Philippa Kaur

Understanding the spatial patterns and drivers of animal movement is a crucial first step to controlling disease spread4. Our study provides novel information about where, how and when cattle move in a region beset by endemic pathogens2,39,40. Because contacts occur heterogeneously through time and space, interventions targeting areas and times of high contact risk could effectively break the chain of transmission across wide areas. We found that cattle herds had the highest probability of contact at dipping sites, far from their bomas, in small herds and during periods of low rainfall, indicating that transmission of all pathogens may be particularly elevated under these conditions (Figs. 5, 6). Nonetheless, cattle spent most of their time in other areas (i.e. near bomas or in grazing areas) where the direction and magnitude of effect of spatiotemporal scale on contact rates varies. This suggests that interventions for different pathogens in these systems will likely require a consideration of scale of transmission and be tailored to particular pathogens. Overall, our study provides a framework for risk-based livestock disease control approaches for the most dominant management systems in sub-Saharan Africa.Daily movement patterns of cattle in pastoral and agropastoral settings in sub-Saharan Africa largely reflect the distribution of shared resources, which determines the distance animals move each day and the probability of contacting each other. Our results are similar to those reported in other regions of Africa, suggesting broadly comparable patterns of daily displacement. For instance, cattle in our agropastoral study area travel to grazing, watering and dipping locations that are ~ 4 km from their bomas and primarily during daylight hours (Fig. 2). Similarly, in Kenya, cattle in the pastoral Mara and Ol Pajeta regions move less than 6 km from their bomas and movements peak around 12:00–14:00 h each day9,41. Despite the predominance of short-distance daily movements, we observed occasional long-distance movements (i.e. up to 12 km), particularly by larger herds. Transhumant cattle in Cameroon also moved up to 23 km/day for short periods, while relocating to seasonal grazing areas on the edge of the Sahel, though in most observations (86%) they moved less than 5 km/day8. Although we observed no contacts among cattle from bomas  > 17 km apart (Supplementary Fig. S5), regardless of how contact was defined, infrequent long-distance movements by large herds may provide a conduit for disease transmission between villages42. Indeed, larger herds actually had a lower relative probability of contact across spatiotemporal scales (Fig. 5), which may reflect the fact that large herds were more likely to move to areas away from other collared cattle, either because they were moving outside the study area, or because they had exclusive use of particular areas, whereas smaller herds that were mostly moved around bomas mixed more frequently. While interventions (e.g. vaccination or quarantine) targeting small herds would address local disease events, particularly within villages, halting larger-scale transmission requires an understanding of livestock pathways enabling inter-village connectivity and strategies tailored to herds driving these processes.A key difference between the movement of cattle in agropastoral and pastoral systems lies in the seasonal variation of daily movement. In our study, agropastoralists move their herds farther in the wet compared to the dry season, while the opposite has been reported for pastoralists8,9,41. During the wet season, agropastoralists cultivate crops near their homesteads, which increases competition for space and displaces cattle to reserved grazing areas far from cultivated land11. During the dry season, particularly in the early period, cattle graze harvested fields around the homestead and tend to move short distances each day. In our study, although individual herds travelled more (marginally) in the wet compared to the dry season, there were more contacts following low rainfall periods when resources were typically scarce (Fig. 5). Similarly, a previous study has shown that more villages were connected at shared resource areas during dry spells, which resulted in higher contacts11. This suggests a higher disease risk in the dry compared to wet seasons in agropastoral management systems.Translating movements into contact between individuals is challenging because the definition of a “contact” depends on the distance at which pathogens can travel in space, and the time period that pathogens survive, or mature to an infectious state, in the environment. Most studies that attempt to measure contact, however, focus only on a single scale. Here, we show that pairwise contact rates between cattle herds generally increase with broader spatiotemporal definitions of contact. Yet, there was no difference at spatial scales between 50 m, 100 m and 200 m for a temporal scale of one hour, suggesting these scales are functionally equivalent definitions of contact. Thus, we define “close contact” as proximity of livestock herds within 200 m in any given hour, which would be applicable to multiple disease systems and vital for understanding infectious disease spread in traditionally managed herds. However, given that herds tracked in our study ranged in size from 30 to 500 cattle, for households with herds of  More

Soil physicochemical propertiesThe test results of physicochemical factors of the soil are shown in Table 2. In the soil with 15-year vines, the average contents of TK and SK were highest and the contents of SOM and TN were lowest. In the soil with 5-year vines, the contents of XN and SK were relatively higher, and the soil pH was between 7.86 and 7.98, thus it is alkaline soil.Table 2 Determined results of soil physicochemical properties.Full size tableThe analysis of variance showed that grape planting year had significant effect on TK and SP (P  More

1.Palumbi, S. R. Humans as the world’s greatest evolutionary force. Science 293, 1786–1790 (2001).ADS 
PubMed 
Article 
CAS 

Google Scholar 
2.Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human Influences on Evolution, and the Ecological and Societal Consequences (The Royal Society, 2017).Book 

Google Scholar 
3.Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B 285, 20182047 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Dynesius, M. & Nilsson, C. Fragmentation and flow regulation of river systems in the northern third of the world. Science 266, 753–762 (1994).ADS 
PubMed 
Article 
CAS 

Google Scholar 
5.Gibson, L., Wilman, E. N. & Laurance, W. F. How green is ‘green’energy?. Trends Ecol. Evol. 32, 922–935 (2017).PubMed 
Article 

Google Scholar 
6.Calles, O. & Greenberg, L. Connectivity is a two-way street—the need for a holistic approach to fish passage problems in regulated rivers. River Res. Appl. 25, 1268–1286 (2009).Article 

Google Scholar 
7.Poff, N. L. et al. The natural flow regime. Bioscience 47, 769–784 (1997).Article 

