Ecology

Callie Veelenturf measured the pH, conductivity and temperature near a leatherback sea turtle’s nest during research in Equatorial Guinea.Credit: Jonah Reenders

This picture of marine conservation biologist Callie Veelenturf won the Nature Careers photo competition in 2018 — an event Veelenturf credits with kick-starting her career. She went on to assist in drafting a law that will help to protect species and habitats in Panama.Since 2021, editors at Nature have been tracking instances such as this, in which our journalism and opinion articles have had an impact. Here, we look at three times when content on biodiversity affected researchers, communities or policies. As well as shaping Veelenturf’s conservation work, Nature articles have raised the profile of a proposal to protect part of the Antarctic Ocean and fuelled discussions of carbon-tax proposals to fund tropical-forest conservation.Protect PanamaIn the prize-winning photo, Veelenturf was pictured with a leatherback sea turtle (Dermochelys coriacea) in Equatorial Guinea, where she was collecting data for her master’s degree at Purdue University Fort Wayne, Indiana, in 2016. She and biologist Jonah Reenders, now a photographer based in San Francisco, California, spent nearly half a year there, living in tents on Bioko Island, and Reenders took the picture of her as she measured the pH, conductivity and temperature of the sand near the leatherback’s nest.After the photo was published, a deluge of e-mails and messages “gave me this network, almost overnight, of other sea-turtle conservationists doing similar things around the world”, says Veelenturf, who is now based in Arraiján, Panama. “All of a sudden I was an ‘us’.”The photo award also validated her hard work, Veelenturf says, contradicting a common assumption that sea-turtle research just meant relaxing on the beach. Karla Barrientos-Muñoz, a Colombian sea-turtle conservationist at the Fundación Tortugas del Mar, based in Medellín, wrote that Veelenturf’s win was for all women in sea-turtle conservation. “It made me feel part of this community,” Veelenturf says.Inspired, she founded a non-profit organization called the Leatherback Project, based in Norfolk, Massachusetts, and later won a National Geographic Explorers grant, allowing her to perform the first scientific survey of sea turtles in Panama’s Pearl Islands archipelago. Here, her team worked with local communities to study the nesting sites and foraging grounds of olive ridley (Lepidochelys olivacea), green (Chelonia mydas), hawksbill (Eretmochelys imbricata) and eastern Pacific leatherback sea turtles.While doing fieldwork, Veelenturf read David Boyd’s book The Rights of Nature (2017), which described how some lawyers had fought to earn legal rights for nature. Such laws, which now exist in at least nine countries, make it easier to conserve the environment, because organizations can sue to protect a rainforest or stream. She went on to work with environmentally minded congress member Juan Diego Vásquez Gutiérrez and Panamanian legal advisers to draft a similar law for Panama, which is especially rich in biodiversity. Vásquez sponsored the legislation, and after more than a year of debate and revision by the public and in the national assembly, it was signed into law on 24 February 2022.Protect the AntarcticIn October 2020, a Comment article argued that the seas around the western Antarctic Peninsula should be designated a marine protected area. Overfishing there is removing large numbers of shrimp-like crustaceans called Antarctic krill (Euphausia superba), affecting the region’s entire web of species, including penguins, whales and seals, which feed on krill. The peninsula is also one of the fastest-warming ecosystems on the planet.A proposal for a marine protected area in the Antarctic must be approved by the groups of governments that make up the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Cassandra Brooks, a marine scientist at University of Colorado Boulder who co-authored the Nature piece and sits on CCAMLR’s non-voting science delegation, says that the Comment was sent to all the commission’s government delegations and observer groups. “If we can raise the issue in the public,” Brooks says, “it does help raise the issue within that diplomatic space.”The western Antarctic Peninsula proposal is one of three on the table for the next CCAMLR meeting in October 2022. It took ten years for CCAMLR to declare the Ross Sea a marine protected area. “The Antarctic does not have ten years,” says Comment co-author Carolyn Hogg, a conservation biologist at the University of Sydney in Australia.News stories about the article were published globally, including in China, India, South Korea and Malaysia. Hogg says it increased her visibility and further raised her profile with the Australian government. She is working with the government to ensure that the country’s threatened-species policy is informed by the latest genomic research. The goal is to give endangered populations the best chance of survival by preserving as much genetic diversity as possible.Hogg and Brooks wrote the piece with other women, some of whom were part of Homeward Bound, a global leadership programme for women in science, technology, engineering, mathematics and medicine. Many Homeward Bound participants and alumnae — 288 women from at least 30 countries — co-signed it and worked to translate it into many languages, “showing CCAMLR that this large community of women scientists from all over the world is watching, and going to hold them accountable”, Brooks says.Antarctica tends