e., moving, feeding, or foraging). When the radio signal was stable and the individual was not visible, we recorded the animal’s behavior as “sheltering”. We strived for an accuracy of  5 m difference), and land-use type (e.g., mixed deciduous forest, human-settlement, semi-natural area, agriculture, plantation; see Supp. Figs. 3 and 4 for photos of land-use types), behavior (e.g., sheltering, moving, foraging, or feeding), and shelter type (e.g., anthropogenic, burrow, or unknown, note we also recorded if we suspected the shelter to be part of a termite tunnel complex due to a close proximity to a visible termite mound; Supp. Fig. 5).During each location check we recorded the straight-line distance between the current and previous locations (distance moved/step length) with the GPS device. We then used step-lengths to summarize their movements by estimating the mean daily displacement (MDD; the total distance moved divided by the number of days the snake was located) and mean movement distance (MMD; the mean relocation distance, excludes distances ≤ 5). In order to limit biases due to some snakes being located multiple times within a given day/night, we limited our sample for estimating MMD and MDD to only include a single location per day. This was accomplished by manually removing “extra” nocturnal location checks that occurred within the same day, making sure to have all shelter relocations present within the dataset. When calculating MDD, we used the total number of daily location checks rather than the number of days between the individual’s tracking start and stop date since there were some days where individuals were not tracked. We also used the same one location check per day dataset to calculate movement/relocation probabilities and to examine each individual’s MMD, MDD, and relocation probability for the overall tracking duration as well as for each season.When feasible, we positioned a Bushnell (Bushnell Corporation, Overland Park, Kansas) time lapse field camera (Trophy Cam HD Essential E3, Model:119837) with infrared night capability on a tripod spaced 2–5 m from occupied shelter sites. We positioned the cameras so that we may gather photos of the focal snake as it exited the shelter site and/or behaviors exhibited near the shelter. We programmed the cameras using a combined setting, including field scan, which continuously captured one photo every minute, along with a motion sensor setting, which took photos upon movement trigger outside of the regular 1-min intervals.Space use and site fidelityAll analyses and most visualizations were done in R v.4.0.5 using RStudio v.1.4.1106 43,44. We attempted to estimate home ranges for the telemetered B. candidus individuals using autocorrelated kernel density estimates (AKDEs) using R package ctmm v.0.6.045,46 in order to better understand the spatial requirements of B. candidus. However, examination of the variograms revealed that the majority of the variograms had not fully stabilized (i.e., limited evidence of range stability in our sample), and many individuals had extremely low effective sample sizes (21.82 ± 9.75, range = 1.49–135.75; Supp. Table 4). Therefore, we do not report home ranges in this text, as the AKDE estimates would violate the assumption of range residency and either underestimate or misrepresent B. candidus spatial requirements. We also examined the speed estimates resulting from fitted movement models. Resulting variograms and tentative home range estimates are included in a supplementary file for viewing only (Supp. Fig. 6, Supp. Table 4). The original code is from Montaño et al.47.Since our data was not sufficient to estimate home range size for the telemetered B. candidus, we instead used Dynamic Brownian Bridge Movement Models (dBBMMs) with the R package move v.4.0.648 to estimate within study occurrence distributions. We caution readers that these are not home range estimates but instead modeling the potential movement pathways animals could have traversed49. Use of dBBMMs not only allows us to estimate occurrence distributions for each individual, thus helping us better understand the animal’s movement pathways and resource use, but it also allows us to examine movement patterns through dBBMM derived motion variance50,51. We selected a window size of 19 and margin size of 5, to catch short resting periods with the margin, while the window size of 19 is long enough to get a valid estimate of motion variance when the animals exhibit activity/movement. Contours however are somewhat arbitrary; therefore, we used three different contours levels (90%, 95%, 99%) to estimate dBBMM occurrence distributions (using R packages adehabitatHR v.0.4.19, and rgeos v.0.5.5), and show the sensitivity to contour choice52,53.All movement data, either including initial capture locations or beginning with the first location check ~ 24 h post release, was used for production of