More stories

  • in

    Sampling from four geographically divergent young female populations demonstrates forensic geolocation potential in microbiomes

    Cohort demographicsA total of 206 female participants were enrolled in the study and passed our quality control standards. All participants were required to be between the ages of 18–26 years old (22.5 ± 2.1) and to be born and at the time living in one of four geographically distinct regions of the world: Barbados; Santiago, Chile; Pretoria, S. Africa; and Bangkok, Thailand. The regions do, however, differ by an order of magnitude in their geographic spread as the intra-distance separating the residence neighborhood of participants ranged from 34 (Barbados) to 681 km (Pretoria, S. Africa) (Fig. S2). The Chilean and the South African datasets are further divided into two contiguous sub-regions, or neighborhoods, to allow for a micro-geographic analysis. The study population is largely dominated by individuals with self-identified Thai heritage (33%), followed by Black African (16%), Afro-Caribbean (14%) and white (14%) descent, although 19% of the Chilean population did not report ethnicity.Study participants, despite the divergent geographies, mostly have similar dietary and lifestyle habits (Table S1). Over half the study population (62%) have a normal BMI, with the mean BMI in this range (22.6 ± 5.5). The diets of the different cohorts are also similar as of the total cohort, 78% consume a starch heavy diet (≥ 4 days a week) of rice, bread and pasta, followed by 66% who frequently consume (≥ 4 days a week) vegetables and fruit and 49% who frequently consume dairy products. The study population is split by level of tobacco exposure, with 51% of the population having never smoked, and 43% being exposed to second-hand smoke through living with a smoker. Over half (56%) of the study population own one or more pets.Stool microbiomeThe OTUs identified using the UPARSE pipeline17 were used to compute the alpha diversity of the microbial communities using the Chao1 (species richness) and Shannon (species evenness) indices. The mean Shannon indices reveal that the microbiota diversity is only significant between Thailand-Chile with FDR  More

  • in

    Improving quantitative synthesis to achieve generality in ecology

    Houlahan, J. E., McKinney, S. T., Anderson, T. M. & McGill, B. J. The priority of prediction in ecological understanding. Oikos 126, 1–7 (2017).Article 

    Google Scholar 
    Lawton, J. H. Are there general laws in ecology? Oikos 84, 177–192 (1999).Article 

    Google Scholar 
    Elliott-Graves, A. Generality and causal interdependence in ecology. Philos. Sci. 85, 1102–1114 (2018).Article 

    Google Scholar 
    Fox, J. W. The many roads to generality in ecology. Philos. Top. 9, 83–104 (2019).Article 

    Google Scholar 
    McGill, B. J. et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).Article 
    PubMed 

    Google Scholar 
    MacArthur, R. H. & Wilson, E. O. An equilibrium theory of insular zoogeography. Evolution 17, 373–387 (1963).Article 

    Google Scholar 
    Gurevitch, J., Fox, G. A., Wardle, G. M., Inderjit & Taub, D. Emergent insights from the synthesis of conceptual frameworks for biological invasions. Ecol. Lett. 14, 407–418 (2011).Article 
    PubMed 
    CAS 

    Google Scholar 
    Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014).Article 

    Google Scholar 
    Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Anderson, S. C. et al. Trends in ecology and conservation over eight decades. Front. Ecol. Environ. 19, 274–282 (2021).Article 

    Google Scholar 
    Kneale, D., Thomas, J., O’Mara-Eves, A. & Wiggins, R. How can additional secondary data analysis of observational data enhance the generalisability of meta-analytic evidence for local public health decision making? Res. Synth. Methods 10, 44–56 (2019).Article 
    PubMed 

    Google Scholar 
    Aguinis, H., Pierce, C. A., Bosco, F. A., Dalton, D. R. & Dalton, C. M. Debunking myths and urban legends about meta-analysis. Organ. Res. Methods 14, 306–331 (2011).Article 

    Google Scholar 
    Polit, D. F. & Beck, C. T. Generalization in quantitative and qualitative research: myths and strategies. Int. J. Nurs. Stud. 47, 1451–1458 (2010).Article 
    PubMed 

    Google Scholar 
    Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).Article 

    Google Scholar 
    Lundberg, I., Johnson, R. & Stewart, B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am. Sociol. Rev. 86, 532–565 (2021).Article 

    Google Scholar 
    Lawrance, R. et al. What is an estimand & how does it relate to quantifying the effect of treatment on patient-reported quality of life outcomes in clinical trials? J. Patient-Rep. Outcomes 4, 68 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Findley, M. G., Kikuta, K. & Denly, M. External validity. Annu. Rev. Polit. Sci. 24, 365–393 (2021).Article 

    Google Scholar 
    Pearl, J. & Bareinboim, E. External validity: from do-calculus to transportability across populations. Stat. Sci. 29, 579–595 (2014).Article 

    Google Scholar 
    Westreich, D., Edwards, J. K., Lesko, C. R., Cole, S. R. & Stuart, E. A. Target validity and the hierarchy of study designs. Am. J. Epidemiol. 188, 438–443 (2019).Article 
    PubMed 

    Google Scholar 
    Carpenter, C. J. Meta-analyzing apples and oranges: how to make applesauce instead of fruit salad. Hum. Commun. Res. 46, 322–333 (2020).Article 

    Google Scholar 
    Rohrer, J. M. & Arslan, R. C. Precise answers to vague questions: issues with interactions. Adv. Methods Pract. Psychol. Sci. 4, 1–19 (2021).
    Google Scholar 
    Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9–25 (1993).
    Google Scholar 
    Koricheva, J. & Gurevitch, J. Uses and misuses of meta-analysis in plant ecology. J. Ecol. 102, 828–844 (2014).Article 