Google Scholar 
8.Haraldstad, T. et al. Anthropogenic and natural size-related selection act in concert during brown trout (Salmo trutta) smolt river descent. Hydrobiologia, 1–14 (2020).9.Limburg, K. E. & Waldman, J. R. Dramatic declines in North Atlantic diadromous fishes. Bioscience 59, 955–965 (2009).Article 

Google Scholar 
10.Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).ADS 
PubMed 
Article 
CAS 

Google Scholar 
11.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): a review of aspects of their life histories. Ecol. Freshw. Fish 12, 1–59 (2003).Article 

Google Scholar 
12.Thorstad, E. B., Økland, F., Aarestrup, K. & Heggberget, T. G. Factors affecting the within-river spawning migration of Atlantic salmon, with emphasis on human impacts. Rev. Fish Biol. Fish. 18, 345–371 (2008).Article 

Google Scholar 
13.Parrish, D. L., Behnke, R. J., Gephard, S. R., McCormick, S. D. & Reeves, G. H. Why aren’t there more Atlantic salmon (Salmo salar)?. Can. J. Fish. Aquat. Sci. 55, 281–287 (1998).Article 

Google Scholar 
14.Larinier, M. Fish passage experience at small-scale hydro-electric power plants in France. Hydrobiologia 609, 97–108 (2008).Article 

Google Scholar 
15.Coutant, C. C. & Whitney, R. R. Fish behavior in relation to passage through hydropower turbines: a review. Trans. Am. Fish. Soc. 129, 351–380 (2000).Article 

Google Scholar 
16.Montèn, E. Fish and Turbines: Fish Injuries During Passage Through Power Station Turbines (Nordsteds Tryckeri, 1985).
Google Scholar 
17.Pracheil, B. M., DeRolph, C. R., Schramm, M. P. & Bevelhimer, M. S. A fish-eye view of riverine hydropower systems: the current understanding of the biological response to turbine passage. Rev. Fish Biol. Fisheries 26, 153–167 (2016).Article 

Google Scholar 
18.Calles, O., Rivinoja, P. & Greenberg, L. A Historical perspective on downstream passage at hydroelectric plants in swedish rivers. In: Ecohydraulics. Wiley (2013).19.Silva, A. T. et al. The future of fish passage science, engineering, and practice. Fish Fish. 19, 340–362 (2017).Article 

Google Scholar 
20.Noonan, M. J., Grant, J. W. A. & Jackson, C. D. A quantitative assessment of fish passage efficiency. Fish Fish. 13, 450–464 (2012).Article 

Google Scholar 
21.Scruton, D. A., McKinley, R. S., Kouwen, N., Eddy, W. & Booth, R. K. Improvement and optimization of fish guidance efficiency (FGE) at a behavioural fish protection system for downstream migrating Atlantic salmon (Salmo salar) smolts. River Res. Appl. 19, 605–617 (2003).Article 

Google Scholar 
22.Mallen-Cooper, M. & Brand, D. A. Non-salmonids in a salmonid fishway: what do 50 years of data tell us about past and future fish passage?. Fish. Manage. Ecol. 14, 319–332 (2007).Article 

Google Scholar 
23.Bunt, C., Castro-Santos, T. & Haro, A. Performance of fish passage structures at upstream barriers to migration. River Res. Appl. 28, 457–478 (2012).Article 

Google Scholar 
24.Haugen, T. O., Aass, P., Stenseth, N. C. & Vøllestad, L. A. Changes in selection and evolutionary responses in migratory brown trout following the construction of a fish ladder. Evol. Appl. 1, 319–335 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Mallen-Cooper, M. & Stuart, I. G. Optimising Denil fishways for passage of small and large fishes. Fish. Manage. Ecol. 14, 61–71 (2007).Article 

Google Scholar 
26.Maynard, G. A., Kinnison, M. & Zydlewski, J. D. Size selection from fishways and potential evolutionary responses in a threatened Atlantic salmon population. River Res. Appl. 33, 1004–1015 (2017).Article 

Google Scholar 
27.Lothian, A. J. et al. Are we designing fishways for diversity? Potential selection on alternative phenotypes resulting from differential passage in brown trout. J Environ Manag 262, 110317 (2020).Article 

Google Scholar 
28.Haraldstad, T., Haugen, T. O., Kroglund, F., Olsen, E. M. & Höglund, E. Migratory passage structures at hydropower plants as potential physiological and behavioural selective agents. R. Soc. Open Sci. 6, 190 (2019).Article 

Google Scholar 
29.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: a review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).PubMed 
Article 
CAS 

Google Scholar 
30.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: heritability of personality. Proc. R. Soc. B: Biol. Sci. 282, 20142201 (2015).Article 

Google Scholar 
31.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B: Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
32.Haraldstad, T., Höglund, E., Kroglund, F., Haugen, T. O. & Forseth, T. Common mechanisms for guidance efficiency of descending Atlantic salmon smolts in small and large hydroelectric power plants. River Res. Appl. 34, 1179–1185 (2018).Article 

Google Scholar 
33.Larsen, M. H., Thorn, A. N., Skov, C. & Aarestrup, K. Effects of passive integrated transponder tags on survival and growth of juvenile Atlantic salmon Salmo salar. Anim. Biotelem. 1, 19 (2013).Article 

Google Scholar 
34.Vollset, K. W. et al. Systematic review and meta-analysis of PIT tagging effects on mortality and growth of juvenile salmonids. Rev. Fish Biol. Fish, 1–16 (2020).35.Adriaenssens, B. & Johnsson, J. I. Natural selection, plasticity and the emergence of a behavioural syndrome in the wild. Ecol. Lett. 16, 47–55 (2013).PubMed 
Article 

Google Scholar 
36.Dingemanse, N. J. et al. Behavioural syndromes differ predictably between 12 populations of three-spined stickleback. J. Anim. Ecol. 76, 1128–1138 (2007).PubMed 
Article 