both the AKDEs and dBBMMs for each individual. We also estimated dBBMM occurrence distributions for each telemetered individual with the exception of M29, which only made three small moves within a burrow complex during the short time he was radio-tracked before transmitter failure.We compared space use estimates to two previously published B. candidus tracking datasets34,36, and one unpublished dataset shared on the Zenodo data repository54, all originating from the Sakaerat Biosphere Reserve (approximately 41 km to the south of our study site): two adult males from within the forested area of the reserve [one tracked every 27.8 ± 0.99 h over a period of 103 days, the other tracked every 38.63 ± 11.2 h over a period of 30.58 days]34,54, and a juvenile male from agriculture on a forest boundary [tracked every 50.19 ± h for 66.91 days]36. The previous studies on B. candidus only tracked the movements of a single individual each, had coarser tracking regimes, and used traditional—fundamentally flawed methods55,56—to estimate space use34,36. Therefore, we ran dBBMMs with these previous datasets using the same window (19) and margin size (5).To quantify site reuse and time spent at sites (residency time) we used recursive analysis with the R package recurse v.1.1.257. We defined each site as a circular area with a radius of 5 m around each unique location (matching the targeted GPS accuracy). Then we calculated each individual’s overall number of relocations, each individual’s total number of relocations to each site, and each individual’s site revisit frequency and residency time at each unique site. Then we plotted revisited locations on a land-use map with space use estimates (95% and 99% dBBMM) in an attempt to help identify and highlight activity centers for telemetered individuals (see Supp. Figs. 7–13). All maps were created using Quantum Geographic Information System (QGIS v.3.8.2).Habitat selectionWe used Integrated Step Selection Function models (ISSF) to examine the influence of land-use features on the movements of B. candidus at both the individual and population levels. We included movement data from all male individuals that used more than one habitat feature in our ISSF analysis. Therefore, we excluded F16 and M29 who both only used settlement habitat. Excluding M29 was justified by the individual having been tracked for the shortest duration (19 days) and had the fewest number of moves (n = 3), thus there were not enough relocations for ISSF models to work effectively. Using modified code from Smith et al.51 that used ISSF with Burmese python radio-telemetry data, we used the package amt v.0.1.458 to run ISSF for each individual, with Euclidean distance to particular land-use features (natural areas, agriculture, settlement, buildings, and roads) to determine association or avoidance of features. Cameron Hodges created all land-use shape files in QGIS by digitizing features from satellite imagery and verified all questionable satellite land-use types via on-ground investigation.The semi-natural areas, plantations, mixed deciduous forest and water bodies (such as irrigation canals and ponds which have densely vegetated edges) were all combined into a single layer of less-disturbed habitats which we refer to as “natural areas”. All feature raster layers were then converted into layers with a gradient of continuous values of Euclidean distances to the land-use features, and were inverted in order to avoid zero-inflation of distance to feature values and to make the resulting model directional effects easier to more intuitive. We were able to generate 200 random steps per each observed step (following Smith et al.51), due to the coarse temporal resolution of manually collected radio-telemetry data (i.e., we were not computational limited when deciding the number of random locations). Higher numbers of random steps are preferable as they can aid in detecting smaller effects and rarer landscape features59.To investigate individual selection, we created nine different models testing for association to habitat features, with one being a null model which solely incorporated step-length and turning angle to predict movement60, five examining land-use features individually (agriculture, buildings, settlement, natural areas, roads), and the other three being multi-factor models. Each model considers distance to a land-use variable, step-length, and turn-angle as an aspect of the model. After running each of the nine models for each individual, we then examined the AIC for each model, point estimates (with lower and upper confidence intervals), and p-values in order to identify the best models for each individual and determine the strongest relationships and trends among the samples. We considered models with ∆ AIC  More

