Philippa Kaur

The growth behaviour of Linnemannia is strain-specificMost strains showed comparable morphological characteristics on both media as well as in pure and co-culture. However, Linnemannia solitaria and Entomortierella galaxiae produced more aerial mycelium on PDA compared to LcA. There was more/less aerial mycelium in co-cultures with P. helmanticensis compared to pure cultures depending on the strain (Fig. 1, SI Fig. S3).The comparison of Linnemannia and E. galaxiae daily radial growth rates did not support a difference between these genera (p ≥ 0.3). The overall linear model indicated that the fungal daily growth rates mainly differed among species (Table 1). In addition, the effect of strains highlighted the heterogeneity among strains within species (Fig. 2, SI Figs. S4, S5). Although there was no relevant main effect of medium on the daily radial growth rate of the fungi, the medium did affect the fungi in a strain-specific manner (Table 1, Fig. 2, SI Figs. S4, S5). On nutrient poor LcA, the fungal daily radial growth rates were reduced for all species, except for L. solitaria, which grew better on LcA (SI Figs. S3, S4).Table 1 The effect of experimental factors on the fungal daily radial growth rate.Full size tableFigure 2Daily radial growth rate of pure Linnemannia and Entomortierella cultures as well as co-cultures with P. helmanticensis on nutrient rich PDA medium. (a) L. exigua, (b) L. gamsii, (c) L. hyalina, (d) L. sclerotiella, (e) L. solitaria, (f) E. galaxiae.Full size imageThe main effect of co-plating P. helmanticensis on radial growth rate was small, yet significant (0.7%, p  More

Data reportingThe data underpinning this study is a compilation of existing datasets and therefore, no statistical methods were used to predetermine sample size, the experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. The measurements were taken from distinct samples, repeated measurements from the same sites were averaged in the main analysis.Inclusion & ethicsData were primarily collected from individual archives of contributing co-authors. The data collection initiative was openly announced via the mailing list of the 10th International Seminar on Apterygota and via social media (Twitter, Researchgate). In addition, colleagues from less explored regions (Africa, South America) were contacted via personal networks of the initial authors group and literature search. All direct data providers who collected and standardised the data were invited as co-authors with defined minimum role (data provision and cleaning, manuscript editing and approval). For unpublished data, people who were directly involved in sorting and identification of springtails, including all local researchers, were invited as co-authors. Principal investigators were normally not included as co-authors, unless they contributed to conceptualisation and writing of the manuscript. All co-authors were informed and invited to contribute throughout the research process—from the study design and analysis to writing and editing. The study provided an inclusive platform for researchers around the globe to network, share and test their research ideas.Data acquisitionBoth published and unpublished data were collected, using raw data whenever possible entered into a common template. In addition, data available from Edaphobase47 was included. The following minimum set of variables was collected: collectors, collection method (including sampling area and depth), extraction method, identification precision and resources, collection date, latitude and longitude, vegetation type (generalized as grassland, scrub, woodland, agriculture and other for the analysis), and abundances of springtail taxa found in each soil sample (or sampling site). Underrepresented geographical areas (Africa, South America, Australia and Southeast Asia) were specifically targeted by a literature search in the Web of Science database using the keywords ‘springtail’ or ‘Collembola’, ‘density’ or ‘abundance’ or ‘diversity’, and the region of interest; data were acquired from all found papers if the minimum information listed above was provided. All collected datasets were cleaned using OpenRefine v3.3 (https://openrefine.org) to remove inconsistencies and typos. Geographical coordinates were checked by comparing the dataset descriptions with the geographical coordinates. In total, 363 datasets comprising 2783 sites were collected and collated into a single dataset (Supplementary Fig. 1).Calculation of community parametersCommunity parameters were calculated at the site level. Here, we defined a site as a locality that hosts a defined springtail community, is covered by a certain vegetation type, with a certain management, and is usually represented by a sampling area of up to a hundred metres in diameter, making species co-occurrence and interactions plausible. To calculate density, numerical abundance in all samples was averaged and recalculated per square metre using the sampling area. Springtail communities were assessed predominantly during active vegetation periods (i.e., spring, summer and autumn in temperate and boreal biomes, and summer in polar biomes). Our estimations of community parameters therefore refer to the most favourable conditions (peak yearly densities). This seasonal sampling bias is likely to have little effect on our conclusions, since most springtails survive during cold periods38,48. Finally, we used mean annual soil temperatures49 to estimate the seasonal mean community metabolism (described below) and tested for the seasonal bias in additional analysis (see Linear mixed-effects models).All data analyses were conducted in R v. 4.0.250 with RStudio interface v. 1.4.1103 (RStudio, PBC). Data was transformed and visualised with tidyverse packages51,52, unless otherwise mentioned. Background for the global maps was acquired via the maps package53,54. To calculate local species richness, we used data identified to species or morphospecies level (validated by the expert team). Since the sampling effort varied among studies, we extrapolated species richness using rarefaction curves based on individual samples with the Chao estimator51,52 in the vegan package53. For some sites, sample-level data were not available in the original publications, but site-level averages were provided, and an extensive sampling effort was made. In such cases, we predicted extrapolated species richness based on the completeness (ratio of observed to extrapolated richness) recorded at sites where sample-level data were available (only sites with 5 or more samples were used for the prediction). We built a binomial model to predict completeness in sites where no sample-level data were available using latitude and the number of samples taken at a site as predictors: glm(Completeness~N_samples*Latitude). We found a positive effect of the number of samples (Chisq = 1.97, p = 0.0492) and latitude (Chisq = 2.07, p = 0.0391) on the completeness (Supplementary Figs. 17–19). We further used this model to predict extrapolated species richness on the sites with pooled data (435 sites in Europe, 15 in Australia, 6 in South America, 4 in Asia, and 3 in Africa).To calculate biomass, we first cross-checked all taxonomic names with the collembola.org checklist55 using fuzzy matching algorithms (fuzzyjoin R package56) to align taxonomic names and correct typos. Then we merged taxonomic names with a dataset on body lengths compiled from the BETSI database57, a personal database of Matty P. Berg, and additional expert contributions. We used average body lengths for the genus level (body size data on 432 genera) since data at the species level were not available for many morphospecies (especially in tropical regions), and species within most springtail genera had similar body size ranges. Data with no genus-level identifications were excluded from the analysis. Dry and fresh body masses were calculated from body length using a set of group-specific length-mass regressions (Supplementary Table 1)58,59 and the results of different regressions applied to the same morphogroup were averaged. Dry mass was recalculated to fresh mass using corresponding group-specific coefficients58. We used fresh mass to calculate individual metabolic rates60 and account for the mean annual topsoil (0–5 cm) temperature at a given site61. Group-specific metabolic coefficients for insects (including springtails) were used for the calculation: normalization factor (i0) ln(21.972) [J h−1], allometric exponent (a) 0.759, and activation energy (E) 0.657 [eV]60. Community-weighted (specimen-based) mean individual dry masses and metabolic rates were calculated for each sample and then averaged by site after excluding 10% of maximum and 10% of minimum values to reduce impact of outliers. To calculate site-level biomass and community metabolism, we summed masses or metabolic rates of individuals, averaged them across samples, and recalculated them per unit area (m2).Parameter uncertaintiesOur biomass and community metabolism approximations contain several assumptions. To account for the uncertainty in the length-mass and mass-metabolism regression coefficients, in addition to the average coefficients, we also used maximum (average + standard error) and minimum coefficients (average—standard error; Supplementary Table 1) in all equations to calculate maximum and minimum estimations of biomass and community metabolism reported in the main text. Further, we ignored latitudinal variation in body sizes within taxonomic groups62. Nevertheless, latitudinal differences in springtail density (30-fold), environmental temperature (from −16.0 to +27.6 °C in the air and from −10.2 to +30.4 °C in the soil), and genus-level community compositions (there are only few common genera among polar regions and the tropics)55 are higher than the uncertainties introduced by indirect parameter estimations, which allowed us to detect global trends. Although most springtails are concentrated in the litter and uppermost soil layers20, their vertical distribution depends on the particular ecosystem63. Since sampling methods are usually ecosystem-specific (i.e. sampling is done deeper in soils with developed organic layers), we treated the methods used by the original data collectors as representative of a given ecosystem. Under this assumption, we might have underestimated the number of springtails in soils with deep organic horizons, so our global estimates are conservative and we would expect true global density and biomass to be slightly higher. To minimize these effects, we excluded sites where the estimations were likely to be unreliable (see data selection below).Data selectionOnly data collection methods allowing for area-based recalculation (e.g. Tullgren or Berlese funnels) were used for analysis. Data from artificial habitats, coastal ecosystems, caves, canopies, snow surfaces, and strong experimental manipulations beyond the bounds of naturally occurring conditions were excluded (Supplementary Fig. 1). To ensure data quality, we performed a two-step quality check: technical selection and expert evaluation. Collected data varied according to collection protocols, such as sampling depth and the microhabitats (layers) considered. To technically exclude unreliable density estimations, we explored data with a number of diagnostic graphs (Supplementary Table 2; Supplementary Figs. 12–20) and filtered it, excluding the following: (1) All woodlands where only soil or only litter was considered; (2) All scrub ecosystems where only ground cover (litter or mosses) was considered; (3) Agricultural sites in temperate zones where only soil with sampling depth 90% of cases were masked on the main maps; for the map with density-species richness visualisation, two corresponding masks were applied (Fig. 2).To estimate spatial variability of our predictions while accounting for the spatial sampling bias in our data (Fig. 1a) we performed a spatially stratified bootstrapping procedure. We used the relative area of each IPBES79 region (i.e., Europe and Central Asia, Asia and the Pacific, Africa, and the Americas) to resample the original dataset, creating 100 bootstrap resamples. Each of these resamples was used to create a global map, which was then reduced to create mean, standard deviation, 95% confidence interval, and coefficient of variation maps (Supplementary Figs. 4–7).Global biomass, abundance, and community metabolism of springtails were estimated by summing predicted values for each 30 arcsec pixel10. Global community metabolism was recalculated from joule to mass carbon by assuming 1 kg fresh mass = 7 × 106 J80, an average water proportion in springtails of 70%58, and an average carbon concentration of 45% (calculated from 225 measurements across temperate forest ecosystems)81. We repeated the procedure of global extrapolation and prediction for biomass and community metabolism using minimum and maximum estimates of these parameters from regression coefficient uncertainties (see Parameter uncertainties).Path analysisTo reveal the predictors of springtail communities at the global scale, we performed a path analysis. After filtering the selected environmental variables (see above) according to their global availability and collinearity, 13 variables were used (Supplementary Fig. 9b): mean annual air temperature, mean annual precipitation (CHELSA database67), aridity (CGIAR database68), soil pH, sand and clay contents combined (sand and clay contents were co-linear in our dataset), soil organic carbon content (SoilGrids database73), NDVI (MODIS database72), human population density (GPWv4 database74), latitude, elevation69, and vegetation cover reported by the data providers following the habitat classification of European Environment Agency (woodland, scrub, agriculture, and grasslands; the latter were coded as the combination of woodland, scrub, and agriculture absent). Before running the analysis, we performed the Rosner’s generalized extreme Studentized deviate test in the EnvStats package82 to exclude extreme outliers and we z-standardized all variables (Supplementary R Code).Separate structural equation models were run to predict density, dry biomass, community metabolism, and local species richness in the lavaan package83. To account for the spatial clustering of our data in Europe, instead of running a model for the entire dataset, we divided the data by the IPBES79 geographical regions and selected a random subset of sites for Eurasia, such that only twice the number of sites were included in the model as the second-most represented region. We ran the path analysis 99 times for each community parameter with different Eurasian subsets (density had n = 723 per iteration, local species richness had n = 352, dry biomass had n = 568, and community metabolism had n = 533). We decided to keep the share of the Eurasian dataset larger than other regions to increase the number of sites per iteration and validity of the models. The Eurasian dataset also had the best data quality among all regions and a substantial reduction in datasets from Eurasia would result in a low weight for high-quality data. We additionally ran a set of models in which the Eurasian dataset was represented by the same number of sites as the second-most represented region, which yielded similar effect directions for all factors, but slightly higher variations and fewer consistently significant effects. In the paper, only the first version of analysis is presented. To illustrate the results, we averaged effect sizes for the paths across all iterations and presented the distribution of these effect sizes using mirrored Kernel density estimation (violin) plots. We marked and discussed effects that were significant at p  More