Google Scholar 
37.Larsen, M. H. et al. Effects of emergence time and early social rearing environment on behaviour of Atlantic salmon: consequences for juvenile fitness and smolt migration. PLoS ONE 10, e0119127 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Castanheira, M. F., Herrera, M., Costas, B., Conceição, L. E. & Martins, C. I. Can we predict personality in fish? Searching for consistency over time and across contexts. PLoS ONE 8, e62037 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Huntingford, F. et al. Coping strategies in a strongly schooling fish, the common carp Cyprinus carpio. J. Fish Biol. 76, 1576–1591 (2010).PubMed 
Article 
CAS 

Google Scholar 
40.Brown, C., Jones, F. & Braithwaite, V. Correlation between boldness and body mass in natural populations of the poeciliid Brachyrhaphis episcopi. J. Fish Biol. 71, 1590–1601 (2007).Article 

Google Scholar 
41.R Development Core Team. R: A language and environment for statistical computing.). R Foundation for Statistical Computing (2016).42.Akaike, H. A. new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
43.Anderson, D. R. Model-Based Interference in the Life Sciences: A Primer on Evidence (Springer, 2008).MATH 
Book 

Google Scholar 
44.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).45.Brunham, A. & Anderson D, R. Model selection and multimodel inference: A practical information-theoretic approach. 2nd edn (Springer-Verlag, New York 2002).46.Fjeldstad, H. P., Alfredsen, K. & Boissy, T. Optimising Atlantic salmon smolt survival by use of hydropower simulation modelling in a regulated river. Fish. Manage. Ecol. 21, 22–31 (2014).Article 

Google Scholar 
47.Calles, O. et al. Anordning för upp- och nedströmspassage av fisk vid vattenanläggningar (2013).48.Larinier, M., Travade, F. The development and evaluation of downstream bypasses for juvenile salmonids at small hydroelectric plants in France. Innov. Fish Passage Technol. 25–42 (1999).49.Turnpenny, A. W. H., O`Keeffe, N. Screening for intake and Outfalls: a best practice guide (2005).50.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 
Article 

Google Scholar 
51.Taylor, M. K. & Cooke, S. J. Repeatability of movement behaviour in a wild salmonid revealed by telemetry. J. Fish Biol. 84, 1240–1246 (2014).PubMed 
Article 
CAS 

Google Scholar 
52.Odling-Smee, L. & Braithwaite, V. A. The role of learning in fish orientation. Fish Fish. 4, 235–246 (2003).Article 

Google Scholar 
53.Lucon-Xiccato, T., Montalbano, G. & Bertolucci, C. Personality traits covary with individual differences in inhibitory abilities in 2 species of fish. Curr. Zool. 66, 187–195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Endler, J. A. Natural Selection in the Wild (Princeton University Press, 1986).
Google Scholar 
55.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: a meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
57.Wuerz, Y. & Krüger, O. Personality over ontogeny in zebra finches: long-term repeatable traits but unstable behavioural syndromes. Front. Zool. 12, S9 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Wolf, M. & Weissing, F. J. Animal personalities: consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 
Article 

Google Scholar 
59.Cordero-Rivera, A. Behavioral diversity (ethodiversity): a neglected level in the study of biodiversity. Front. Ecol. Evol. 5, 7 (2017).ADS 
Article 

Google Scholar 
60.Biro, P. A. & Post, J. R. Rapid depletion of genotypes with fast growth and bold personality traits from harvested fish populations. Proc. Natl. Acad. Sci. 105, 2919–2922 (2008).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Uusi-Heikkilä, S., Wolter, C., Klefoth, T. & Arlinghaus, R. A behavioral perspective on fishing-induced evolution. Trends Ecol. Evol. 23, 419–421 (2008).PubMed 
Article 

Google Scholar 
62.Cooke, S. J., Suski, C. D., Ostrand, K. G., Wahl, D. H. & Philipp, D. P. Physiological and behavioral consequences of long-term artificial hselection for vulnerability to recreational angling in a teleost fish. Physiol. Biochem. Zool. 80, 480–490 (2007).PubMed 
Article 

Google Scholar  More

Host and mutual symbionts decline at different rates following consecutive cyclones and bleachingBefore and after disturbances, we surveyed Acropora corals known to host Gobiodon coral gobies along line (30 m) and cross (two 4-m by 1-m belt) transects. In February 2014, prior to cyclones and bleaching events, most of these Acropora corals were inhabited by Gobiodon coral gobies. Gobies were not found in corals under 7-cm average diameter, therefore we only sampled bigger corals. The vast majority of transects (95%) had Acropora corals. On average there were 3.24 ± 0.25 (mean ± standard error) Acropora coral species per transect (Fig. 2a) and a total of 17 species were observed among all 2014 transects. Average coral diameter was 25.4 ± 1.0 cm (Fig. 2b), with some corals reaching over 100 cm. Only 4.1 ± 1.4% of corals lacked any goby inhabitants (Fig. 2c). On average there were 3.37 ± 0.26 species of gobies per transect (Fig. 2d) and a total of 13 species among all 2014 transects. In each occupied coral there were 2.20 ± 0.14 gobies (Fig. 2e), with a maximum of 11 individuals of the same species.Figure 2Effects of consecutive climate disturbances on coral and goby populations. Changes in Acropora (a) richness (n = 279), and (b) average diameter (n = 244), (c) percent goby occupancy (n = 244) and Gobiodon (d) richness (n = 279), and (e) group size (n = 230) per transect (n = sample size per variable) before and after each cyclone (black cyclone symbols) and after two consecutive heatwaves/bleaching events (white coral symbols) around Lizard Island, Great Barrier Reef, Australia. Error bars are standard error. Fish and coral symbols above each graph illustrate the change in means for each variable among sampling events from post-hoc tests. Figures were illustrated in R (v3.5.2)33 and Microsoft Office PowerPoint 2016.Full size imageIn January–February 2015, 9 months after Cyclone Ita (category 4) struck from the north (Supplementary Fig. 1), follow-up surveys revealed no changes to coral richness (p = 0.986, see Supplementary Table 1 for all statistical outputs) relative to February 2014, but corals were 19% smaller (p  More