In this picture, taken in February at Copenhagen Zoo, I’m holding a vacuum device equipped with a tiny fan and filter. The devices — we call them air samplers — are designed to collect DNA samples from the air. We deployed three samplers at the zoo: one in a stable with two okapi (Okapia johnstoni) and two duikers, one in a rainforest house and one outside, near an exhibit of animals that live in the African savannah.At best, we had hoped to detect nearby animals in small enclosures — an okapi in the stable, for instance. But as we reported in Current Biology, the devices outperformed our expectations (C. Lynggaard Curr. Biol. 32, 701–707; 2022). They picked up identifiable DNA from 49 vertebrates, including guppies in the rainforest pool, ostriches and giraffes in the savannah area, and even cats and dogs in the park next door. Interestingly, we didn’t get any signal from turtles in the rainforest house. Maybe turtles mostly keep their DNA to themselves.Our analysis ultimately found that the sampler could detect animals from nearly 200 metres away. The giraffe in the picture is standing much closer than is necessary for collection of a sample.Airborne DNA is all around us. Birds release skin cells when they flap their wings. Saliva from all sorts of animals can become airborne. Animals release DNA when they defecate. In November 2021, I received a grant to start a research group whose goal is to collect airborne DNA in nature. This approach could transform conservation biology and species monitoring. We could detect rare animals and get a better understanding of diversity without disturbing an environment.We have so many lines of inquiry in this work. The location of the samplers, the rates of air flow, the time, the best methods for sorting DNA from the sample — we’re still trying to work all of these out. We hadn’t expected that the zoo experiment would ever work, so we’re scrambling to plan the next steps. It’s an exciting time. More

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Fossil dataWe downloaded all fossil occurrences recorded for the order Scleractinia at the species level from the Paleobiology Database (PBDB – paleobiodb.org; accessed on 3 August 2021). This is the most comprehensive repository for palaeontological data in reef corals to date. Due to the nature of the data, no ethics approval was required. To minimize identification issues, we excluded taxa with uncertain generic and species assignments (i.e., classified as aff. and cf.) and only selected species that had accepted names. We also selected the variables classification and palaeoenvironment from the output options to facilitate taxonomic and environmental filters applied in downstream analyses. The full dataset consisted of 24,011 occurrences across 4235 species, spanning over 250 Myr of coral evolution from the Triassic to the present. Although our focus here lies on the Cenozoic, we used the complete fossil dataset (i.e., including all of the occurrences) to have estimates of the diversification dynamics in scleractinian corals throughout the whole timespan of their evolution.Evolutionary ratesWith the full palaeontological dataset, we estimated evolutionary rates through time in scleractinian corals using the Bayesian framework of the program PyRate (v3.0)12,36,37. This program uses fossil occurrence data to calculate the temporal variation in rates of preservation, speciation and extinction, while incorporating multiple sources of uncertainty12. At its core implementation, PyRate jointly estimates the times of origination (Ts) and extinction (Te) for each fossil lineage; the fossilization and sampling parameters that determine preservation rates (q); and the overall rates of speciation (λ) and extinction (μ) through time36. Recently, the program has been upgraded to include a reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to estimate diversification rate heterogeneity, which provides more accurate and precise estimates than other commonly used methods12. Therefore, despite the inherent bias of the fossil record (i.e., estimates are conditioned on sampled lineages), PyRate is a robust method to quantify speciation and extinction rates, and their respective temporal shifts, from fossil occurrence data.Extant taxa can also be included in the PyRate framework as long as they are also represented in the fossil record. This is done to extend the fossil geologic ranges to the recent times. Hence, the first step in our analysis was to identify which species in our dataset is still alive at the present. To do this, we matched the accepted species names in the PBDB dataset with those from the extant species dataset of Huang et al.38. Subsequently, we split our dataset into eleven independent subsets, with the goal of keeping each subset with an equal number of species. Each data subset included a random selection of species with their respective occurrences, which was enough to calculate Ts and Te (see below). This was done to avoid convergence issues, given the large