    Google Scholar 
    Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).Article 
    PubMed 

    Google Scholar 
    Konno, K. et al. Ignoring non-English-language studies may bias ecological meta-analyses. Ecol. Evol. 10, 6373–6384 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13, 4–21 (2022).Article 

    Google Scholar 
    Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86, 638–641 (1979).Article 

    Google Scholar 
    Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).Article 
    PubMed 
    CAS 

    Google Scholar 
    Rothman, K. J., Gallacher, J. E. J. & Hatch, E. E. Why representativeness should be avoided. Int. J. Epidemiol. 42, 1012–1014 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spake, R. et al. Implications of scale dependence for cross-study syntheses of biodiversity differences. Ecol. Lett. 24, 374–390 (2021).Article 
    PubMed 

    Google Scholar 
    Spake, R. & Doncaster, C. P. Use of meta-analysis in forest biodiversity research: key challenges and considerations. For. Ecol. Manag. 400, 429–437 (2017).Article 

    Google Scholar 
    Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).Article 

    Google Scholar 
    Nakagawa, S., Noble, D. W. A., Senior, A. M. & Lagisz, M. Meta-evaluation of meta-analysis: ten appraisal questions for biologists. BMC Biol. 15, 18 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higgins, J. P. T. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).Article 
    PubMed 

    Google Scholar 
    Schielzeth, H. & Nakagawa, S. Conditional repeatability and the variance explained by reaction norm variation in random slope models. Methods Ecol. Evol. 13, 1214–1223 (2022).Article 

    Google Scholar 
    Nakagawa, S. et al. The orchard plot: cultivating a forest plot for use in ecology, evolution, and beyond. Res. Synth. Methods 12, 4–12 (2021).Article 
    PubMed 

    Google Scholar 
    Lorah, J. Effect size measures for multilevel models: definition, interpretation, and TIMSS example. Large-Scale Assess. Educ. 6, 8 (2018).Article 

    Google Scholar 
    O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).Article 

    Google Scholar 
    Ojha, M., Naidu, D. G. T. & Bagchi, S. Meta-analysis of induced anti-herbivore defence traits in plants from 647 manipulative experiments with natural and simulated herbivory. J. Ecol. 110, 799–816 (2022).Dodds, K. C. et al. Material type influences the abundance but not richness of colonising organisms on marine structures. J. Environ. Manag. 307, 114549 (2022).Article 

    Google Scholar 
    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).Article 
    PubMed 
    CAS 

    Google Scholar 
    Senior, A. M. et al. Heterogeneity in ecological and evolutionary meta- analyses: its magnitude and implications. Ecology 97, 3293–3299 (2016).Article 
    PubMed 

    Google Scholar 
    Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).Article 
    PubMed 
    CAS 

    Google Scholar 
    Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).Article 
    PubMed 

    Google Scholar 
    Glass, G. V. Primary, secondary, and meta-analysis of research. Educ. Res. 5, 3–8 (1976).Article 

    Google Scholar 
    Glass, G. V. Meta‐analysis at 25: a personal history. Education in Two Worlds https://ed2worlds.blogspot.com/2022/07/meta-analysis-at-25-personal-history.html (2000).Cooper, H. M. Organizing knowledge syntheses: a taxonomy of literature reviews. Knowl. Soc. 1, 104–126 (1988).
    Google Scholar 
    Soranno, P. A. et al. Cross-scale interactions: quantifying multi-scaled cause-effect relationships in macrosystems. Front. Ecol. Environ. 12, 65–73 (2014).Article 

    Google Scholar 
    Gerstner, K. et al. Will your paper be used in a meta-analysis? Make the reach of your research broader and longer lasting. Methods Ecol. Evol. 8, 777–784 (2017).Article 

    Google Scholar 
    Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    Simons, D. J., Shoda, Y. & Lindsay, D. S. Constraints on Generality (CoG): a proposed addition to all empirical papers. Perspect. Psychol. Sci. 12, 1123–1128 (2017).Article 
    PubMed 

    Google Scholar 
    Yarkoni, T. The generalizability crisis. Behav. Brain Sci. https://doi.org/10.1017/S0140525X20001685 (2020).Lopez, P. M., Subramanian, S. V. & Schooling, C. M. Effect measure modification conceptualized using selection diagrams as mediation by mechanisms of varying population-level relevance. J. Clin. Epidemiol. 113, 123–128 (2019).Article 
    PubMed 

    Google Scholar 
    Campbell, D. T. in Advances in QuasiExperimental Design and Analysis (ed. Trochim, W.) 67–77 (Jossey-Bass, 1986).Spake, R. et al. Meta‐analysis of management effects on biodiversity in plantation and secondary forests of Japan. Conserv. Sci. Pract. 1, e14 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Forest Ecosystem Diversity Basic Survey (in Japanese) (Forestry Agency of Japan, 2019); https://www.rinya.maff.go.jp/j/keikaku/tayouseichousa/index.htmlIto, S., Ishigamia, S., Mizoue, N. & Buckley, G. P. Maintaining plant species composition and diversity of understory vegetation under strip-clearcutting forestry in conifer plantations in Kyushu, southern Japan. For. Ecol. Manag. 231, 234–241 (2006).Article 

    Google Scholar 
    Utsugi, E. et al. Hardwood recruitment into conifer plantations in Japan: effects of thinning and distance from neighboring hardwood forests. For. Ecol. Manag. 237, 15–28 (2006).Article 