1.Mitchard, E. T. A., Saatchi, S. S., Gerard, F. F., Lewis, S. L. & Meir, P. Measuring woody encroachment along a forest-savanna boundary in Central Africa. Earth Interact. 13, 1–29 (2009).Article 

Google Scholar 
2.Murphy, B. P. & Bowman, D. M. J. S. What controls the distribution of tropical forest and savanna?. Ecol. Lett. 15, 748–758 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Staver, A. C., Archibald, S. & Levin, S. Tree cover in sub-Saharan Africa: Rainfall and fire constrain forest and savanna as alternative stable states. Ecology 92, 1063–1072 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Bowman, D. M. J. S., Murphy, B. P. & Banfai, D. S. Has global environmental change caused monsoon rainforests to expand in the Australian monsoon tropics?. Landsc. Ecol. 25, 1247–1260 (2010).Article 

Google Scholar 
5.Favier, C., Namur, C. D. & Dubois, M. F. Forest progression modes in littoral Congo, central atlantic Africa. J. Biogeogr. 31, 1445–1461 (2004).Article 

Google Scholar 
6.Puyravaud, J. P., Dufour, C. & Aravajy, S. Rain forest expansion mediated by successional processes in vegetation thickets in the Western Ghats of India. J. Biogeogr. 30, 1067–1080 (2003).Article 

Google Scholar 
7.Tng, D. Y. P. et al. Humid tropical rain forest has expanded into eucalypt forest and savanna over the last 50 years. Ecol. Evol. 2, 34–45 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Mariano, V., Rebolo, I. F. & Christianini, A. V. Fire sensitive species dominate seed rain after fire supression: implications for plant community diversity and woody encroachment in the Cerrado. Biotropica 51, 5–9 (2019).Article 

Google Scholar 
9.Ferreira, A. V., Bruna, E. M. & Vasconcelos, H. L. Seed predators limit plant recruitment in neotropical savannas. Oikos 120, 1013–1022 (2010).Article 

Google Scholar 
10.Azihou, A. F., Glèlè Kakaï, R. & Sinsin, B. Do isolated gallery-forest trees facilitate recruitment of forest seedlings and saplings in savannna?. Acta Oecol. 53, 11–18 (2013).ADS 
Article 

Google Scholar 
11.Duarte, L. D. S., Dos-Santos, M. M. G., Hartz, S. M. & Pillar, V. D. Role of nurse plants in Araucaria Forest expansion over grassland in south Brazil. Austral Ecol. 31, 520–528 (2006).Article 

Google Scholar 
12.Hoffmann, W. A. The Effects of Fire and Cover on Seedling Establishment in a Neotropical Savanna. J. Ecol. 84, 383–393 (1996).Article 

Google Scholar 
13.Hoffmann, W. A., Orthen, B. & Franco, A. C. Constraints to seedling success of savanna and forest trees across the savanna-forest boundary. Oecologia 140, 252–260 (2004).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Lawes, M. J., Murphy, B. P., Midgley, J. J. & Russell-Smith, J. Are the eucalypt and non-eucalypt components of Australian tropical savannas independent?. Oecologia 166, 229–239 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Russell-Smith, J., Stanton, P. J., Whitehead, P. J. & Edwards, A. Rain forest invasion of eucalypt-dominated woodland savanna, iron range, north-eastern Australia: I. Successional processes. J. Biogeogr. 31, 1293–1303 (2004).Article 

Google Scholar 
16.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
17.Callaway, R. M. Positive interactions among plants. Bot. Rev. 61, 306–349 (1995).Article 

Google Scholar 
18.Slocum, M. G. & Horvitz, C. C. Seed arrival under different genera of trees in a neotropical pasture. Plant Ecol. 149, 51–62 (2000).Article 

Google Scholar 
19.Schlawin, J. R. & Zahawi, R. A. ‘Nucleating’ succession in recovering neotropical wet forests: the legacy of remnant trees. J. Veg. Sci. 19, 485–492 (2008).Article 

Google Scholar 
20.Slocum, M. G. How tree species differ as recruitment foci in a tropical pasture. Ecology 82, 2547–2559 (2001).Article 

Google Scholar 
21.Fujita, T. Ficus natalensis facilitates the establishment of a montane rain-forest tree in south-east African tropical woodlands. J. Trop. Ecol. 30, 303–310 (2014).Article 

Google Scholar 
22.de Dantas, V. L. et al. Plant dispersal strategies and the colonization of araucaria forest patches in a grassland-forest mosaic. J. Veg. Sci. 18, 847–858 (2007).Article 

Google Scholar 
23.Campbell, B., Frost, P. & Byron, N. Miombo woodlands and their use: overview and key issues. In The Miombo in transition: woodlands and welfare in Africa (ed. Campbell, B.) 1–5 (Center for International Forestry Research, 1996).
Google Scholar 
24.White, F., Dowsett-Lemaire, F. & Chapman, S. Evergreen Forest Flora of Malawi (Royal Botanic Gardens, 2001).
Google Scholar 
25.Chapman, J. D. PART II Description of the forest. In The evergreen forests of Malawi (eds. Chapman, J. D. & White, F.) 113–180 (Commonwealth Forestry Institute, 1970).
Google Scholar 
26.Hoffmann, W. A. et al. Ecological thresholds at the savanna-forest boundary: how plant traits, resources and fire govern the distribution of tropical biomes. Ecol. Lett. 15, 759–768 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Frazer, G. W., Canham, C. D., & Lertzman, K. P. Gap Light Analyzer (GLA) 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs. http://remmain.rem.sfu.ca/downloads/Forestry/GLAV2UsersManual.pdf (1999).28.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
29.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
30.Trauernicht, C., Murphy, B. P., Portner, T. E. & Bowman, D. M. J. S. Tree cover-fire interactions promote the persistence of a fire-sensitive conifer in a highly flammable savanna. J. Ecol. 100, 958–968 (2012).Article 