size of our dataset and the consequent complexity of the model37. For each of our subsets, we generated fifty replicates by resampling the fossil ages from their temporal ranges to account for the uncertainty associated with the age of occurrences. We then used the maximum-likelihood test in PyRate to compare between three models of fossil preservation12: the homogeneous Poisson process (HPP; q is constant through time); the nonhomogeneous Poisson process (NHPP; q varies throughout the lifespan of a species); and the time-variable Poisson process (TPP; q varies across geological epochs). The latter model (TPP) was selected across all of our data subsets (Supplementary Table 2).After selecting the preservation model, we first focused on assessing the estimates of times of origination and extinction in each data subset, rather than using the full dataset to jointly estimate all parameters at once as in the original implementation of PyRate. This further reduced the complexity of the model and allowed for more precise parameter estimates. For each replicate in all of our data subsets, we approximated the posterior distribution of Ts and Te through a 50 million generation run of the rjMCMC algorithm under the TPP, sampling parameters every 40 thousand iterations. At the end of each run, we discarded 20% of the samples as burn-in and assessed chain convergence through the effective sample sizes of posterior parameter estimates, using the software Tracer39 (v1.7.1).From the results of this first set of models, we extracted the median estimates of Ts and Te across replicates, and we merged the estimates from the eleven independent data subsets. This merged data frame contained estimated times of origination and extinction for all coral lineages within our fossil dataset. We then used this merged Ts and Te data frame as input for another rjMCMC chain to finally estimate overall λ and μ through time, by applying the option -d in PyRate. In this option, Ts and Te for all fossil lineages are given as fixed values and, therefore, are not estimated by the model. The chain for this model was run for 100 million generations, sampling parameters at every 40 thousand iterations. Once again, we excluded 20% of the initial samples as burn-in and checked model convergence using Tracer. Finally, we calculated net diversification rates through time by subtracting the post burn-in samples of μ from λ.To explore the taxonomic idiosyncrasies in the evolutionary rates of reef corals, we selected the most abundant families on present-day coral reefs in terms of the number of colonies per area18 (Acroporidae, Agariciidae, Merulinidae, Mussidae, Pocilloporidae, and Poritidae). Altogether, species within these families account for ~40% of the total extant diversity in Scleractinia. These families also account for most of the occurrences in the PBDB fossil dataset (excluding extinct families, which are generally older and had little temporal overlap with extant ones): Acroporidae (1457 occ. in 165 spp.); Agariciidae (722 occ. in 89 spp.); Merulinidae (2464 occ. in 229 spp.); Mussidae (1146 occ. in 100 spp.); Pocilloporidae (615 occ. in 64 spp.); and Poritidae (1149 occ. in 91 spp.). Therefore, from our full dataset, we selected six independent ones encompassing all species in each of the selected families. We also selected only species that are classified as reef-associated within these families, since we were specifically interested in these environments. This selection had a negligible effect on the size of the individual datasets, given that the vast majority of fossil species within these families are reef-associated. In each family, we followed the same modelling steps described above to estimate μ and λ, and diversity trajectories. However, this time it was not necessary to split the datasets into subsets, given that each family has far less occurrences than the full dataset. We started by comparing models of preservation, which showed the TPP as the best supported for all families (Supplementary Table 3). Then we created fifty replicates by resampling fossil ages to accommodate the uncertainty associated with the time of occurrences. For each replicate, we ran the rjMCMC algorithm for 50 million generations under the TPP model, with a sampling frequency of 40 thousand iterations. We discarded initial 20% of the samples as burn-in, and assessed convergence through Tracer. We then combined all replicates, resampling 100 random samples from each replicate to assess the estimates of μ and λ through time for each family. Finally, we extracted diversity trajectories in each family for all of the replicates by applying the -ltt option in PyRate, which generates a table with estimated range-through diversity at every 0.1 Myr. From these trajectories, we calculated the mean difference in diversity (slope in species per 0.1 Myr) between subsequent time samples backwards