Global Forest Resources Assessment 2020 (FAO, 2020).Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).ADS 
CAS 

Google Scholar 
Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).ADS 

Google Scholar 
Foley, J. A. et al. Amazonia revealed: Forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32 (2007).
Google Scholar 
Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).ADS 
CAS 

Google Scholar 
Brandon, K. Ecosystem services from tropical forests: Review of current science. SSRN J. https://doi.org/10.2139/ssrn.2622749 (2014). Article 

Google Scholar 
Indarto, J. & Mutaqin, D. J. An overview of theoretical and empirical studies on deforestation. MPRA. Paper No. 70178 (2016).Geist, H. J. & Lambin, E. F. Proximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. Bioscience 52, 143–150 (2002).
Google Scholar 
Angelsen, A. & Kaimowitz, D. Rethinking the causes of deforestation: Lessons from economic models. World Bank Res. Obs. 14, 73–98 (1999).CAS 

Google Scholar 
Contreras-Hermosilla, A. The Underlying Causes of Forest Decline (Center for International Forestry Research, 2000).
Google Scholar 
Turner, B. L. et al. Two types of global environmental change: Definitional and spatial-scale issues in their human dimensions. Glob. Environ. Change 1, 14–22 (1990).
Google Scholar 
Meyer, W. B. & Turner, B. L. Human population growth and global land-use/cover change. Ann. Rev. Ecol. Syst. 2, 39–61 (1992).
Google Scholar 
Miyamoto, M., Mohd Parid, M., Noor Aini, Z. & Michinaka, T. Proximate and underlying causes of forest cover change in Peninsular Malaysia. For. Policy Econ. 44, 18–25 (2014).
Google Scholar 
Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).
Google Scholar 
Carodenuto, S. et al. A methodological framework for assessing agents, proximate drivers and underlying causes of deforestation: Field test results from southern cameroon. Forests 6, 203–224 (2015).
Google Scholar 
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 

Google Scholar 
Hosonuma, N. et al. An assessment of deforestation and forest degradation drivers in developing countries. Environ. Res. Lett. 7, 044009 (2012).ADS 