Google Scholar 
31.Castro, J., Zamora, R., Hódar, J. A. & Gómez, J. M. Seedling establishment of a boreal tree species (Pinus sylvestris) at its southernmost distribution limit: consequences of being in a marginal Mediterranean habitat. J. Ecol. 92, 266–277 (2004).Article 

Google Scholar 
32.Gómez-Aparicio, L., Gómez, J. M., Zamora, R. & Boettinger, J. L. Canopy vs. soil effects of shrubs facilitating tree seedlings in Mediterranean montane ecosystems. J. Veg. Sci. 16, 191–198 (2005).Article 

Google Scholar 
33.Smit, C., Den Ouden, J. & Díaz, M. Facilitation of Quercus ilex recruitment by shrubs in Mediterranean open woodlands. J. Veg. Sci. 19, 193–200 (2008).Article 

Google Scholar 
34.Rao, S. J., Iason, G. R., Hulbert, I. A. R., Elston, D. A. & Racey, P. A. The effect of sapling density, heather height and season on browsing by mountain hares on birch. J. Appl. Ecol. 40, 626–638 (2003).Article 

Google Scholar 
35.de Dantas, V. L., Hirota, M., Oliveira, R. S. & Pausas, J. G. Disturbance maintains alternative biome states. Ecol. Lett. 19, 12–19 (2016).Article 

Google Scholar 
36.Terborgh, J. et al. Megafaunal influences on tree recruitment in African equatorial forests. Ecography 39, 180–186 (2016).Article 

Google Scholar 
37.Ripple, R. et al. Bushmeat hunting and extinction risk to the world’s mammals. R. Soc. Open Sci. 3, 160498. https://doi.org/10.1098/rsos.160498 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Hegerl, C., Burgess, N., Nielsen, M., Martin, E., Ciolli, M., & Rovero, F. Using camera trap data to assess the impact of bushmeat hunting on forest mammals in Tanzania. Oryx 51(1), 87–97 (2017).Article 

Google Scholar 
39.Bowman, D. M. J. S. & Panton, W. J. Factors that control monsoon-rainforest seedling establishment and growth in North Australian Eucalyptus Savanna. J. Ecol. 81, 297–304 (1993).Article 

Google Scholar 
40.Ruggiero, C. P. G., Batalha, M. A., Pivello, V. R. & Meirelles, S. T. Soil-vegetation relationships in cerrado (Brazilian savanna) and semideciduous forest, Southeastern Brazil. Plant Ecol. 160, 1–16 (2002).Article 

Google Scholar 
41.Viani, R. A. G., Rodrigues, R. R., Dawson, T. E. & Oliveira, R. S. Savanna soil fertility limits growth but not survival of tropical forest tree seedlings. Plant Soil 349, 341–353 (2011).CAS 
Article 

Google Scholar 
42.Chen, J. et al. Soil nutrient availability determines the facilitative effects of cushion plants on other plant species at high elevations in the south-eastern Himalayas. Plant Ecolog. Divers. 8, 199–210 (2015).Article 

Google Scholar 
43.Zahawi, R. A., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50(2013), 88–96 (2013).Article 

Google Scholar 
44.Clark, C. J., Poulsen, J. R., Connor, E. F. & Parker, V. T. Fruiting trees as dispersal foci in a semi-deciduous tropical forest. Oecologia 139, 66–75 (2004).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Carlo, T. A. & Aukema, J. E. Female-directed dispersal and facilitation between a tropical mistletoe and a dioecious host. Ecology 86, 3245–3251 (2005).Article 

Google Scholar 
46.Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

Laris also notes that people in West Africa overwhelmingly set early dry season (EDS) fires. This is true for Burkina Faso, Senegal, Benin, Togo, Ghana, which all have an early burning pattern (See Table 1). However, this is not the case for Nigeria, Sierra Leone and Guinea-Bissau, which have most emissions in the late dry season (LDS) (see Table 1). Also, if we sum the total EDS and LDS emissions for West African Countries, then 45% of emissions occur in the EDS and 55% in the late dry season (see Table 1). The total West African contribution is around 8% of the total African savanna emissions—a relatively small contributor.We haven’t suggested that the early burning practise would work for all of West Africa, but the evidence suggests that it would work for Nigeria, Sierra Leone and Guinea-Bissau (see Table 1). We agree, many of the West African countries have significant EDS burning patterns like Burkina Faso, Senegal, Benin, Togo and Ghana and would not benefit from the approach. However, for those countries with significant EDS burning that still have significant LDS emissions as well, such as Mali and Côte d’Ivoire, there may be some opportunity for further emissions reductions through improved fire management practices as presented in our paper3.Laris1 also points out that the same EDS regime proposed is one that was developed by indigenous people and that it has been applied by Africans for centuries. The same is true for Australia, but colonial occupation altered that, as it has in some areas of Africa. A new incentive in the form of carbon payments for early burning in Australia has empowered local indigenous people to reconnect to their traditional lands and fulfil their cultural obligations and a diversity burning practices14. More

1.Chesson, P. General theory of competitive coexistence in spatially-varying environments. Theor. Popul. Biol. 58, 211–237 (2000).2.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).3.Ellner, S. P., Snyder, R. E., Adler, P. B. & Hooker, G. An expanded modern coexistence theory for empirical applications. Ecol. Lett. 22, 3–18 (2019).4.Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).5.Costantino, R. F., Cushing, J. M., Dennis, B. & Desharnais, R. A. Experimentally induced transitions in the dynamic behaviour of insect populations. Nature 375, 227–230 (1995).6.Fussmann, G. F., Ellner, S. P., Shertzer, K. W. & Hairston, N. G. Jr. Crossing the Hopf bifurcation in a live predator-prey system. Science 290, 1358–1360 (2000).7.Dalziel, B. D. et al. Persistent chaos of measles epidemics in the prevaccination United States caused by a small change in seasonal transmission patterns. PLoS Comput. Biol. 12, e1004655 (2016).8.Darwin, C. On the Origin of Species by Means of Natural Selection, or The Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).9.Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).10.Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).Article 