    Google Scholar 
    Kominami, Y. et al. Classification of bird-dispersed plants by fruiting phenology, fruit size, and growth form in a primary lucidophyllous forest: an analysis, with implications for the conservation of fruit–bird interactions. Ornthological Sci. 2, 3–23 (2003).Article 

    Google Scholar 
    Tsujino, R. & Matsui, K. Forest regeneration inhibition in a mixed broadleaf-conifer forest under sika deer pressure. J. For. Res. 27, 230–235 (2021).Article 

    Google Scholar 
    Spake, R., Soga, M., Catford, J. A. & Eigenbrod, F. Applying the stress-gradient hypothesis to curb the spread of invasive bamboo. J. Appl. Ecol. 58, 1993–2003 (2021).Article 

    Google Scholar 
    Mize, T. D. Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociol. Sci. 6, 81–117 (2019).Article 

    Google Scholar 
    Karaca-Mandic, P., Norton, E. C. & Dowd, B. Interaction terms in nonlinear models. Health Serv. Res. 47, 255–274 (2012).Article 
    PubMed 

    Google Scholar 
    Spake, R. et al. Forest damage by deer depends on cross-scale interactions between climate, deer density and landscape structure. J. Appl. Ecol. 57, 1376–1390 (2020).McCabe, C. J., Kim, D. S. & King, K. M. Improving present practices in the visual display of interactions. Adv. Methods Pract. Psychol. Sci. 1, 147–165 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shackelford, G. E. et al. Dynamic meta-analysis: a method of using global evidence for local decision making. BMC Biol. 19, 33 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Christie, A. P. et al. Innovation and forward‐thinking are needed to improve traditional synthesis methods: a response to Pescott and Stewart. J. Appl. Ecol. 59, 1191–1197 (2022).Article 

    Google Scholar 
    Haddaway, N. R. et al. EviAtlas: a tool for visualising evidence synthesis databases. Environ. Evid. 8, 22 (2019).Delory, B. M., Li, M., Topp, C. N. & Lobet, G. archiDART v3.0: a new data analysis pipeline allowing the topological analysis of plant root systems. F1000Research 7, 22 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perkel, J. M. The future of scientific figures. Nature 554, 133–134 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Weaver, S. & Gleeson, M. P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model. 26, 1315–1326 (2008).Article 
    PubMed 
    CAS 

    Google Scholar 
    Sutton, C. et al. Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, 4428 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods Ecol. Evol. 12, 1620–1633 (2021).Article 

    Google Scholar 
    Pearl, J. & Bareinboim, E. Transportability of causal and statistical relations: a formal approach. In 2011 IEEE 11th International Conference on Data Mining Workshops https://doi.org/10.1109/ICDMW.2011.169 (IEEE, 2011).Munthe-Kaas, H., Nøkleby, H. & Nguyen, L. Systematic mapping of checklists for assessing transferability. Syst. Rev. 8, 22 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dekkers, O. M., von Elm, E., Algra, A., Romijn, J. A. & Vandenbroucke, J. P. How to assess the external validity of therapeutic trials: a conceptual approach. Int. J. Epidemiol. 39, 89–94 (2010).Article 
    PubMed 
    CAS 

    Google Scholar 
    Schloemer, T. & Schröder-Bäck, P. Criteria for evaluating transferability of health interventions: a systematic review and thematic synthesis. Implement. Sci. 13, 88 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernandez-Hermida, J. R., Calafat, A., Becoña, E., Tsertsvadze, A. & Foxcroft, D. R. Assessment of generalizability, applicability and predictability (GAP) for evaluating external validity in studies of universal family-based prevention of alcohol misuse in young people: systematic methodological review of randomized controlled trials. Addiction 107, 1570–1579 (2012).Article 
    PubMed 

    Google Scholar 
    Avellar, S. A. et al. External validity: the next step for systematic reviews? Eval. Rev. 41, 283–325 (2017).Article 
    PubMed 

    Google Scholar 
    Bareinboim, E. & Pearl, J. A general algorithm for deciding transportability of experimental results. J. Causal Inference 1, 107–134 (2013).Article 

    Google Scholar 
    Degtiar, I. & Rose, S. A review of generalizability and transportability. Preprint at https://doi.org/10.48550/arXiv.2102.11904 (2021).Bareinboim, E. & Pearl, J. Meta-transportability of causal effects: a formal approach. J. Mach. Learn. Res. 31, 135–143 (2013).
    Google Scholar 
    Jamieson, D. Scientific uncertainty: how do we know when to communicate research findings to the public? Sci. Total Environ. 184, 103–107 (1996).Article 
    CAS 

    Google Scholar 
    Burchett, H. E. D., Mayhew, S. H., Lavis, J. N. & Dobrow, M. J. When can research from one setting be useful in another? Understanding perceptions of the applicability and transferability of research. Health Promot. Int. 28, 418–430 (2013).Article 
    PubMed 

    Google Scholar 
    Forscher, P. et al. Build up big-team science. Nature 601, 505–507 (2022).Article 

    Google Scholar 
    Whalen, M. A. et al. Climate drives the geography of marine consumption by changing predator communities. Proc. Natl Acad. Sci. USA 117, 28160–28166 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Moshontz, H. et al. The Psychological Science Accelerator: advancing psychology through a distributed collaborative network. Adv. Methods Pract. Psychol. Sci. 1, 501–515 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marschner, I. C. A general framework for the analysis of adaptive experiments. Stat. Sci. 36, 465–492 (2021).Article 

    Google Scholar 
    Clark, M. Shrinkage in Mixed Effects Models https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixed-models/ (2019).Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, 1142–1149 (1999).Article 