Google Scholar 
Köthke, M., Leischner, B. & Elsasser, P. Uniform global deforestation patterns—An empirical analysis. For. Policy Econ. 28, 23–37 (2013).
Google Scholar 
Busch, J. & Ferretti-Gallon, K. What drives deforestation and what stops it? A meta-analysis. Rev. Environ. Econ. Policy 11, 3–23 (2017).
Google Scholar 
Ferrer Velasco, R. F., Köthke, M., Lippe, M. & Günter, S. Scale and context dependency of deforestation drivers: Insights from spatial econometrics in the tropics. PLoS One 15, e0226830 (2020).CAS 

Google Scholar 
Lambin, E. F. et al. Effectiveness and synergies of policy instruments for land use governance in tropical regions. Glob. Environ. Change 28, 129–140 (2014).
Google Scholar 
Börner, J., Schulz, D., Wunder, S. & Pfaff, A. The effectiveness of forest conservation policies and programs. Ann. Rev. Resour. Econ. 12, 45–64 (2020).
Google Scholar 
Bemelmans-Videc, M.-L., Rist, R. C. & Vedung, E. Carrots, Sticks & Sermons: Policy Instruments and their Evaluation (Transaction Publishers, 1998).
Google Scholar 
Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).ADS 
CAS 

Google Scholar 
Lambin, E. F. et al. The role of supply-chain initiatives in reducing deforestation. Nat. Clim. Change 8, 109–116 (2018).ADS 

Google Scholar 
Wolff, S. & Schweinle, J. Effectiveness and economic viability of forest certification: A systematic review. Forests 13, 798 (2022).
Google Scholar 
Müller, R., Pistorius, T., Rohde, S., Gerold, G. & Pacheco, P. Policy options to reduce deforestation based on a systematic analysis of drivers and agents in lowland Bolivia. Land Use Policy 30, 895–907 (2013).
Google Scholar 
Tegegne, Y. T., Lindner, M., Fobissie, K. & Kanninen, M. Evolution of drivers of deforestation and forest degradation in the Congo Basin forests: Exploring possible policy options to address forest loss. Land Use Policy 51, 312–324 (2016).
Google Scholar 
Hoffmann, C., García Márquez, J. R. & Krueger, T. A local perspective on drivers and measures to slow deforestation in the Andean-Amazonian foothills of Colombia. Land Use Policy 77, 379–391 (2018).
Google Scholar 
Henders, S., Ostwald, M., Verendel, V. & Ibisch, P. Do national strategies under the UN biodiversity and climate conventions address agricultural commodity consumption as deforestation driver?. Land Use Policy 70, 580–590 (2018).
Google Scholar 
Salvini, G. et al. How countries link REDD+ interventions to drivers in their readiness plans: implications for monitoring systems. Environ. Res. Lett. 9, 074004 (2014).ADS 

Google Scholar 
Bos, A. B. et al. Integrated assessment of deforestation drivers and their alignment with subnational climate change mitigation efforts. Environ. Sci. Policy 114, 352–365 (2020).CAS 

Google Scholar 
Fritz, S. et al. A continental assessment of the drivers of tropical deforestation with a focus on protected areas. Front. Conserv. Sci. https://doi.org/10.3389/fcosc.2022.830248 (2022).Article 

Google Scholar 
Lawrence, D. & Vandecar, K. Effects of tropical deforestation on climate and agriculture. Nat. Clim. Change 5, 27–36 (2015).ADS 

Google Scholar 
Fedele, G., Locatelli, B., Djoudi, H. & Colloff, M. J. Reducing risks by transforming landscapes: Cross-scale effects of land-use changes on ecosystem services. PLoS One 13, e0195895 (2018).
Google Scholar 
Yackulic, C. B. et al. Biophysical and socioeconomic factors associated with forest transitions at multiple spatial and temporal scales. Ecol. Soc. https://doi.org/10.5751/ES-04275-160315 (2011).Article 

Google Scholar 
Loran, C., Ginzler, C. & Bürgi, M. Evaluating forest transition based on a multi-scale approach: Forest area dynamics in Switzerland 1850–2000. Reg. Environ. Change 16, 1807–1818 (2016).
Google Scholar 
Moonen, P. C. et al. Actor-based identification of deforestation drivers paves the road to effective REDD+in DR Congo. Land Use Policy 58, 123–132 (2016).
Google Scholar 
Strassburg, B. The tragedy of the tropics: A dynamic, cross-scale analysis of deforestation incentives. Working Paper—Centre for Social and Economic Research on the Global Environment No. 07-02 (2007).López-Carr, D. et al. Space versus place in complex human–natural systems: Spatial and multi-level models of tropical land use and cover change (LUCC) in Guatemala. Ecol. Model. 229, 64–75 (2012).
Google Scholar 
Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).
Google Scholar 
Pendrill, F. et al. Agricultural and forestry trade drives large share of tropical deforestation emissions. Glob. Environ. Change 56, 1–10 (2019).
Google Scholar 
Ferrer Velasco, R. et al. Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters. Remote Sens. Environ. 274, 112997 (2022).ADS 

Google Scholar 
Jayathilake, H. M., Prescott, G. W., Carrasco, L. R., Rao, M. & Symes, W. S. Drivers of deforestation and degradation for 28 tropical conservation landscapes. Ambio 50, 215–228 (2021).
Google Scholar 
Minang, P. A. et al. REDD+Readiness progress across countries: Time for reconsideration. Clim. Policy 14, 685–708 (2014).
Google Scholar 
Current pledges | Bonn challenge. https://www.bonnchallenge.org/pledges. Accessed: 15th August 2022.Nansikombi, H. et al. Can de facto governance influence deforestation drivers in the Zambian Miombo?. For. Policy Econ. 120, 102309 (2020).
Google Scholar 
Sullivan, A., York, A., White, D., Hall, S. & Yabiku, S. D. Jure versus de facto institutions: Trust, information, and collective efforts to manage the invasive mile-a-minute weed (Mikania micrantha). Int. J. Commons 11, 171–199 (2017).
Google Scholar 
Busch, J. & Amarjargal, O. Authority of second-tier governments to reduce deforestation in 30 tropical countries. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2020.00001 (2020).Article 

Google Scholar 
Sandström, C., Eckerberg, K. & Raitio, K. Studying conflicts, proposing solutions—Towards multi-level approaches to the analyses of forest conflicts. For. Policy Econ. 33, 123–127 (2013).
Google Scholar 
Hoogstra-Klein, M. A., Permadi, D. B. & Yasmi, Y. The value of cultural theory for participatory processes in natural resource management. For. Policy Econ. 20, 99–106 (2012).
Google Scholar 
de Jong, W., Ruiz, S. & Becker, M. Conflicts and communal forest management in northern Bolivia. For. Policy Econ. 8, 447–457 (2006).
Google Scholar 
Eckerberg, K. & Sandström, C. Forest conflicts: A growing research field. For. Policy Econ. 33, 3–7 (2013).
Google Scholar 
Sierra, R., Calva, O. & Guevara, A. La Deforestación en el Ecuador, 1990–2018. Factores promotores y tendencias recientes, 216 (2021).Wasserstrom, R. & Southgate, D. Deforestation, agrarian reform and oil development in Ecuador, 1964–1994. Nat. Resour. 04, 31 (2013).
Google Scholar 
Wiebe, P. C., Zhunusova, E., Lippe, M., Ferrer Velasco, R. & Günter, S. What is the contribution of forest-related income to rural livelihood strategies in the Philippines’ remaining forested landscapes?. For. Policy Econ. 135, 102658 (2022).
Google Scholar 
Le, H. D., Smith, C. & Herbohn, J. What drives the success of reforestation projects in tropical developing countries? The case of the Philippines. Glob. Environ. Change 24, 334–348 (2014).
Google Scholar 
Carandang, A. P. et al. Analysis of key drivers of deforestation and forest degradation in the Philippines. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) (2013).Phiri, D., Morgenroth, J. & Xu, C. Four decades of land cover and forest connectivity study in Zambia—An object-based image analysis approach. Int. J. Appl. Earth Obs. Geoinf. 79, 97–109 (2019).ADS 