to be “both diplomatically and scientifically dominated by men”, she notes, and the impact of this global community of women was inspiring.Carbon tax for tropical forestsTropical countries should adopt a carbon tax, urged another Comment in February 2020, creating a levy on fossil fuels that should be used to conserve tropical forests. Costa Rica and Colombia had already adopted such a tax, and several other countries, including Indonesia, Brazil and Peru, are now considering implementing one, says Sebastian Troëng, executive vice-president of conservation partnerships at Conservation International who is based in Brussels and co-authored the piece.After the article was published, the authors made sure it was widely discussed. One of them, environmental economist Edward Barbier at Colorado State University in Fort Collins, presented the proposal at major meetings. These included the World Bank–International Monetary Fund forum in April 2022 and the Global Peatlands Initiative of the United Nations Framework Convention on Climate Change, at the 2021 climate summit COP26, in Glasgow, UK. The carbon-pricing proposal can be applied to any ecosystem, Barbier says. “Peatlands are ideal, because you’re saving probably the most carbon-dense ecosystem on our planet.”Meanwhile, Troëng’s colleagues presented the proposal to representatives from the finance and environment ministries of Chile, Mexico, Peru, Ecuador, Colombia and Costa Rica. “Since then, we’ve been working directly with government ministries,” he says, to strengthen the existing carbon-tax system in Colombia and to establish similar systems in Peru and Singapore. “I think what people appreciate the most is the fact that two countries have already done it, so it’s not just a theory or a wild idea, but it’s actually working,” Barbier says.“It’s always challenging to say, was it this paper that made something happen?” notes Troëng, on the impact of the article. “But it’s part of this growing consensus that nature plays an extremely important role in how we address climate change.” More

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Study subjectsWe conducted foraging experiments on strepsirrhines (Nindividuals = 18) at the Duke Lemur Center (DLC), North Carolina, from February to November 201513. Our sample includes six fat-tailed dwarf lemurs (3–16 years of age, 3 males, 3 females), six gray mouse lemurs (3–7 years of age, all female), and six aye-ayes (17–32 years of age, 2 males, 4 females). Because these species are solitary and nocturnal, most animals were housed singly and were kept on a reversed light cycle such that they were active and could be tested during the day. Housing conditions were similar for all individuals, and they were all fed daily in a similar manner with a diet that included fruits, vegetables, meal worms, and monkey chow (details in13).All vervet data were collected on wild animals (Nindividuals = 12) at Lake Nabugabo, Uganda (0°22′–12° S and 31°54′ E) during four separate field seasons (April-June 2013, Double Trapezoid array, M group15; June–September 2013, Pentagon array, M group24; August–September 2015, Z-array, M group12; July–August 2017, Pentagon array, KS group25). M group was composed of between 21–28 individuals, containing 2–3 adult males, 7–9 adult females, 2 subadult males, 1–3 subadult females, and 9–12 juveniles and infants. KS group was composed of 39–40 individuals including 5 adult males, 11 adult females, 3 sub-adult males, 5 sub-adult females, and 15–16 juveniles and infants. All individuals were reliably identified based on natural features (details in12,15,24,25). Outside of foraging experiments, wild vervets were not provision fed.All Japanese macaque data (Nindividuals = 10) were collected at the Awajishima Monkey Centre (AMC), Awaji Island, Japan (34°14′43.6″ N and 134°52′59.9″ E) between July and August 2019 (Z-array26). AMC is a privately-run tourist and conservation center visited by a large group of free-ranging Japanese macaques (~ 400 individuals) called the “Awajishima group”47. The group is composed of different-aged individuals of both sexes, with bachelor males and bachelor male groups living around the periphery48. The Awajishima group forages on wild foods for much of their dietary requirements but is also provision-fed a combination of wheat and soybeans, supplemented with peanuts, fruits, and vegetables twice daily for ~ 10 months of the year (details in47,49,50).Study designNavigation arraysThe strepsirrhines and vervets were tested on a “double-trapezoid” shaped multi-destination array with six feeding platforms13,15, modified from17 (Fig. 1a), where there were 720 possible routes (6!). Three different double-trapezoid arrays were built to account for differences in body size: one for the smaller dwarf and mouse lemurs, one for the mid-sized aye-ayes, and one for the larger, wild vervets. Arrays were scaled such that the distance from platform 1–2 (the shortest distance between targets) was approximately twice the body length of the subject species. Vervets were additionally tested on a Z-shaped array with six feeding platforms (720 possible routes, Fig. 1b12), and a pentagon-shaped array with five feeding platforms (120 possible routes, Fig. 1c24,25,46). Japanese macaques were tested on an identically sized Z-array26.Figure 1Design of the navigational arrays used, with (a) the Double Trapezoid array used for Cheirogaleus medius, Microcebus murinus, Daubentonia madagascariensis, and Chlorocebus pygerythrus. Three different arrays were built and scaled to the body size of animals (see “Methods”). (b) The