    Google Scholar 
    Mengersen, K., Gurevitch, J. & Schmid, C. H. in Handbook of Meta-analysis in Ecology and Evolution (eds Koricheva, U. et al.) 300–312 (Princeton Univ. Press, 2013).Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 
    PubMed 

    Google Scholar 
    Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salguero-Gómez, R. et al. The COMPADRE Plant Matrix Database: an open online repository for plant demography. J. Ecol. 103, 202–218 (2015).Article 

    Google Scholar 
    Salguero-Gómez, R. et al. COMADRE: a global data base of animal demography. J. Anim. Ecol. 85, 371–384 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pastor, D. A. & Lazowski, R. A. On the multilevel nature of meta-analysis: a tutorial, comparison of software programs, and discussion of analytic choices. Multivar. Behav. Res. 53, 74–89 (2018).Article 

    Google Scholar  More

  • in

    Mixotrophy in depth

    Rippka, R. et al. J. Gen. Microbiol. https://doi.org/10.1099/00221287-111-1-1 (1979).Article 

    Google Scholar 
    Muñoz-Marín, M. C. et al. ISME J. 14, 1065–1073 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yelton, A. P. et al. ISME J. 10, 2946–2957 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ward, B. A. & Follows, M. J. Proc. Natl Acad. Sci. USA 113, 2958–2963 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, Z. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01250-5 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Flombaum, P. et al. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zubkov, M. et al. Appl. Environ. Microbiol. 69, 1299–1304 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vila-Costa, M. et al. Science 314, 652–654 (2006).Article 
    PubMed 

    Google Scholar 
    Muñoz-Marín, M. C. et al. Proc. Natl Acad. Sci. USA 110, 8597–8602 (2013).Article 
    PubMed Central 

    Google Scholar 
    Gómez-Baena, G. et al. PLoS ONE 3, e3416 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coe, A. et al. Limnol. Oceanogr. 71, 1375–1388 (2016).Article 

    Google Scholar 
    Muñoz-Marín, M. C. et al. Microbiol. Spectr. https://doi.org/10.1101/2021.10.04.462702 (2022).Article 
    PubMed 

    Google Scholar  More

  • in

    Zebras of all stripes repel biting flies at close range

    The evolutionary origins of zebra stripes have been investigated—and debated—for centuries. The trait is rare, conspicuous, and intensely expressed, and thus appears to beg an adaptationist explanation. However, the utility of a complete coat of densely packed, starkly contrasting black-and-white stripes is not immediately apparent. Unlike many conspicuous visual traits, striped pelage is expressed with comparable intensity in both sexes and is thus unlikely to have arisen through sexual selection alone (although in plains zebras, Equus quagga, males have stripes closer to true black than females). Stripes are clearly not aposematic warning signals, nor do they provide camouflage in either the woodland or savannah habitats common across zebra ranges1,2. So, striping presents an ideal evolutionary puzzle: a trait so refined it seems it must be “for” something, but one that confers no clear advantage upon its bearers and imposes apparent costs (conspicuousness) that cannot be explained in Zahavian terms.Scientists have proposed and investigated several possible explanations for the evolution of zebra stripes (reviewed in3). The hypotheses suggest various ways in which stripes may provide a social function (species or individual recognition or social cohesion1,4), a temperature-regulation benefit5,6, an anti-predator effect7,8, or an anti-parasite effect9,10. There is continued debate over both the merits of individual hypotheses and the likelihood of stripes having arisen via a single driver vs. a confluence or alternation of multiple selective pressures6,11.The present study addresses the hypothesis that has thus far received the most empirical support: the anti-parasite hypothesis (also known as the ectoparasite hypothesis12). Zebras, like most ungulates, are harassed by tabanid, glossinid and Stomoxys species of biting flies, which can inflict significant blood loss, transmit disease, and weaken hosts when fly-avoidance behaviors reduce the host’s feeding rate9,13,14. Yet zebras are attacked far less than sympatric ungulates across their African range15,16, and also less than other equids9,17. Zebras also produce odors that may augment their anti-fly defenses18, but so do other sympatric ungulate species18,19, and a host of observations and experiments have demonstrated that black-and-white stripes alone are unattractive, or actively repellent to tabanid, glossinid, and Stomoxys flies17,20,21,22,23.Though the effect of stripes on flies is well-established, the source of the effect remains unexplained. Since Waage’s foundational studies in the 1970s and 1980s9,24 most hypotheses have suggested ways that stripes might interfere with the visual and navigational systems of flies, making it harder for them to locate, identify, or successfully land on striped targets. These hypothetical mechanisms can be roughly grouped by the distance (and the attendant phase of a fly’s orientation and landing behavior) at which they would likely operate:

    From afar: stripes might make it harder for flies to locate and distinguish zebras from background vegetation, perhaps by breaking up their outline9 or varying the way they polarize or reflect light17,31 especially from distances at which composite eyes support only low-resolution vision and cannot resolve zebra stripes as clear bands of alternating color on a single host (estimated at  > 2.0 m22,  > 4.4 m24, and even  > 20 m25).

    At close range (estimates range from 0.5 to 4.0 m26): stripes might interfere with orientation or landing behavior via any of several disruptive or ‘dazzle’-related visual effects27. For example, stripes might affect ‘optic flow’, or the fly’s perceived relative motion to its target as it approaches, by creating an illusion of false direction or speed of motion (e.g., via variants of the ‘barber pole’ or ‘wagon wheel’ effects28). Alternatively, relative motion to a striped pattern within the visual field may create the perception of self-rotation, inducing the fly’s involuntary ‘optomotor response’ and resulting in an avoidance turn in an effort to stay on a straight course29.