Google Scholar 
Nansikombi, H., Fischer, R., Kabwe, G. & Günter, S. Exploring patterns of forest governance quality: Insights from forest frontier communities in Zambia’s Miombo ecoregion. Land Use Policy 99, 104866 (2020).
Google Scholar 
Zhang, H., Wang, P. & Wood, J. Does institutional quality matter for the nexus between environmental quality and economic growth?: A tropics perspective. In Business, Industry, and Trade in the Tropics (eds Wood, J. et al.) (Routledge, 2022).
Google Scholar 
Reed, J., Van Vianen, J., Deakin, E. L., Barlow, J. & Sunderland, T. Integrated landscape approaches to managing social and environmental issues in the tropics: Learning from the past to guide the future. Glob. Change Biol. 22, 2540–2554 (2016).ADS 

Google Scholar 
Fischer, R. et al. Interplay of governance elements and their effects on deforestation in tropical landscapes: Quantitative insights from Ecuador. World Dev. 148, 105665 (2021).
Google Scholar 
Torres, B., Vasco, C., Günter, S. & Knoke, T. Determinants of agricultural diversification in a hotspot area: Evidence from colonist and indigenous communities in the Sumaco biosphere reserve Ecuadorian Amazon. Sustainability 10, 1432 (2018).
Google Scholar 
Ojeda Luna, T., Zhunusova, E., Günter, S. & Dieter, M. Measuring forest and agricultural income in the Ecuadorian lowland rainforest frontiers: Do deforestation and conservation strategies matter?. For. Policy Econ. 111, 102034 (2020).
Google Scholar 
Kazungu, M. et al. Effects of household-level attributes and agricultural land-use on deforestation patterns along a forest transition gradient in the Miombo landscapes Zambia. Ecol. Econ. 186, 107070 (2021).
Google Scholar 
Kleemann, J. et al. Deforestation in continental ecuador with a focus on protected areas. Land 11, 268 (2022).
Google Scholar 
Mulenga, M. M. & Roos, A. Assessing the awareness and adoptability of pellet cookstoves for low-income households in Lusaka, Zambia. J. Energy South. Afr. 32, 52–61 (2021).
Google Scholar 
Eguiguren, P., Ojeda Luna, T., Torres, B., Lippe, M. & Günter, S. Ecosystem service multifunctionality: Decline and recovery pathways in the amazon and chocó lowland rainforests. Sustainability 12, 7786 (2020).CAS 

Google Scholar 
Vasco, C., Torres, B., Pacheco, P. & Griess, V. The socioeconomic determinants of legal and illegal smallholder logging: Evidence from the Ecuadorian Amazon. For. Policy Econ. 78, 133–140 (2017).
Google Scholar 
van der Ploeg, J., van Weerd, M., Masipiqueña, A. B. & Persoon, G. A. Illegal logging in the Northern Sierra Madre Natural Park, the Philippines. Conserv. Soc. 9, 202–215 (2011).
Google Scholar 
Liu, D. S., Iverson, L. R. & Brown, S. Rates and patterns of deforestation in the Philippines: Application of geographic information system analysis. For. Ecol. Manag. 57, 1–16 (1993).
Google Scholar 
Boquet, Y. Environmental challenges in the Philippines. In The Philippine Archipelago (ed. Boquet, Y.) 779–829 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-51926-5_22.Chapter 

Google Scholar 
MAGAP. ATPA: Reconversión Agro productiva Sostenible en la Amazonía Ecuatoriana (2014).Jones, K. W. et al. Forest conservation incentives and deforestation in the Ecuadorian Amazon. Environ. Conserv. 44, 56–65 (2017).
Google Scholar 
Lindsey, P. A. et al. Underperformance of African protected area networks and the case for new conservation models: Insights from Zambia. PLoS One 9, e94109 (2014).ADS 

Google Scholar 
Fischer, R. et al. Effectiveness of policy instrument mixes for forest conservation in the tropics – a stakeholder perspective from Ecuador, the Philippines and Zambia. Land Use Policy https://doi.org/10.1016/j.landusepol.2023.106546 (2022).Article 

Google Scholar 
Gurney, G. G. et al. Biodiversity needs every tool in the box: Use OECMs. Nature 595, 646–649 (2021).ADS 
CAS 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).ADS 
CAS 

Google Scholar 
Priebe, J. et al. Transformative change in context—Stakeholders’ understandings of leverage at the forest–climate nexus. Sustain. Sci. 17, 1921–1938 (2022).
Google Scholar 
Höhl, M. et al. Forest landscape restoration—What generates failure and success?. Forests 11, 938 (2020).
Google Scholar 
Köthke, M., Ahimbisibwe, V. & Lippe, M. The evidence base on the environmental, economic and social outcomes of agroforestry is patchy—An evidence review map. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2022.925477 (2022).Article 

Google Scholar 
Fischer, R., Giessen, L. & Günter, S. Governance effects on deforestation in the tropics: A review of the evidence. Environ. Sci. Policy 105, 84–101 (2020).
Google Scholar 
Bare, M., Kauffman, C. & Miller, D. C. Assessing the impact of international conservation aid on deforestation in sub-Saharan Africa. Environ. Res. Lett. 10, 125010 (2015).ADS 

Google Scholar 
Vuohelainen, A. J., Coad, L., Marthews, T. R., Malhi, Y. & Killeen, T. J. The effectiveness of contrasting protected areas in preventing deforestation in Madre de Dios. Peru. Environ. Manag. 50, 645–663 (2012).ADS 

Google Scholar 
Hull, V. & Liu, J. Telecoupling: A new frontier for global sustainability. Ecol. Soc. 23, 41 (2018).
Google Scholar 
Aitchison, J. The statistical analysis of compositional data. J. Roy. Stat. Soc. 44, 139–160 (1982).MathSciNet 
MATH 

Google Scholar 
Norman, G. Likert scales, levels of measurement and the “laws” of statistics. Adv. Health Sci. Educ. 15, 625–632 (2010).
Google Scholar 
Day, M., Gumbo, D., Moombe, K. B., Wijaya, A. & Sunderland, T. Zambia Country Profile: Monitoring, Reporting and Verification for REDD+ Vol. 113 (CIFOR, 2014).
Google Scholar 
Piotrowski, M. Nearing the tipping point. Drivers of Deforestation in the Amazon Region (2019).Sarker, P. K., Fischer, R., Tamayo, F., Navarrete, B. T. & Günter, S. Analyzing forest policy mixes based on the coherence of policies and the consistency of legislative policy instruments: A case study from Ecuador. For. Policy Econ. 144, 102838 (2022).
Google Scholar 
Likert, R. A technique for the measurement of attitudes. Arch. Psychol. 22(140), 55–55 (1932).
Google Scholar 
Altinsoy, M. et al. Ambulatory ECG monitoring for syncope and collapse in United States, Europe, and Japan: The patients’ viewpoint. J. Arrhythm. 37, 1023–1030 (2021).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/. (R Foundation for Statistical Computing, Vienna, Austria, 2022).Kassambara, A. rstatix: Pipe-friendly framework for basic statistical tests. R package version 0.7.0 (2021).Kassambara, A. & Mundt, F. factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7 (2020).Komsta, L. & Novometsky, F. moments: Moments, cumulants, skewness, kurtosis and related tests. R package version 0.14.1 (2022).Zhang, Y., Zhou, M. & Shao, Y. mvnormalTest: Powerful tests for multivariate normality. R package version 1.0.0 (2020).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0 (2020).Wickham, H. et al. Welcome to the Tidyverse. JOSS 4, 1686 (2019).ADS 