Google Scholar 
11.Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994).Article 

Google Scholar 
12.McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).13.Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).14.Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).15.May, R. M. Host-parasitoid systems in patchy environments: a phenomenological model. J. Anim. Ecol. 47, 833–844 (1978).Article 

Google Scholar 
16.Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid–host and predator–prey models: a review. Theor. Popul. Biol. 65, 299–315 (2004).17.Vicsek, T. & Zafeiris, A. Collective motion. Phys. Rep. 517, 71–140 (2012).Article 

Google Scholar 
18.Berdahl, A., Torney, C. J., Ioannou, C. C., Faria, J. J. & Couzin, I. D. Emergent sensing of complex environments by mobile animal groups. Science 339, 574–576 (2013).19.Nagy, M., Akos, Z., Biro, D. & Vicsek, T. Hierarchical group dynamics in pigeon flocks. Nature 464, 890–893 (2010).20.Dalziel, B. D., Corre, M. L., Côté, S. D. & Ellner, S. P. Detecting collective behaviour in animal relocation data, with application to migrating caribou. Methods Ecol. Evol. 7, 30–41 (2015).Article 

Google Scholar 
21.Torney, C. J. et al. Inferring the rules of social interaction in migrating caribou. Phil. Trans. R. Soc. B 373, 20170385 (2018).22.Fryxell, J. M., Mosser, A., Sinclair, A. R. E. & Packer, C. Group formation stabilizes predator–prey dynamics. Nature 449, 1041–1043 (2007).23.Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I. & Shochet, O. Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75, 1226 (1995).CAS 
Article 

Google Scholar 
24.Buhl, J. et al. From disorder to order in marching locusts. Science 312, 1402–1406 (2006).25.King, A. J., Fehlmann, G., Biro, D., Ward, A. J. & Fürtbauer, I. Re-wilding collective behaviour: an ecological perspective. Trends Ecol. Evol. 33, 347–357 (2018).26.Sumpter, D. J. T. Collective Animal Behavior (Princeton Univ. Press, 2010).27.Guttal, V. & Couzin, I. D. Social interactions, information use, and the evolution of collective migration. Proc. Natl Acad. Sci. USA 107, 16172–16177 (2010).CAS 
Article 

Google Scholar 
28.Barbier, M. & Watson, J. R. The spatial dynamics of predators and the benefits and costs of sharing information. PLoS Comput. Biol. 12, e1005147 (2016).29.Lotka, A. J. Analytical note on certain rhythmic relations in organic systems. Proc. Natl Acad. Sci. USA 6, 410–415 (1920).CAS 
Article 

Google Scholar 
30.Rosenzweig, M. L. & MacArthur, R. H. Graphical representation and stability conditions of predator-prey interactions. Am. Nat. 97, 209–223 (1963).Article 

Google Scholar 
31.Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).32.Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).33.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).Article 

Google Scholar 
34.Dalziel, B. D., Thomann, E., Medlock, J. & De Leenheer, P. Global analysis of a predator-prey model with variable predator search rate. J. Math. Biol. 81, 159–183 (2020).35.Lukas, D. & Clutton-Brock, T. Social complexity and kinship in animal societies. Ecol. Lett. 21, 1129–1134 (2018).36.Purves, D. W., Lichstein, J. W., Strigul, N. & Pacala, S. W. Predicting and understanding forest dynamics using a simple tractable model. Proc. Natl Acad. Sci. USA 105, 17018–17022 (2008).CAS 
Article 

Google Scholar 
37.Dalziel, B. D. et al. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 362, 75–79 (2018).38.Monk, C. T. et al. How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs. Ecol. Lett. 21, 779–793 (2018).39.Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058 (2017).40.Zakem, E. J. et al. Ecological control of nitrite in the upper ocean. Nat. Commun. 9, 1206 (2018).41.Axtell, R. L. Zipf distribution of U.S. firm sizes. Science 293, 1818–1820 (2001).42.Turchin, P. et al. Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization. Proc. Natl Acad. Sci. USA 115, E144–E151 (2018).CAS 
Article 

Google Scholar 
43.Press, W. H. Numerical Recipes in C (Cambridge Univ. Press, 1986). More

1.Lewis, S. L. & Maslin, M. A. Defining the anthropocene. Nature 519, 171–180 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Pinto-Ledezma, J. N. & Rivero Mamani, M. L. Temporal patterns of deforestation and fragmentation in lowland Bolivia: Implications for climate change. Clim. Change 127, 43–54 (2014).ADS 
Article 

Google Scholar 
4.Allen, J. M., Folk, R. A., Soltis, P. S., Soltis, D. E. & Guralnick, R. P. Biodiversity synthesis across the green branches of the tree of life. Nat. Plants 5, 11–13 (2019).PubMed 
Article 

Google Scholar 
5.Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Accessed 15 Feb 2021. https://zenodo.org/record/3553579. https://doi.org/10.5281/ZENODO.3553579 (2019). 6.Cavender-Bares, J., Balvanera, P., King, E. & Polasky, S. Ecosystem service trade-offs across global contexts and scales. Ecol. Soc. 20, art22 (2015).Article 

Google Scholar 
7.Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
CAS 
PubMed 

Google Scholar 
9.Watson, J. E. M. et al. Set a global target for ecosystems. Nature 578, 360–362 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity models: What if unsaturation is the rule?. Trends Ecol. Evol. 32, 556–566 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
12.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).Article 

Google Scholar 
14.Guisan, A. & Rahbek, C. SESAM—A new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages: Predicting spatio-temporal patterns of species assemblages. J. Biogeogr. 38, 1433–1444 (2011).Article 