from the present (i.e., diversity in time t was subtracted from diversity in time t-1) using the diff function in R (v4.0.3).As an alternative to PyRate, we also calculated the diversity dynamics of reef coral fossils using the R package divDyn40, which combines a range of published methods for quantifying fossil diversification rates. Differently from PyRate, the metrics applied in divDyn require that the fossil occurrences are split into discrete time bins. Therefore, these metrics treat the origination and extinction rates as independent parameters in each bin, while PyRate is designed to detect rate heterogeneity through a continuous time setting12. Our goal here, however, was not to compare models but to assess the robustness of our rate patterns and diversity trajectories using alternative methods. We divided our dataset into one-million-year time bins to have enough temporal resolution for rate calculations. To account for the uncertainty in the assignment of fossil ages, we created 50 binned replicates by sampling the age of each occurrence from a random uniform distribution, with bounds defined by the age ranges provided in the PBDB dataset. We then used the divDyn function to calculate the per capita rates of origination and extinction through time (based on the rate equations by Foote41) for all scleractinians (Supplementary Fig. 5a) and for reef-associated acroporids alone (Supplementary Fig. 5b). We also used the same procedure to generate range-through diversity curves for each of the six families selected previously, to compare with the curves generated by PyRate (Fig. 2a). Although the rate results differed between the PyRate (Fig. 1) and the divDyn (Supplementary Fig. 5) approaches, the general patterns remained unchanged. Rates are more volatile through time in divDyn estimates, with larger confidence intervals, which is expected from the metrics applied in the package12,42. Yet, we found the same peaks in extinction for Scleractinia: at the Cretaceous-Paleogene and Eocene-Oligocene boundaries, and at the Pliocene-Pleistocene (Supplementary Fig. 5a). The recent peak in speciation in Acroporidae was also detected, although less strong (Supplementary Fig. 5b). Despite these slight differences in rate estimates, the diversity curves reconstructed through divDyn (Supplementary Fig. 6) mirrored almost exactly the ones found with PyRate (Fig. 2), demonstrating that the overall macroevolutionary trends described herein (Figs. 1 and 2) are robust to methodological choices.Diversity-dependent modelsTo assess the effects of diversity dependency on the evolution of reef coral lineages, we implemented the Multivariate Birth-Death model (MBD)11 within the PyRate framework. This method was first described as the Multiple Clade Diversity Dependence model (MCDD)19, in which rates of speciation and extinction are modelled as having linear correlations with the diversity trajectories of other clades. At its original implementation, the MCDD was developed to assess the effects of negative interactions, where increasing species diversity in one group can suppress speciation rates and/or promote extinction in itself or in other ecologically similar clades19. However, the model also incorporates the possibility of positive interactions, where increasing diversity in one clade can correlate with enhanced rates of speciation or buffered extinction. Through further model developments43, the MCDD was updated to also include a horseshoe prior44 on the diversity-dependence parameters, which helped controlling for overparameterization and enhanced the power of the model to recover true effects43. More recently, this model took its current form as the MBD11, with the additional possibilities of including environmental correlates and setting exponential, rather than just linear, correlations.We first applied the MBD to estimate the diversity-dependent effects of individual extant coral families (i.e., the ones selected in the previous analysis; see Evolutionary rates) in their combined diversity trajectories. From the rjMCMC model results for individual families, we extracted estimates of Ts and Te in each of the fifty replicates and merged them across families. This merged dataset with fifty replicates of Ts and Te was then used as input for the MBD model, where we set the relative diversity trajectories of each individual family as predictors. We also included three key environmental predictors—paleotemperature, sea level and rate of sea-level change—to assess their influence in overall evolutionary rates. The paleotemperature data was obtained from Westerhold et al.45, and consists of global mean temperature estimates for the last 66 million years, averaged across 0.1 Myr time bins. Eustatic sea-level data was downloaded from Miller et al.46, and contains estimates of sea level for the last 100 million years in comparison to present-day levels, also