Google Scholar 
Bache, S. M. & Wickham, H. magrittr: A Forward-Pipe Operator for R. R package version 2.0.3 (2022).Ushey, K., Allaire, J., Wickham, H. & Ritchie, G. rstudioapi: Safely Access the RStudio API. R package version 0.13 (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-24277-4.Book 
MATH 

Google Scholar 
Wilkins, D. treemapify: Draw Treemaps in ‘ggplot2’. R package version 2.5.5 (2021).Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).MathSciNet 
MATH 

Google Scholar 
Mardia, K. V. Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519–530 (1970).MathSciNet 
MATH 

Google Scholar 
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).MATH 

Google Scholar 
Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).
Google Scholar 
Conover, W. J. & Iman, R. L. Rank transformations as a bridge between parametric and nonparametric statistics. Am. Stat. 35, 124–129 (1981).MATH 

Google Scholar 
Student,. The probable error of a mean. Biometrika 6, 1–25 (1908).MATH 

Google Scholar 
Tukey, J. W. Comparing individual means in the analysis of variance. Biometrics 5, 99–114 (1949).MathSciNet 
CAS 

Google Scholar 
Jolliffe, I. T. Principal Component Analysis (Springer, 2002).MATH 

Google Scholar  More

Plant growth model without environmental forcingThe model without environmental forcing closely follows the original description of the Thornley transport resistance (TTR) model29. A summary of the model parameters is provided in Supplementary Table 2. The shoot and root mass pools (MS and MR, in kg structural dry matter) change as a function of growth and loss (equations (1) and (2)). The litter (kL) and maintenance respiration (r) loss rates (in kg kg−1 d−1) are treated as constants. In the original model description29 r = 0. The parameter KM (units kg) describes how loss varies with mass (MS or MR). Growth (Gs and Gr, in kg d−1) varies as a function of the carbon and nitrogen concentrations (equations (3) and (4)). CS, CR, NS and NR are the amounts (kg) of carbon and nitrogen in the roots and shoots. These assumptions yield the following equations for shoot and root dry matter,$${mathrm{MS}}[t+1]={mathrm{MS}}[t]+{G}_{{mathrm{S}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MS}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MS}}[t]}},$$
(1)
$${mathrm{MR}}[t+1]={mathrm{MR}}[t]+{G}_{{mathrm{R}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MR}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MR}}[t]}},$$
(2)
where GS and GR are$${G}_{{mathrm{S}}}=gfrac{{mathrm{CS}}times {mathrm{NS}}}{{mathrm{MS}}},$$
(3)
$${G}_{{mathrm{R}}}=gfrac{{mathrm{CR}}times {mathrm{NR}}}{{mathrm{MR}}},$$
(4)
and g is the growth coefficient (in kg kg−1 d−1).Carbon uptake UC is determined by the net photosynthetic rate (a, in kg kg−1 d−1) and the shoot mass (equation (5)). Similarly, nitrogen uptake (UN) is determined by the nitrogen uptake rate (b, in kg kg−1 d−1) and the root mass. The parameter KA (units kg) forces both photosynthesis and nitrogen uptake to be asymptotic with mass. The second terms in the denominators of equations (5) and (6) model product inhibitions of carbon and nitrogen uptake, respectively; that is, the parameters JC and JN (in kg kg−1) mimic the inhibition of source activity when substrate concentrations are high,$${U}_{{mathrm{C}}}=frac{a{mathrm{MS}}}{left(1+frac{{mathrm{MS}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{CS}}}{{mathrm{MS}}times {J}_{{mathrm{C}}}}right)},$$
(5)
$${U}_{{mathrm{N}}}=frac{b{mathrm{MR}}}{left(1+frac{{mathrm{MR}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{NR}}}{{mathrm{MR}}times {J}_{{mathrm{N}}}}right)}.$$
(6)
The substrate transport fluxes of C and N (τC and τN, in kg d−1) between roots and shoots are determined by the concentration gradients between root and shoot and by the resistances. In the original model description29, these resistances are defined flexibly, but we simplify and assume that they scale linearly with plant mass,$${tau }_{{mathrm{C}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{CS}}}{{mathrm{MS}}}-frac{{mathrm{CR}}}{{mathrm{MR}}}right)$$
(7)
$${tau }_{{mathrm{N}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{NR}}}{{mathrm{MR}}}-frac{{mathrm{NS}}}{{mathrm{MS}}}right)$$
(8)
The changes in mass of carbon and nitrogen in the roots and shoots are then$${mathrm{CS}}[t+1]={mathrm{CS}}[t]+{U}_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{s}}}[t]-{tau }_{{mathrm{C}}}[t]$$
(9)
$${mathrm{CR}}[t+1]={mathrm{CR}}[t]+{tau }_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{r}}}[t]$$
(10)
$${mathrm{NS}}[t+1]={mathrm{NS}}[t]+{tau }_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{s}}}[t]$$
(11)
$${mathrm{NR}}[t+1]={mathrm{NR}}[t]+{U}_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{r}}}[t]-{tau }_{{mathrm{N}}}[t]$$
(12)
where fC and fN (in kg kg−1) are the fractions of structural carbon and nitrogen in dry matter.Adding environmental forcing to the plant growth modelIn this section, we describe how the net photosynthetic rate (a), the nitrogen uptake rate (b), the growth rate (g) and the respiration rate (r) are influenced by environmental-forcing factors. These environmental-forcing effects are described in equations (13)–(17) and summarized graphically in Extended Data Fig. 1. All other model parameters are treated as constants. Previous work that implemented the TTR model as a species distribution model30 is used as a starting point for adding environmental forcing. As in this previous work30, we assume that parameters a, b and g are co-limited by environmental factors in a manner analogous to Liebig’s law of the minimum, which is a crude but pragmatic abstraction. The implementation here differs in some details.Unlike previous work30, we use the Farquhar model of photosynthesis47,48 to represent how solar radiation, atmospheric CO2 concentration and air temperature co-limit photosynthesis35. We assume that the Farquhar model parameters are universal and that all vegetation in our study uses the C3 photosynthetic pathway. The Farquhar model photosynthetic rates are rescaled to [0,amax] to yield afqr. The effects of soil moisture (Msoil) on photosynthesis are represented as an increasing step function ({{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})=max left{min left(frac{M_{mathrm{soil}}-{beta }_{1}}{{beta }_{2}-{beta }_{1}},1right),0right}). This allows us to redefine a as,$$a={a}_{{mathrm{fqr}}} {{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})$$
(13)
The processes influencing nitrogen availability are complex, and global data products on plant available nitrogen are uncertain. We therefore assume that nitrogen uptake will vary with soil temperature and soil moisture. That is, the nitrogen uptake rate b is assumed to have a maximum rate (bmax) that is co-limited by soil temperature Tsoil and soil moisture Msoil,$$b={b}_{{mathrm{max}}} {{{{S}}}}({T}_{soil},{beta }_{3},{beta }_{4}) {{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8}).$$
(14)
In equation (14), we have assumed that the nitrogen uptake rate is a simple increasing and saturating function of temperature. We have also assumed that the nitrogen uptake rate is a trapezoidal function of soil moisture with low uptake rates in dry soils, higher uptake rates at intermediate moisture levels and lower rates once soils are so moist as to be waterlogged. The trapezoidal function is ({{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8})=max left{min left(frac{M_{mathrm{soil}}-{{{{{beta }}}}}_{5}}{{{{{{beta }}}}}_{6}-{{{{{beta }}}}}_{5}},1,frac{{{{{{beta }}}}}_{8}-M_{mathrm{soil}}}{{beta }_{8}-{beta }_{7}}right),0right}).The previous sections describe how the assimilation of carbon and nitrogen by a plant are influenced by environmental factors. The TTR model describes how these assimilate concentrations influence growth (equations (3) and (4)). In our implementation, we additionally allow the growth rate to be co-limited by temperature (soil temperature, Tsoil) and soil moisture (Msoil),$$g={g}_{{mathrm{max}}} {{{{Z}}}}({T}_{{mathrm{soil}}},{beta }_{9},{beta }_{10},{beta }_{11},{beta }_{12}) {{{{S}}}}(M_{mathrm{soil}},{beta }_{13},{beta }_{14}).$$
(15)
We use Tsoil since we assume that growth is more closely linked to soil temperature, which varies slower than air temperature. The respiration rate (r, equations (1) and (2)) increases as a function of air temperature (Tair) to a maximum rmax,$$r={r}_{{mathrm{max}}}{{{{S}}}}({T}_{{mathrm{air}}},{beta }_{15},{beta }_{16}).$$
(16)
The parameter r is best interpreted as a maintenance respiration. Growth respiration is not explicitly considered; it is implicitly incorporated in the growth rate parameter (g, equation (15)), and any temperature dependence in growth respiration is therefore assumed to be accommodated by equation (15).Fire can reduce the structural shoot mass MS as follows,$${mathrm{MS}}[t+1]={mathrm{MS}}[t](1-{{{{S}}}}(F,{beta }_{17},{beta }_{18})).$$
(17)