Google Scholar 
15.Cavender-Bares, J., Schweiger, A. K., Pinto-Ledezma, J. N. & Meireles, J. E. Applying remote sensing to biodiversity science. in Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J. et al.) 13–42 (Springer, 2020). https://doi.org/10.1007/978-3-030-33157-3_2.Chapter 

Google Scholar 
16.Fawcett, D. et al. Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling. Remote Sens. Environ. 204, 583–595 (2018).ADS 
Article 

Google Scholar 
17.Randin, C. F. et al. Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens. Environ. 239, 111626 (2020).ADS 
Article 

Google Scholar 
18.Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.D’Amen, M., Pradervand, J.-N. & Guisan, A. Predicting richness and composition in mountain insect communities at high resolution: A new test of the SESAM framework: Community-level models of insects. Glob. Ecol. Biogeogr. 24, 1443–1453 (2015).Article 

Google Scholar 
20.Pottier, J. et al. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients: Climate and species assembly predictions. Glob. Ecol. Biogeogr. 22, 52–63 (2013).Article 

Google Scholar 
21.Zurell, D. et al. Testing species assemblage predictions from stacked and joint species distribution models. J. Biogeogr. 47, 101–113 (2020).Article 

Google Scholar 
22.D’Amen, M. et al. Improving spatial predictions of taxonomic, functional and phylogenetic diversity. J. Ecol. 106, 76–86 (2018).Article 

Google Scholar 
23.Dobrowski, S. Z. et al. Modeling plant ranges over 75 years of climate change in California, USA: Temporal transferability and species traits. Ecol. Monogr. 81, 241–257 (2011).Article 

Google Scholar 
24.Soria-Auza, R. W. et al. Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: A case study from Bolivia. Ecol. Model. 221, 1221–1229 (2010).Article 

Google Scholar 
25.Rocchini, D. et al. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sens. Ecol. Conserv. 2, 25–36 (2016).Article 

Google Scholar 
26.Schulte to Bühne, H. & Pettorelli, N. Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science. Methods Ecol. Evol. 9, 849–865 (2018).Article 

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

Google Scholar 
28.Hobi, M. L. et al. A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness. Remote Sens. Environ. 195, 142–152 (2017).ADS 
Article 

Google Scholar 
29.Radeloff, V. C. et al. The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity. Remote Sens. Environ. 222, 204–214 (2019).ADS 
Article 

Google Scholar 
30.Pinto-Ledezma, J. N. & Cavender-Bares, J. Using remote sensing for modeling and monitoring species distributions. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 199–223 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_9.31.Fernández, N., Ferrier, S., Navarro, L. M. & Pereira, H. M. Essential biodiversity variables: Integrating in-situ observations and remote sensing through modeling. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 485–501 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_18.32.Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01451-x (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Saatchi, S., Buermann, W., ter Steege, H., Mori, S. & Smith, T. B. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens. Environ. 112, 2000–2017 (2008).ADS 
Article 

Google Scholar 
34.He, K. S. et al. Will remote sensing shape the next generation of species distribution models?. Remote Sens. Ecol. Conserv. 1, 4–18 (2015).Article 

Google Scholar 
35.Cord, A. F., Meentemeyer, R. K., Leitão, P. J. & Václavík, T. Modelling species distributions with remote sensing data: Bridging disciplinary perspectives. J. Biogeogr. 40, 2226–2227 (2013).Article 

Google Scholar 
36.Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).PubMed 
Article 

Google Scholar 
37.Scherrer, D., D’Amen, M., Fernandes, R. F., Mateo, R. G. & Guisan, A. How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods Ecol. Evol. 9, 2155–2166 (2018).Article 

Google Scholar 
38.Cavender-Bares, J. Diversification, adaptation, and community assembly of the American oaks (Quercus), a model clade for integrating ecology and evolution. New Phytol. 221, 669–692 (2019).PubMed 
Article 

Google Scholar 
39.Cavender-Bares, J., Ackerly, D. D., Baum, D. A. & Bazzaz, F. A. Phylogenetic overdispersion in Floridian oak communities. Am. Nat. 163, 823–843 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Cavender-Bares, J. et al. The role of diversification in community assembly of the oaks (Quercus L.) across the continental U.S. Am. J. Bot. 105, 565–586 (2018).PubMed 
Article 

Google Scholar 
41.Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: The once and future niche. Proc. Natl. Acad. Sci. 106, 19651–19658 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Townsend Peterson, A. et al. Ecological Niches and Geographic Distributions. (Princeton University Press, 2011). Book 

Google Scholar 
43.Cavender-Bares, J., Fontes, G. C. & Pinto-Ledezma, J. Open questions in understanding the adaptive significance of plant functional trait variation within a single lineage. New Phytol. https://doi.org/10.1111/nph.16652 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Cavender-Bares, J., Kitajima, K. & Bazzaz, F. A. Multiple trait associations in relation to habitat differentiation among 17 Floridian oak species. Ecol. Monogr. 74, 635–662 (2004).Article 

Google Scholar 
45.Menges, E. S. & Hawkes, C. V. Interactive effects of fire and microhabitat on plants of Florida scrub. Ecol. Appl. 8, 935–946 (1998).Article 

Google Scholar 
46.Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models: Stacking species distribution models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).Article 

Google Scholar 
47.Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl. Acad. Sci. 104, 13384–13389 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Pinto-Ledezma, J. N., Jahn, A. E., Cueto, V. R., Diniz-Filho, J. A. F. & Villalobos, F. Drivers of phylogenetic assemblage structure of the Furnariides, a widespread clade of lowland neotropical birds. Am. Nat. 193, E41–E56 (2019).PubMed 
Article 

Google Scholar 
49.Gamon, J. A. et al. Consideration of scale in remote sensing of biodiversity. In Remote Sensing of Plant Biodiversity (eds. Cavender-Bares, J., Gamon, J. A. & Townsend, P. A.) 425–447 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-33157-3_16.50.Pollock, L. J. et al. Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol. Evol. 5, 397–406 (2014).Article 