Z-array used for C. pygerythrus and Macaca fuscata. The same size array was used for both species because they are similar in adult body lengths (vervet mean range from four sites: 34.5–42.6 cm51, Japanese macaque mean range from six sites: 48.9–59.7 cm52. (c) The Pentagon used for C. pygerythrus. Distances here are unitless but roughly proportional to the body size of each species tested. Created in R version 4.0.4 and ProCreate.Full size imageFor strepsirrhine trials, DLC staff captured individuals in their enclosures and transported them in padded crates to the testing room. The dwarf and mouse lemur array was set up in a specially designed box (0.91 × 1.83 m) with a small compartment to contain strepsirrhines for rebaiting between trials. The aye-aye array was set up on the ground in a room measuring 2.44 × 4.27 m, where subjects stayed during the duration of their daily trials13. Vervet and macaque trials occurred when individual monkeys voluntarily left their group to participate in foraging experiments alone. Vervet arrays were set up using wooden feeding platforms (0.75 m long, 0.75 m wide × 0.75 m high) placed in an outdoor clearing measuring roughly 10 × 14 m in the home range of the study group. Japanese macaque arrays were also set up using small wooden feeding tables (0.40 m long, 0.30 m wide, 0.21 m high), covered in green plastic labeled with the platform number. Two identical arrays were built in neighbouring provision-feeding fields at the AMC (Near Lower Field: ~ 10 × 35 m, and Far Lower Field: ~ 15 × 45 m).In these studies, all platforms were baited with a single food item. The reward used varied by species (strepsirrhines: grape piece, apple piece, honey, agave nectar, or nut butters, vervets: slice of banana, piece of popcorn; macaques: single peanut or piece of sweet potato). Strepsirrhines have sensory adaptations for using olfaction to locate food53, while the cercopithecoids are heavily reliant on vision to locate resources54, so we ensured that each platform was baited with identical food items within a trial that smelled and looked the same to avoid biasing where the animals chose to go. Platforms for the wild monkeys were not rebaited between trials until all animals were ≥ 20 m away and the entire sequence could be rebaited before their return15,24,25,26.For all species, we started a trial when the tested individual entered the array and took the reward at a platform. We then recorded each successive platform visit (including revisits to empty platforms) until all rewards had been collected ending the trial. In our analyses, we included a total of 852 trials collected over six navigational experiments, completed by 40 unique individuals (18 lemurs, 12 vervets, 10 macaques) (Table 2).Table 2 Individuals and trial sample size included in the analysis.Full size tableData simulationsIn addition to empirically collected data, we simulated agents learning to travel efficiently in the same set of arrays using a simple iterative-reinforcement learning model based on the one used by Reynolds et al.6 to test for traplining behavior in bumblebees. In this model, agents move randomly between locations in an array until they visit all locations, then reset for another trial. If the agent completed a trial by travelling less distance than on previous trials, the probability of the agent repeating location-to-location transitions that occurred in that trial increased for future trials by a reinforcement factor. Initial transition probabilities were inversely proportional to the distance between two locations. Unlike Reynolds et al.6 our simulated agents started at a random location and were not required to return to that location to complete the trial. This matches the trial structure used in our experiments (open-TSP), and reflects multiple central place foraging patterns in primates55. Finally, agents could not return to the location they had just come from, using an “avoid the last location” behavioral heuristic observed in nectivores56,57, which prevented agents from getting stuck in “loops” between two locations (S1 Simulation Validation).Within each of the arrays used to collect empirical data, we ran simulations with reinforcement factors of 1 (no reinforcement), 1.2 (mild reinforcement), and 2 (strong reinforcement). For each array and reinforcement factor combination, we ran 100 agents that each completed 120 trials, where there was an equal probability of starting each trial at any location. Then, for each array and reinforcement factor combination, we ran 100 additional simulations per species tested in the given array, where the probability of starting a trial at any location was equal to the empirically observed location-starting probabilities of the respective species.These simulations were designed to help us test predictions of our two hypotheses regarding primate learning and decision making within the arrays. If primates learn to solve navigational arrays efficiently by reinforcing movements between platform pairs, they should exhibit overall greater receptiveness in their sequences of location visits than reinforcement factor 1 simulations, and a greater decrease over time in total distance travelled to complete the arrays. If primates are pre-disposed to navigate arrays using heuristics, they should exhibit shorter distances travelled on initial trials than in simulations.Data analysisFrom the raw sequences of locations visited in each trial, we calculated two metrics: minimum distance traveled, and the proportion of platform revisits that occurred within identical 3-platform visit sequences (determinism-DET)18. All calculations were done using R version 4.0.458 and packages rstan59 and tidyverse60. A fully reproducible data notebook containing this work, as well as all analyzed data, is available at https://github.com/aqvining/Do-Primates-Trapline. All figures were created by AQV in R version 4.0.4 and ProCreate.Distance traveledTo calculate minimum distance traveled, we created a distance matrix for each resource array containing the relative linear distance between any two resource locations. These minimum linear distances approximate the distances traveled by the animals, which may not necessarily be linear. We then summed the linear distances for all transitions made in a trial. Because resource arrays were scaled to the subject species’ body size, these relative distances were standardized.DeterminismGiven a sequence of observations, Ayers et al.63 defines determinism (DET) as the proportion of all matching observation-pairs (recurrences) that occur within matching sub-sequences of observations (repeats) of a given length (minL). This metric has been previously used to distinguish sequences of resource visitation generated by traplining behaviour from sequences generated by known processes of random movement within a given resource array18,61,62. It has several advantages in the analysis of foraging patterns, including the ability to detect repeated sequences between non-consecutive foraging bouts, imperfect repeats in sequences (i.e., omission or addition of a particular site), and distinguishing between forward- and reverse-order sequence repeats63.We adapted the methods of63 to calculate the number of recurrences and repeats generated by the sequence of location visits in each trial of our experiments and simulations. Based on an analysis of the sensitivity of DET scores to the parameterization of minL, we set minL to three for our calculations (S2 Sensitivity Analysis).Statistical analysesLearning ratesWe modelled distance travelled as a function of trial number, species, and individual. Metrics of animal performance on learned tasks are known to follow power functions over time and experience64, so we a priori applied log transformations to distance travelled and trial number, then fit a linear model. Thus, in the resulting model, the intercept can be interpreted as an estimated distance travelled on the first trial and the slope can be interpreted as the exponent of a learning curve. We modelled species and individual effects on the intercept by summing an estimated grand mean (µ0), species level deviation (µsp,j), and individual level deviation (µid,i). We treated species and individual level effects on the learning rate parameter (slope) the same way, summing a grand mean (b0), species level deviation (bsp,j), and individual level deviation (bid,i). We estimated additional parameters for the variance of individual level deviations in intercept and slope (σµID and σbID, respectively). Finally, after finding residuals in an initial analysis to have variances predicted by trial number and species, we estimated a separate error variance for each species (σε,sp) and weighted the standard deviations of the resulting error distributions by dividing them by the square root of one plus the trial number.We set regularizing priors on the model parameters, assuming distances travelled would remain within one order of magnitude of the most efficient route, but not setting any strict boundaries. For the grand mean of the intercept, we used a normal distribution centered around twice the minimum possible distance required to visit all platforms in the array, with a variance of one. For the grand mean of the slope and all species and individual level deviations to the slope and intercept, we used normal distributions centered at zero with variance of one. For all error terms, we used half-cauchy priors with a location parameter of zero and a scale parameter of one. The full, hierarchical definition of the model is given in Eq. (1).$$Distance sim {mu }_{0}+ {mu }_{sp,j}+ {mu }_{id, i}+left({b}_{0}+ {b}_{sp, j}+ {b}_{id,i}right)Trial+ epsilon$$$${mu }_{0} sim mathrm{N}(4.78, 1)$$$${mu }_{sp}, {b}_{0}, {b}_{sp} sim mathrm{N}(mathrm{0,1})$$$${mu }_{id} sim mathrm{N}(0, {sigma }_{mu ID})$$$${b}_{id} sim mathrm{N}(0, {sigma }_{bID})$$$$epsilon sim mathrm{N}(0, {sigma }_{epsilon ,sp}/sqrt[2]{1+Trial})$$$${sigma }_{mu ID}, {sigma }_{bID}, {sigma }_{epsilon } sim mathrm{Half Cauchy}(mathrm{0,1})$$DeterminismTo compare DET between species, and between empirical and simulated data, we created a binomial model of expected repeats generated in a trial given the number of recurrences (Eq. 2).$$Repeats sim binom(Recursions, DET)$$$$DET= {logit}^{-1}(alpha)$$$$alpha={a}_{0}+Sp+Src+ Int+ID$$$${a}_{0}, Sp, Src, Int sim mathrm{N}(0, 1)$$$$ID sim mathrm{N}(0, {sigma }_{ID})$$$${sigma }_{ID}sim mathrm{Half Cauchy}(mathrm{0,1})$$where a0 is the mean intercept, Sp is one of four coefficients determined by the species (simulations are of the “species” which was used to assign its starting-location probabilities), Src is one of four coefficients determined by the source (empirical data and each level of reinforcement factor), Int is one of 16 interaction coefficients (each possible combination of Sp and Src), and ID is a varying effect of the individual. Because the length of a sequence affects DET, we limit our analysis of DET to the sequences generated by a subject’s or an agent’s first ten trials. Subjects that completed fewer than ten trials were excluded from this portion of the analysis. More

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