    Finally, stripes might cause confusion in the transition between long- and short-distance orientation. If zebras appear as blurred gray from a distance and then, at closer range, suddenly resolve into a sequence of floating black and white bars, this abrupt ‘visual transformation’26 might disrupt the behavioral sequence that facilitates landing.

    Within these categories, hypotheses have proliferated faster than experimental tests of many of the proposed mechanisms. The very active literature on this question has grown in somewhat haphazard fashion, as curious researchers test new possibilities without eliminating old ones6. Importantly, few experiments have controlled the distance from which flies are first able to view potential landing sites (but see23). While growing evidence supports a mechanism operating at close range22,26, failing to restrict the starting distance of the fly means that the full set of possible mechanisms outlined above all remain plausible contributors to most previous results.Additionally, while many studies have, appropriately, used artificial stimuli to isolate basic effects of color, pattern, brightness, and light polarization of (usually flat) test surfaces, possible contributions of several aspects of natural zebra pelage remain untested. Controlled experiments have used various landing substrates, including striped and solid oil tray traps, sticky plastic, smooth plastic17, cloth (Experiment 2 in22), horse blankets or sheets26, and paint on live animals30. These have all clearly demonstrated a broadly replicable visual effect: stripes, and some other juxtapositions of black and white (e.g., checkerboard patterns26), repel flies. However, insofar as specific features of zebra pelage factor into proposed mechanisms of fly repellence—the reflective properties of “smooth, shiny” coats31; the orientation of the stripes17,32; the light-polarizing effects of black and white hair vs. background vegetation25; and the complex structure of hair25—there is a need for more experiments that present natural targets to wild flies (but see22,33). Similarly, most experiments have compared landing preferences between black-and-white striped, solid black, solid white, and occasionally solid grey substrates, which have served as important controls for determining that light polarization, rather than a combination of polarization and brightness, is sufficient to induce the effect of stripe avoidance17. However, it is now time to refocus on the original question by presenting flies with more realistic choices. Since biting flies seeking a bloodmeal on the African savannah seldom encounter solid black hosts, and even more rarely solid white hosts, landing choices should be compared between zebra stripes and common coat colors of sympatric mammals, namely various shades of brown. Further, tabanid, glossinid, and Stomoxys flies all avoid landing on stripes that are the same width or narrower than the widest zebra stripes 17,23, and there is some evidence that narrower stripes are even more repellent to tabanids17. This pattern is potentially significant in the application of the anti-parasite hypothesis to an adaptive explanation for the striking variation in stripe width across zebra species and between the different areas of the body on individual zebras22, but must first be confirmed with experiments using real zebra pelage.Here, we present a simple experiment designed to address each of these gaps in the literature on the anti-fly benefits of zebra stripes. In this field experiment, the landing choices of flies were tested entirely within the range at which all estimates agree flies should be able to perceive the presented stripes ( More

  • in

    Low levels of sibship encourage use of larvae in western Atlantic bluefin tuna abundance estimation by close-kin mark-recapture

    Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More

  • in

    Humid tropical vertebrates are at lower risk of extinction and population decline in forests with higher structural integrity

    Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
    PubMed 

    Google Scholar 
    Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).Article 
    PubMed 

    Google Scholar 
    Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).Article 

    Google Scholar 
    Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 
    PubMed 

    Google Scholar 
    Hansen, A. et al. Global humid tropics forest structural condition and forest structural integrity maps. Sci. Data 6, 232 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    COP 11 Decision X/2. Strategic Plan for Biodiversity 2011–2020 (Convention on Biological Diversity, 2010).New York Declaration on Forests (UN, 2014).Transforming our World: The 2030 Agenda for Sustainable Development. A/RES/70/1 Resolution Adopted by the United Nations General Assembly (UN, 2015).Adoption of the Paris Agreement. Proposal by the President. Draft Decision -/CP.21 (UNFCCC, 2015).Hansen, A. J. et al. Toward monitoring forest ecosystem integrity within the post-2020 Global Biodiversity Framework. Conserv. Lett. 14, e12822 (2021).Article 

    Google Scholar 
    Scholes, R. et al. (eds) Summary for Policymakers of the Assessment Report on Land Degradation and Restoration (IPBES, 2018).First Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2021).Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).Article 

    Google Scholar 
    The IUCN Red List of Threatened Species Version 2020–1 (IUCN, 2020).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ives, A. R. & Garland, T. Phylogenetic logistic regression for binary dependent variables. Syst. Biol. 59, 9–26 (2010).Article 
    PubMed 

    Google Scholar 
    Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).Article 
    PubMed 

    Google Scholar 
    Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fletcher, R. & Fortin, M.-J. Spatial Ecology and Conservation Modeling: Applications with R (Springer, 2018). https://doi.org/10.1007/978-3-030-01989-1Briant, G., Gond, V. & Laurance, S. G. W. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. Conserv. 143, 2763–2769 (2010).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pillay, R. et al. Using interview surveys and multispecies occupancy models to inform vertebrate conservation. Conserv. Biol. 36, e13832 (2022).Article 
    PubMed 

    Google Scholar 
    Agresti, A. Categorical Data Analysis (John Wiley and Sons, 2002).Smith, A. C., Koper, N., Francis, C. M. & Fahrig, L. Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation. Landsc. Ecol. 24, 1271–1285 (2009).Article 

    Google Scholar 
    Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl Acad. Sci. USA 18, 10309–10313 (2003).Article 