Google Scholar 
51.Ovaskainen, O. et al. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol. Lett. 20, 561–576 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Ovaskainen, O. Joint Species Distribution Modelling: with Applications in R (Cambridge University Press, 2020).Book 

Google Scholar 
53.Poggiato, G. et al. On the interpretations of joint modeling in community ecology. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.01.002 (2021).Article 
PubMed 

Google Scholar 
54.Wilkinson, D. P., Golding, N., Guillera-Arroita, G., Tingley, R. & McCarthy, M. A. Defining and evaluating predictions of joint species distribution models. Methods Ecol. Evol. 12, 394–404 (2021).Article 

Google Scholar 
55.Bystrova, D. et al. Clustering species with residual covariance matrix in joint species distribution models. Front. Ecol. Evol. 9, 601384 (2021).Article 

Google Scholar 
56.Mateo, R. G. et al. Hierarchical species distribution models in support of vegetation conservation at the landscape scale. J. Veg. Sci. 30, 386–396 (2019).ADS 
Article 

Google Scholar 
57.Petitpierre, B. et al. Will climate change increase the risk of plant invasions into mountains?. Ecol. Appl. 26, 530–544 (2016).PubMed 
Article 

Google Scholar 
58.Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).PubMed 
Article 

Google Scholar 
59.Schweiger, A. K. et al. Spectral Niches Reveal Taxonomic Identity and Complementarity in Plant Communities. (2020) https://doi.org/10.1101/2020.04.24.060483. 60.Cavender-Bares, J. et al. Associations of leaf spectra with genetic and phylogenetic variation in oaks: Prospects for remote detection of biodiversity. Remote Sens. 8, 221 (2016).ADS 
Article 

Google Scholar 
61.Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. New Phytol. 228, 485–493 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
62.Williams, L. J. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat. Ecol. Evol. 5, 46–54 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Alonso, K. et al. Data products, quality and validation of the DLR earth sensing imaging spectrometer (DESIS). Sensors 19, 4471 (2019).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
64.Stavros, E. N. et al. ISS observations offer insights into plant function. Nat. Ecol. Evol. 1, 0194 (2017).Article 

Google Scholar 
65.Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Cavender-Bares, J. et al. BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world. Res. Ideas Outcomes 7, e63850 (2021).Article 

Google Scholar 
67.Hipp, A. L. et al. Sympatric parallel diversification of major oak clades in the Americas and the origins of Mexican species diversity. New Phytol. 217, 439–452 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Cavender-Bares, J. et al. Phylogeny and biogeography of the American live oaks (Quercus subsection Virentes): A genomic and population genetics approach. Mol. Ecol. 24, 3668–3687 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

Google Scholar 
70.Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114 (2010).Article 

Google Scholar 
71.Barnett, D. T. et al. The plant diversity sampling design for The National Ecological Observatory Network. Ecosphere 10, e02603 (2019).
Google Scholar 
72.Deblauwe, V. et al. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics: Remotely sensed climate data for tropical species distribution models. Glob. Ecol. Biogeogr. 25, 443–454 (2016).Article 

Google Scholar 
73.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. R package version 1.3. https://CRAN.R-project.org/package=dismo (2020).74.Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).ADS 
Article 

Google Scholar 
75.Gower, S. T., Kucharik, C. J. & Norman, J. M. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ. 70, 29–51 (1999).ADS 
Article 

Google Scholar 
76.Reich, P. B. Key canopy traits drive forest productivity. Proc. R. Soc. B Biol. Sci. 279, 2128–2134 (2012).Article 

Google Scholar 
77.Xiao, Z. et al. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens. 52, 209–223 (2014).ADS 
Article 

Google Scholar 
78.Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).PubMed 
Article 

Google Scholar 
79.Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).ADS 
Article 

Google Scholar 
80.Dubuis, A. et al. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approaches: Predicting plant species richness. Divers. Distrib. 17, 1122–1131 (2011).Article 

Google Scholar 
81.Schoener, T. W. Anolis lizards of Bimini: Resource partition in a complex fauna. Ecology 49, 704–726 (1968).Article 

Google Scholar 
82.Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
83.Cooper, J. C. & Soberón, J. Creating individual accessible area hypotheses improves stacked species distribution model performance. Glob. Ecol. Biogeogr. 27, 156–165 (2018).Article 

Google Scholar 
84.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many? How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
85.Carlson, C. J. et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat. Microbiol. 4, 1337–1343 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Chipman, H. A., George, E. I. & McCulloch, R. E. BART: Bayesian additive regression trees. Ann. Appl. Stat. 4, 266–298 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
87.Yen, J. D. L., Thomson, J. R., Vesk, P. A. & Mac Nally, R. To what are woodland birds responding? Inference on relative importance of in-site habitat variables using several ensemble habitat modelling techniques. Ecography 34, 946–954 (2011).Article 

Google Scholar 
88.Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).Article 

Google Scholar 
89.Dorie, V. dbarts: Discrete Bayesian Additive Regression Trees Sampler. (2020).90.Hastie, T. & Tibshirani, R. Bayesian backfitting. Stat. Sci. 15(3), 196–223 (2000). MathSciNet 
MATH 
Article 

Google Scholar 
91.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS): Assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
92.Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
93.Hipp, A. L. et al. Genomic landscape of the global oak phylogeny. New Phytol. 226, 1198–1212 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

Google Scholar 
95.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 

Google Scholar 
96.Kruschke, J. Doing Bayesian Data Analysis, 2nd Ed. (2014).97.Mills, J. A. & Parent, O. Bayesian MCMC estimation. In Handbook of Regional Science (eds Fischer, M. M. & Nijkamp, P.) 1571–1595 (Springer, Berlin, 2014). https://doi.org/10.1007/978-3-642-23430-9_89.Chapter 

Google Scholar 
98.Carpenter, B. et al. Stan : A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).Article 

Google Scholar 
99.Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. (2020).100.Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
101.Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).ADS 
Article 

Google Scholar  More