where F is an indicator of fire severity at a point in time (for example, burnt area) and the function S(F, β17, β18) allows MS to decrease when the fire severity indicator F is high. If F = 0, this process plays no role. This fire impact equation was used in preliminary analyses, but the data on fire activity did not provide sufficient information to estimate β17 and β18; we therefore excluded this process from the final analyses.We further estimate two additional β parameters (βa and βb) so that each site can have unique maximum carbon and nitrogen uptake rates. Specifically, we redefine a as ({a}^{{prime} }={beta }_{a} a) and b as ({b}^{{prime} }={beta }_{b} b).Data sources and preparationTo describe vegetation activity, we use the GIMMS 3g v.1 NDVI data26,27 and the MODIS EVI28 data. The GIMMS data product has been derived from the AVHRR satellite programme and controls for atmospheric contamination, calibration loss, orbital drift and volcanic eruptions26,27. The data provide 24 NDVI raster grids for each year, starting in July 1981 and ending in December 2015. The spatial resolution is 1/12° (~9 × 9 km). The EVI data used are from the MODIS programme’s Terra satellite; it is a 1 km data product provided at a 16-day interval. We use data from the start of the record (February 2000) to December 2019. The MODIS data product (MOD13A2) uses a temporal compositing algorithm to produce an estimate every 16 days that filters out atmospheric contamination. The EVI is designed to reduce the effects of atmospheric, bare-ground and surface water on the vegetation index28.For environmental forcing, we use the ERA5-Land data31,32 (European Centre for Medium-Range Weather Forecasts Reanalysis v. 5; hereafter, ERA5). The ERA5 products are global reanalysis products based on hourly estimates of atmospheric variables and extend from present back to 1979. The data products are supplied at a variety of spatial and temporal resolutions. We used the monthly averages from 1981 to 2019 at a 0.1° spatial resolution (~11 km). The ERA5 data provide air temperature (2 m surface air temperature), soil temperature (0–7 cm soil depth), surface solar radiation and volumetric soil water (0–7 cm soil depth). Fire data were taken from the European Space Agency Fire Disturbance Climate Change Initiative’s AVHRR Long-Term Data Record Grid v.1.0 product49. This product provides gridded (0.25° resolution) data of monthly global (from 1982 to 2017) burned area derived from the AVHRR satellite programme. As mentioned, the fire data did not enrich our analysis, and the analyses we present here therefore exclude further consideration of the fire data.All data were resampled to the GIMMS grid. The mean pixel EVI was then calculated for each GIMMS cell for each time point in the MODIS EVI data. We used linear interpolation on the NDVI, EVI and ERA5 environmental-forcing data to estimate each variable on a weekly time step. This served to set the time step of the TTR difference equations to one week and to synchronize the different time series.Site selectionThe GIMMS grid cells define the spatial resolution of our sample points. GIMMS grid cells are large (1/12°, ~9 km), meaning that most grid cells contain multiple land-cover types. We focused on wilderness landscapes, and our aim was to find multiple grid cells for the major ecosystems of the world. We used the following classification of ecosystem types to guide the stratification of our grid-cell selection: tropical evergreen forest (RF), boreal forest (BF), temperate evergreen and temperate deciduous forest (TF), savannah (SA), shrubland (SH), grassland (GR), tundra (TU) and Mediterranean-type ecosystems (MT).We used the following criteria to select grid cells. (1) Selected grid cells should contain relatively homogeneous vegetation. Small-scale heterogeneity was allowed (for example, catenas, drainage lines, peatlands) as long as many of these elements are repeated in the scene (for example, rolling hills were accepted, but elevation gradients were rejected). (2) Sites should have no signs of transformative human activity (for example, tree harvesting, crop cultivation, paved surfaces). We used the Time Tool in Google Earth Pro, which provides annual satellite imagery of the Earth from 1984 onwards, to ensure that no such activity occurred during the observation period (note that the GIMMS record starts in July 1981; however, it is likely that evidence of transformative activity between July 1981 and 1984 would be visible in 1984). Grid cells with extensive livestock holding on non-improved pasture were included. In some cases, a small agricultural field or pasture was present, and such grid cells were used as long as the field or pasture was small and remained constant in size. (3) Grid cells should not include large water bodies, but small drainage lines or ponds were accepted as long as they did not violate the first criterion. (4) Grid cells should be independent (neighbouring grid cells were not selected) and cover the major ecosystems of the world. Using these criteria, we were able to include 100 sites in the study (Extended Data Figs. 2 and 3 and Supplementary Table 4).State-space modelWe used a Bayesian state-space approach. Conceptually, the analysis takes the form,$$M[t]=f(M[t-1],{boldsymbol{beta}},{boldsymbol{theta}}_{t-1},{epsilon }_{t-1})$$
(18)
$${mathrm{VI}}[t]=m M[t]+eta .$$
(19)
Here M[t] is the plant growth model’s prediction of biomass (M = MS + MR) at time t, and ϵt−1 is the process error associated with the state variables. In the model, each underlying state variable (MS, MR, CS, CR, NS and NR) has an associated process error term. The function f(M[t − 1], β, θt−1, ϵt−1) summarizes that the development of M is influenced by the state variables MS, MR, CS, CR, NS and NR, the environmental-forcing data θt−1 and the β parameters. The observation equation (equation (19)) uses the parameter m to link the VI (vegetation index, either NDVI or EVI) observations to the modelled state M. The parameter η is the observation error. Equation (19) assumes that there is a linear relationship between modelled biomass (M) and VI, which is a simplification of reality50,51,52. The observation error η is structured by our simplification of the data products quality scores (coded Q = 0, 1, 2, with 0 being good and 2 being poor; Supplementary Table 3) to allow the error to increase with each level of the quality score. Specifically, we define η = e0 + e1 × Q.The model was formulated using the R package LaplacesDemon53. All β parameters are given vague uniform priors. The parameter m is given a vague normal prior (truncated to be >0). The process error terms are modelled using normal distributions, and the variances of the error terms are given vague half-Cauchy priors. The ex parameters are given vague normal priors. The model also requires the parameterization of M[0], the initial vegetation biomass; M[0] is given a vague uniform prior. We used the twalk Markov chain Monte Carlo (MCMC) algorithm as implemented in LaplacesDemon53 and its default control parameters to estimate the posterior distributions of the model parameters. We further fitted the model using DEoptim54,55, which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems56, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis: t = –2.9806, degrees of freedom (d.f.) = 99, P = 0.00362; EVI analysis: t = –4.6229, d.f. = 99, P = 1.144 × 10–5), suggesting that the MCMC algorithm performed well compared with the genetic algorithm.Anomaly extraction and trend estimationWe use the ‘seasonal and trend decomposition using Loess’ (STL57) as implemented in the R58 base function stl. STL extracts the seasonal component s of a time series using Loess smoothing. What remains after seasonal extraction (the anomaly or remainder, r) is the sum of any long-term trend and stochastic variation. We estimate the trend in two ways. First, we estimate the trend by fitting a quadratic polynomial (r = a + bx + cx2) to the remainder (r is the remainder, x is time and a, b and c are regression coefficients). The use of polynomials allows the data to specify whether a trend exists, whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. As a second method, we estimate the trend by fitting a bent-cable regression to the remainder. Bent-cable regression is a type of piecewise linear regression for estimating the point of transition between two linear phases in a time series59,60. The model takes the form r = b0 + b1x + b2 q(x, τ, γ)60. Here r is the remainder, x is time, b0 is the initial intercept, b1 is the slope in phase 1, the slope in phase 2 is b2 − b1 and q is a function that defines the change point: (q(x,tau ,gamma )=frac{{(x-tau +gamma )}^{2}}{4gamma }I(tau -gamma < tau +gamma )+(x-tau )I(x > tau +gamma )); τ represents the location of the change point and γ the span of the bent cable that joins the two linear phases; I(A) is an indicator function that returns 1 if A is true and 0 if A is false. The bent-cable model allows the data to specify whether a trend exists and whether there has been a switch in the trend, thereby allowing the identification of whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. Both the polynomial and bent-cable models were estimated using LaplacesDemon’s53 Adaptive Metropolis MCMC algorithm and vague priors, although for the bent-cable model we constrained τ to be in the middle 70% of the time series and γ to be at most two years.The STL extraction of the seasonal components in the air temperature, soil temperature, soil moisture and solar radiation data (there is no stochasticity or seasonal trend in the CO2 data we used) allows us to simulate detrended time series d of these forcing variables as (d=bar{y}+s+{{{{N}}}}(mu ,sigma )) where N(μ, σ) is a normally distributed random variable with mean and standard deviation estimated from the remainder r (we verified that r was well described by the normal distribution), (bar{y}) is the mean of the data over the time series and s is the seasonal component extracted by STL. For CO2, the detrended time series is simply the average CO2 over the time series. More