split in ~0.1 Myr time bins. With this dataset, we calculated the average rate of sea-level change per million years, as measured from the absolute difference between subsequent sea-level values backwards in time (i.e., sea level in time t was subtracted from sea level in time t-1). These environmental factors were rescaled between 0 and 1 to maintain all predictors on the same relative scale.Under our MBD model, the speciation and extinction rates of all families combined could change through time and through correlations with the relative diversity of individual families or environmental factors. The strength and directionality (positive or negative) of the correlations are also jointly estimated for each predictor within the model11. We ran both linear and exponential correlation models (see formulas in Lehtonen et al.11) in each of our fifty replicates for 25 million generations, sampling parameters at every 25 thousand iterations. We then compared the linear and exponential models through the posterior harmonic means of their log likelihoods, which supported the exponential one as having a better fit. From the posterior estimates, we summarized the speciation (Fig. 3c) and extinction (Fig. 3d) correlation parameters (i.e., the strength of the effect) by calculating their median and 95% Highest Posterior Density (HPD) interval across replicates. Finally, we also summarized the effect of families on lineage turnover (Fig. 3e), which we conceptualize as the sum of the effects on speciation and extinction.The MBD model also provides posterior samples of the weight of the correlation parameters, which is estimated through the horseshoe prior11. In essence, this prior is able to reliably distinguish correlation parameters that should be considered noise from those that represent a true signal in the data11. The parameterization of the horseshoe prior contains local and global Bayesian shrinkage parameters44 from which shrinkage weights (w) can be calculated (see formulas in Lehtonen et al.11). These shrinkage weights associated with each correlation parameter in the MBD model vary between 0 and 1, with values closer to 0 representing noise and values closer to 1 representing a true signal. Through simulations, it has been shown that values of w  > 0.5 indicate that the correlation parameter in question significantly differs from the background noise, being the correlation positive or negative43. However, as a conservative way to infer the weight of correlation parameters, here we use a value of w  > 0.7 to detect significance. This value was calculated for each diversity-dependence parameter (speciation, extinction and turnover) from the median values drawn from the model posteriors.The spatial distribution of reef-associated taxa varied considerably throughout the Cenozoic, with biodiversity hotspots moving halfway across the globe47. Therefore, the best way to capture this dynamic biogeographic history in reef corals is by analysing global diversity patterns like we did in our main MBD model. However, to assess the robustness of our diversity-dependent results against the influence of geographic scale and site co-occurrences, we repeated all the modelling steps described above with two data subsets. First, we selected only fossil species that have occurrences in the Indo-Pacific Ocean (i.e., 30°W–180°W) within the six families. Second, we excluded sites in which the Acroporidae did not co-occur with the other families. In each of these data subsets, we calculated diversity trajectories and used them as predictors in a separate MBD model. These models had a merged dataset of Ts and Te of all species included in each case (Indo-Pacific and co-occurrences) as a response variable.Finally, we followed the same modelling procedures described above to investigate the diversity-dependent effects in family pairwise analyses. We applied the MBD model to assess the effects of all other families in each individual family at a time, while also estimating correlations with the key environmental predictors. From the rjMCMC model results for individual families, we extracted the fifty replicates of estimated Ts and Te. Each replicate was then used as input for an MBD run using the relative diversity trajectories of each other individual family as predictors, along with the environmental variables. Once again, we ran 25 million generations of the MBD, with a sampling frequency of 25 thousand, using both linear and exponential correlation models in each age replicate. For all families, we found that the exponential model had a better fit. We then summarized the correlation parameters and the shrinkage weights (Supplementary Fig. 7) derived from the exponential models per family by calculating the median and 95% confidence intervals across replicates.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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