    Google Scholar 
    Turner, I. M. & Corlett, R. T. The conservation value of small, isolated fragments of lowland tropical rain forest. Trends Ecol. Evol. 11, 330–333 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tulloch, A. I. T., Barnes, M. D., Ringma, J., Fuller, R. A. & Watson, J. E. M. Understanding the importance of small patches of habitat for conservation. J. Appl. Ecol. 53, 418–429 (2016).Article 

    Google Scholar 
    Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl Acad. Sci. USA 116, 909–914 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl Acad. Sci. USA 105, 20770–20775 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Conserv. Lett. 13, e12692 (2020).Article 

    Google Scholar 
    Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

    Google Scholar 
    Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Betts, M. G. et al. Forest degradation drives widespread avian habitat and population declines. Nat. Ecol. Evol. 6, 709–719 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Basset, Y. et al. Arthropod diversity in a tropical forest. Science 338, 1481–1484 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. B 280, 20122131 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).Article 
    PubMed 

    Google Scholar 
    Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).Article 
    PubMed 

    Google Scholar 
    Bird Species Distribution Maps of the World Version 2018.1 (BirdLife International, accessed 16 August 2019).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).Article 
    PubMed 

    Google Scholar 
    González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563 (2019).Article 
    PubMed 

    Google Scholar 
    IUCN Habitats Classification Scheme Version 3.1 (IUCN, 2012).Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).Article 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hansen, M. C. et al. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sens. Environ. 185, 221–232 (2016).Article 

    Google Scholar 
    Sanderson, E. W. et al. The human footprint and the last of the wild. Bioscience 52, 891–904 (2002).Article 

    Google Scholar 
    Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Di Marco, M., Watson, J. E. M., Possingham, H. P. & Venter, O. Limitations and trade-offs in the use of species distribution maps for protected area planning. J. Appl. Ecol. 54, 402–411 (2017).Article 

    Google Scholar 
    Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2603–E2610 (2013).Article 

    Google Scholar 
    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Article 

    Google Scholar 
    Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).Article 

    Google Scholar 
    Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

    Google Scholar 
    Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).Article 

    Google Scholar 
    Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).Article 
    PubMed 

    Google Scholar 
    Ho, L. S. T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 
    PubMed 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Verhoeven, K. J. F., Simonsen, K. L. & McIntyre, L. M. Implementing false discovery rate control: increasing your power. Oikos 108, 643–647 (2005).Article 

    Google Scholar 
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
    Google Scholar 
    Bivand, R. et al. spdep: Spatial dependence: weighting schemes, statistics and models. R package version 0.7-4 (2017).Bjornstad, O. N. ncf: Spatial covariance functions. R package version 1.2-1 (2018). More

  • in

    The red harvester ant

    Gordon, D. M. Anim. Behav. 49, 649–659 (1995).Article 

    Google Scholar 
    Gordon, D. M. The Ecology of Collective Behavior (Princeton Univ. Press, in the press).Gordon, D. M. Anim. Behav. 38, 194–204 (1989).Article 

    Google Scholar 
    Greene, M. J. & Gordon, D. M. Nature 423, 32 (2003).Article 
    CAS 

    Google Scholar 
    Pinter-Wollman, N. et al. Anim. Behav. 86, 197–207 (2013).Article 

    Google Scholar 
    Gordon, D. M., Guetz, A., Greene, M. J. & Holmes, S. Behav. Ecol. 22, 429–435 (2011).Article 

    Google Scholar 
    Prabhakar, B., Dektar, K. N. & Gordon, D. M. PLOS Comput. Biol. 8, e1002670 (2012).Article 
    CAS 

    Google Scholar 
    Davidson, J. D., Arauco-Aliaga, R. P., Crow, S., Gordon, D. M. & Goldman, M. S. Front. Ecol. Evol. 4, 115 (2016).Article 

    Google Scholar 
    Pagliara, R., Gordon, D. M. & Leonard, N. E. PLOS Comput. Biol. 14, e1006200 (2018).Article 

    Google Scholar 
    Friedman, D. A. et al. iScience 8, 283–294 (2018).Article 
    CAS 

    Google Scholar 
    Gordon, D. M. Ant Encounters: Interaction Networks and Colony Behavior (Princeton Univ. Press, 2010).Sundaram, M., Steiner, E. & Gordon, D. M. Ecol. Monogr. 92, e1503 (2022).Article 

    Google Scholar 
    Ingram, K. K., Pilko, A., Heer, J. & Gordon, D. M. J. Anim. Ecol. 82, 540–550 (2013).Article 

    Google Scholar 
    Gordon, D. M. Nature 498, 91–93 (2013).Article 
    CAS 

    Google Scholar  More

  • in

    Fragmentation by major dams and implications for the future viability of platypus populations

    Zhou, Y. et al. Platypus and echidna genomes reveal mammalian biology and evolution. Nature 592, 756–762 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bino, G. et al. The platypus: evolutionary history, biology, and an uncertain future. J. Mammal. 100, 308–327 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Veyrunes, F. et al. Bird-like sex chromosomes of platypus imply recent origin of mammal sex chromosomes. Genome Res. 18, 965–973 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anich, P. S. et al. Biofluorescence in the platypus (Ornithorhynchus anatinus). Mammalia 85, 179–181 (2021).Article 

    Google Scholar 
    Pavoine, S., Ollier, S. & Dufour, A. B. Is the originality of a species measurable? Ecol. Lett. 8, 579–586 (2005).Article 

    Google Scholar 
    Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Woinarski, J. & Burbidge, A. In The IUCN Red List of Threatened Species 2016: e. T40488A21964009 (IUCN, 2016).Victoria Government Gazette. Authority of Victorian Government Printer (2021).Hawke, T., Bino, G. & Kingsford, R. T. A silent demise: Historical insights into population changes of the iconic platypus (Ornithorhynchus anatinus). Glob. Ecol. Conserv. 20, e00720 (2019).Article 