Katra, I. et al. Richness and diversity in dust stormborne biomes at the Southeast Mediterranean. Sci. Rep. 4, 5265 (2014).CAS 

Google Scholar 
Kellogg, C. A. & Griffin, D. W. Aerobiology and the global transport of desert dust. Trends Ecol. Evolution 21, 638–644 (2006).
Google Scholar 
Mazar, Y., Cytryn, E., Erel, Y. & Rudich, Y. Effect of dust storms on the atmospheric microbiome in the eastern Mediterranean. Environ. Sci. Technol. 50, 4194–4202 (2016).CAS 

Google Scholar 
Gat, D., Mazar, Y., Cytryn, E. & Rudich, Y. Origin-dependent variations in the atmospheric microbiome community in Eastern Mediterranean Dust Storms. Environ. Sci. Technol. 51, 6709–6718 (2017).CAS 

Google Scholar 
Lang-Yona, N. et al. Links between airborne microbiome, meteorology, and chemical composition in northwestern Turkey. Sci. Total Environ. 725, 138227 (2020).CAS 

Google Scholar 
Gat, D. et al. Size-resolved community structure of bacteria and fungi transported by dust in the Middle East. Front. Microbiol. 12 (2021) https://doi.org/10.3389/fmicb.2021.744117.Hill, T. C. J. et al. Sources of organic ice nucleating particles in soils. Atmos. Chem. Phys. 16, 7195–7211 (2016).CAS 

Google Scholar 
Pandey, R. et al. Ice-nucleating bacteria control the order and dynamics of interfacial water. Sci. Adv. 2, e1501630 (2016).
Google Scholar 
Fröhlich-Nowoisky, J. et al. Ice nucleation activity in the widespread soil fungus Mortierella alpina. Biogeosciences 12, 1057–1071 (2015).
Google Scholar 
Estillore, A. D., Trueblood, J. V. & Grassian, V. H. Atmospheric chemistry of bioaerosols: heterogeneous and multiphase reactions with atmospheric oxidants and other trace gases. Chem. Sci. 7, 6604–6616 (2016).CAS 

Google Scholar 
Brodie, E. L. et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. Acad. Sci. 104, 299–304 (2007).CAS 

Google Scholar 
Šantl-Temkiv, T. et al. Characterization of airborne ice-nucleation-active bacteria and bacterial fragments. Atmos. Environ. 109, 105–117 (2015).
Google Scholar 
Rahav, E., Ovadia, G., Paytan, A. & Herut, B. Contribution of airborne microbes to bacterial production and N2 fixation in seawater upon aerosol deposition. Geophys. Res. Lett. 43, 719–727 (2016).CAS 

Google Scholar 
Failor, K. C., Schmale, D. G., Vinatzer, B. A. & Monteil, C. L. Ice nucleation active bacteria in precipitation are genetically diverse and nucleate ice by employing different mechanisms. ISME J. 11, 2740–2753 (2017).CAS 

Google Scholar 
de Araujo, G. G., Rodrigues, F., Gonçalves, F. L. T. & Galante, D. Survival and ice nucleation activity of Pseudomonas syringae strains exposed to simulated high-altitude atmospheric conditions. Sci. Rep. 9, 7768 (2019).
Google Scholar 
Lazaridis, M. Bacteria as Cloud Condensation Nuclei (CCN) in the Atmosphere. Atmosphere 10, 786 (2019).CAS 