    Google Scholar 
    Grant, T. R. & Fanning, D. Platypus (CSIRO PUBLISHING, 2007).Bino, G., Kingsford, R. T. & Wintle, B. A. A stitch in time–Synergistic impacts to platypus metapopulation extinction risk. Biol. Conserv. 242, 108399 (2020).Article 

    Google Scholar 
    Hawke, T., Bino, G. & Kingsford, R. A National Assessment of the Conservation Status of the Platypus (University of New South Wales, 2021).Bino, G., Hawke, T. & Kingsford, R. T. Synergistic effects of a severe drought and fire on platypuses. Sci. Total Environ. 777, 146137 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Klamt, M., Thompson, R. & Davis, J. Early response of the platypus to climate warming. Glob. Change Biol. 17, 3011–3018 (2011).Article 

    Google Scholar 
    Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 
    PubMed 

    Google Scholar 
    Grill, G. et al. An index-based framework for assessing patterns and trends in river fragmentation and flow regulation by global dams at multiple scales. Environ. Res. Lett. 10, 015001 (2015).Article 

    Google Scholar 
    Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dugan, P. J. et al. Fish migration, dams, and loss of ecosystem services in the Mekong basin. Ambio 39, 344–348 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Sci. Adv. 3, e1700611 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grant, T. R. & Temple-Smith, P. D. Conservation of the platypus, Ornithorhynchus anatinus: threats and challenges. Aquat. Ecosyst. Health Manag. 6, 5–18 (2003).Article 

    Google Scholar 
    Hawke, T., Bino, G. & Kingsford, R. T. Damming insights: variable impacts and implications of river regulation on platypus populations. Aquat. Conserv.: Mar. Freshw. Ecosyst. 31, 504–519 (2021).Article 

    Google Scholar 
    Bethge, P., Munks, S., Otley, H. & Nicol, S. Diving behaviour, dive cycles and aerobic dive limit in the platypus Ornithorhynchus anatinus. Comp. Biochem. Physiol. Part A: Mol. Integr. Physiol. 136, 799–809 (2003).Article 

    Google Scholar 
    Grant, T. & Llewellyn, L. C. The Biology and Management of the Platypus (Ornithorhynchus anatinus) in NSW (NSW National Parks and Wildlife Service, 1991).Grant, T. R. Captures, Capture Mortality, Age and Sex Ratios of Platypuses, Ornithorhynchus Anatinus, during Studies over 30 Years in the Upper Shoalhaven River in New South Wales (Linnean Society of New South Wales, 2004).Marchant, R. & Grant, T. The productivity of the macroinvertebrate prey of the platypus in the upper Shoalhaven River, New South Wales. Mar. Freshw. Res. 66, 1128–1137 (2015).Article 

    Google Scholar 
    Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M. & Turlure, C. Individual dispersal, landscape connectivity and ecological networks. Biol. Rev. 88, 310–326 (2013).Article 
    PubMed 

    Google Scholar 
    Frankham, R. et al. Genetic Management of Fragmented Animal and Plant Populations (Oxford University Press, 2017).Frankham, R. Genetic rescue of small inbred populations: meta‐analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).Article 
    PubMed 

    Google Scholar 
    Garant, D., Forde, S. E. & Hendry, A. P. The multifarious effects of dispersal and gene flow on contemporary adaptation. Funct. Ecol. 21, 434–443 (2007).Article 

    Google Scholar 
    Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).Article 
    PubMed 

    Google Scholar 
    Kolomyjec, S. H. The History and Relationships of Northern Platypus (Ornithorhynchus Anatinus) Populations: A Molecular Approach (James Cook University, 2010).Furlan, E. M. et al. Dispersal patterns and population structuring among platypuses, Ornithorhynchus anatinus, throughout south-eastern Australia. Conserv. Genet. 14, 837–853 (2013).Article 
    CAS 

    Google Scholar 
    Balkenhol, N., Cushman, S., Storfer, A. & Waits, L. Landscape Genetics: Concepts, Methods, Applications (John Wiley & Sons, 2015).Ramachandran, S. et al. Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc. Natl Acad. Sci. USA 102, 15942–15947 (2005).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hoffman, J. R., Willoughby, J. R., Swanson, B. J., Pangle, K. L. & Zanatta, D. T. Detection of barriers to dispersal is masked by long lifespans and large population sizes. Ecol. Evolution 7, 9613–9623 (2017).Article 

    Google Scholar 
    Meirmans, P. G. & Hedrick, P. W. Assessing population structure: F-ST and related measures. Mol. Ecol. Resour. 11, 5–18 (2011).Article 
    PubMed 

    Google Scholar 
    Lehner, B. et al. High‐resolution mapping of the world’s reservoirs and dams for sustainable river‐flow management. Front. Ecol. Environ. 9, 494–502 (2011).Article 

    Google Scholar 
    Lemopoulos, A. et al. Comparing RADseq and microsatellites for estimating genetic diversity and relatedness—implications for brown trout conservation. Ecol. Evolution 9, 2106–2120 (2019).Article 

    Google Scholar 
    Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sherwin, W. B., Chao, A., Jost, L. & Smouse, P. E. Information theory broadens the spectrum of molecular ecology and evolution. Trends Ecol. Evol. 32, 948–963 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Serena, M. & Williams, G. Movements and cumulative range size of the platypus (Ornithorhynchus anatinus) inferred from mark–recapture studies. Aust. J. Zool. 60, 352–359 (2013).Article 

    Google Scholar 
    Hawke, T. et al. Fine‐scale movements and interactions of platypuses, and the impact of an environmental flushing flow. Freshw. Biol. 66, 177–188 (2021).Article 

    Google Scholar 
    Hawke, T. et al. Long-term movements and activity patterns of platypus on regulated rivers. Sci. Rep. 11, 1–11 (2021).Article 

    Google Scholar 
    Nislow, K. H., Hudy, M., Letcher, B. H. & Smith, E. P. Variation in local abundance and species richness of stream fishes in relation to dispersal barriers: implications for management and conservation. Freshw. Biol. 56, 2135–2144 (2011).Article 

    Google Scholar 
    Søndergaard, M. & Jeppesen, E. Anthropogenic impacts on lake and stream ecosystems, and approaches to restoration. J. Appl. Ecol. 44, 1089–1094 (2007).Hoffmann, A. A., Miller, A. D. & Weeks, A. R. Genetic mixing for population management: From genetic rescue to provenancing. Evol. Appl. 14, 634–652 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mills, L. S. & Allendorf, F. W. The one-migrant-per-generation rule in conservation and management. Conserv. Biol. 10, 1509–1518 (1996).Article 

    Google Scholar 
    Brown, J. J. et al. Fish and hydropower on the US Atlantic coast: failed fisheries policies from half‐way technologies. Conserv. Lett. 6, 280–286 (2013).Article 

    Google Scholar 
    Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish. Fish. 19, 340–362 (2018).Article 

    Google Scholar 
    Broadhurst, B., Ebner, B., Lintermans, M., Thiem, J. & Clear, R. Jailbreak: a fishway releases the endangered Macquarie perch from confinement below an anthropogenic barrier. Mar. Freshw. Res. 64, 900–908 (2013).Article 

    Google Scholar 
    Sainsbury, A. W. & Vaughan‐Higgins, R. J. Analyzing disease risks associated with translocations. Conserv. Biol. 26, 442–452 (2012).Article 
    PubMed 

    Google Scholar 
    Kolomyjec, S. H., Grant, T. R., Johnson, C. N. & Blair, D. Regional population structuring and conservation units in the platypus (Ornithorhynchus anatinus). Aust. J. Zool. 61, 378–385 (2014).Article 

    Google Scholar 
    Drechsler, M. & Burgman, M. A. Combining population viability analysis with decision analysis. Biodivers. Conserv. 13, 115–139 (2004).Article 

    Google Scholar 
    Kolomyjec, S. H. et al. Population genetics of the platypus (Ornithorhynchus anatinus): a fine-scale look at adjacent river systems. Aust. J. Zool. 57, 225–234 (2009).Article 

    Google Scholar 
    Kolomyjec, S. H., Grant, T. R. & Blair, D. Ten polymorphic microsatellite DNA markers for the platypus, Ornithorhynchus anatinus. Mol. Ecol. Resour. 8, 1133–1135 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Martin, H. C. et al. Insights into platypus population structure and history from whole-genome sequencing. Mol. Biol. Evol. 35, 1238–1252 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bino, G., Kingsford, R. T., Grant, T., Taylor, M. D. & Vogelnest, L. Use of implanted acoustic tags to assess platypus movement behaviour across spatial and temporal scales. Sci. Rep. 8, 1–12 (2018).Article 
    CAS 

    Google Scholar 
    Kilian, A. et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89 (2012).Article 
    PubMed 

    Google Scholar 
    Georges, A. et al. Genomewide SNP markers breathe new life into phylogeography and species delimitation for the problematic short‐necked turtles (Chelidae: Emydura) of eastern Australia. Mol. Ecol. 27, 5195–5213 (2018).Article 
    PubMed 

    Google Scholar 
    Steane, D. A. et al. Population genetic analysis and phylogeny reconstruction in Eucalyptus (Myrtaceae) using high-throughput, genome-wide genotyping. Mol. Phylogenet. Evol. 59, 206–224 (2011).Article 
    PubMed 

    Google Scholar 
    Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schmidt, T. L., Jasper, M. E., Weeks, A. R. & Hoffmann, A. A. Unbiased population heterozygosity estimates from genome‐wide sequence data. Methods Ecol. Evolution 12, 1888–1898 (2021).Article 

    Google Scholar 
    Pew, J., Muir, P. H., Wang, J. & Frasier, T. R. related: an R package for analysing pairwise relatedness from codominant molecular markers. Mol. Ecol. Resour. 15, 557–561 (2015).Article 
    PubMed 

    Google Scholar 
    Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).Article 
    PubMed 

    Google Scholar 
    Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89 (2013).Article 

    Google Scholar 
    Mijangos, J. L., Gruber, B., Berry, O., Pacioni, C. & Georges, A. dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture. Methods Ecol. Evol. 13, 2150–2158 (2022).McVean, G. A genealogical interpretation of principal components analysis. PLoS Genet. 5, e1000686 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Mijangos, J. et al. Datasets and R scripts for Fragmentation by major dams and implications for the future viability of platypus populations (2022).IUCN (International Union for Conservation of Nature) 2008. Ornithorhynchus anatinus. The IUCN Red List of Threatened Species. Version 2022-1. https://www.iucnredlist.org (2022).Crossman, S. & Li, O. Surface Hydrology Lines (National) (2015).Crossman, S. & Li, O. Surface Hydrology Polygons (National) (2015).Australian Bureau of Statistics (2021). States and Territories – 2021 – Shapefile [https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/access-and-downloads/digital-boundary-files] [Shapefile], Digital boundary files (2022).Australian National Committee on Large Dams Incorporated (ANCOLD). Register of Large Dams Australia (2022). More