Google Scholar 
Amato, P. et al. Active microorganisms thrive among extremely diverse communities in cloud water. PLOS ONE 12, e0182869 (2017).
Google Scholar 
Amato, P. et al. Metatranscriptomic exploration of microbial functioning in clouds. Sci. Rep. 9, 4383 (2019).
Google Scholar 
Vaïtilingom, M. et al. Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds. Proc. Natl. Acad. Sci. 110, 559–564 (2013).
Google Scholar 
Triadó-Margarit, X., Cáliz, J. & Casamayor, E. O. A long-term atmospheric baseline for intercontinental exchange of airborne pathogens. Environ. Int. 158, 106916 (2022).
Google Scholar 
Brodie, E. L. et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. Acad. Sci. 104, 299 (2007).CAS 

Google Scholar 
Archer, S. D. J. et al. Airborne microbial transport limitation to isolated Antarctic soil habitats. Nat. Microbiol 4, 925–932 (2019).CAS 

Google Scholar 
Mayol, E. et al. Long-range transport of airborne microbes over the global tropical and subtropical ocean. Nat. Commun. 8, 201 (2017).
Google Scholar 
Favet, J. et al. Microbial hitchhikers on intercontinental dust: catching a lift in Chad. ISME J. 7, 850–867 (2013).CAS 

Google Scholar 
Cáliz, J., Triadó-Margarit, X., Camarero, L. & Casamayor, E. O. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc. Natl. Acad. Sci. 115, 12229–12234 (2018).
Google Scholar 
Du, P., Du, R., Ren, W., Lu, Z. & Fu, P. Seasonal variation characteristic of inhalable microbial communities in PM2.5 in Beijing city, China. Sci. Total Environ. 610-611, 308–315 (2018).CAS 

Google Scholar 
Lang-Yona, N. et al. Links between airborne microbiome, meteorology, and chemical composition in northwestern Turkey. Sci. Total Environ. 725, 138227 (2020).CAS 

Google Scholar 
Gong, J., Qi, J., E, B., Yin, Y. & Gao, D. Concentration, viability and size distribution of bacteria in atmospheric bioaerosols under different types of pollution. Environ. Pollut. 257, 113485 (2020).CAS 

Google Scholar 
Zhang, T., Li, X., Wang, M., Chen, H. & Yao, M. Time- and size-resolved bacterial aerosol dynamics in highly polluted air: new clues for haze formation mechanism. bioRxiv, 513093 (2019) https://doi.org/10.1101/513093.Wei, M. et al. Size distribution of bioaerosols from biomass burning emissions: Characteristics of bacterial and fungal communities in submicron (PM1.0) and fine (PM2.5) particles. Ecotoxicol. Environ. Saf. 171, 37–46 (2019).CAS 

Google Scholar 
Blazewicz, S. J., Barnard, R. L., Daly, R. A. & Firestone, M. K. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. Isme J. 7, 2061–2068 (2013).CAS 

Google Scholar 
Barnard, R. L., Osborne, C. A. & Firestone, M. K. Responses of soil bacterial and fungal communities to extreme desiccation and rewetting. ISME J. 7, 2229–2241 (2013).CAS 

Google Scholar 
Schostag, M. et al. Distinct summer and winter bacterial communities in the active layer of Svalbard permafrost revealed by DNA- and RNA-based analyses. Front. Microbiol. 6 (2015) https://doi.org/10.3389/fmicb.2015.00399.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl Acad. Sci. 108, 12776–12781 (2011).CAS 

Google Scholar 
Denef, V. J., Fujimoto, M., Berry, M. A. & Schmidt, M. L. Seasonal succession leads to habitat-dependent differentiation in ribosomal RNA:DNA Ratios among freshwater lake bacteria. Front. Microbiol.7 (2016) https://doi.org/10.3389/fmicb.2016.00606.Zhang, Y., Zhao, Z., Dai, M., Jiao, N. & Herndl, G. J. Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea. Mol. Ecol. 23, 2260–2274 (2014).CAS 

Google Scholar 
Hospodsky, D., Yamamoto, N. & Peccia, J. Accuracy, precision, and method detection limits of quantitative PCR for airborne bacteria and fungi. Appl Environ. Microbiol 76, 7004–7012 (2010).CAS 

Google Scholar 
Nieto-Caballero, M., Savage, N., Keady, P. & Hernandez, M. High fidelity recovery of airborne microbial genetic materials by direct condensation capture into genomic preservatives. J. Microbiological Methods 157, 1–3 (2019).CAS 

Google Scholar 
Behzad, H., Gojobori, T. & Mineta, K. Challenges and opportunities of airborne metagenomics. Genome Biol. Evol. 7, 1216–1226 (2015).CAS 

Google Scholar 
Šantl-Temkiv, T., Gosewinkel, U., Starnawski, P., Lever, M. & Finster, K. Aeolian dispersal of bacteria in southwest Greenland: their sources, abundance, diversity and physiological states. FEMS Microbiol. Ecol. 94 (2018) https://doi.org/10.1093/femsec/fiy031.Klein, A. M., Bohannan, B. J. M., Jaffe, D. A., Levin, D. A. & Green, J. L. Molecular evidence for metabolically active bacteria in the atmosphere. Front. Microbiol. 7, 772–772 (2016).
Google Scholar 
Amato, P. et al. Active microorganisms thrive among extremely diverse communities in cloud water. PLoS One 12, e0182869 (2017).
Google Scholar 
Vellend, B. M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).
Google Scholar 
Bodenheimer, S., Lensky, I. M. & Dayan, U. Characterization of Eastern Mediterranean dust storms by area of origin; North Africa vs. Arabian Peninsula. Atmos. Environ. 198, 158–165 (2019).CAS 

Google Scholar 
Kishcha, P., Volpov, E., Starobinets, B., Alpert, P. & Nickovic, S. Dust dry deposition over Israel. Atmosphere 11, 197 (2020).
Google Scholar 
Krasnov, H., Katra, I. & Friger, M. Increase in dust storm related PM10 concentrations: A time series analysis of 2001-2015. Environ. Pollut. 213, 36–42 (2016).CAS 

Google Scholar 
Zittis, G. et al. Climate change and weather extremes in the eastern Mediterranean and Middle East. Rev. Geophysics 60, e2021RG000762 (2022).
Google Scholar 
Griffin, D. W. Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clin. Microbiol Rev. 20, 459–477 (2007).
Google Scholar 
Prospero, J. M. Long-range transport of mineral dust in the global atmosphere: Impact of African dust on the environment of the southeastern United States. Proc. Natl. Acad. Sci. 96, 3396–3403 (1999).CAS 

Google Scholar 
Klingmüller, K., Pozzer, A., Metzger, S., Stenchikov, G. L. & Lelieveld, J. Aerosol optical depth trend over the Middle East. Atmos. Chem. Phys. 16, 5063–5073 (2016).
Google Scholar 
Notaro, M., Alkolibi, F., Fadda, E. & Bakhrjy, F. Trajectory analysis of Saudi Arabian dust storms. J. Geophys. Res. Atmospheres 118, 6028–6043 (2013).
Google Scholar 
Tegen, I. & Schepanski, K. The global distribution of mineral dust. IOP Conf. Ser. Earth Environ. Sci. 7, 012001 (2009).
Google Scholar 
Klappenbach, J. A., Saxman, P. R., Cole, J. R. & Schmidt, T. M. rrndb: the Ribosomal RNA Operon Copy Number Database. Nucleic Acids Res. 29, 181–184 (2001).CAS 

Google Scholar 
Bremer, H. & Dennis, P. P. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus 3 (2008) https://doi.org/10.1128/ecosal.5.2.3.Schneider, D. A., Ross, W. & Gourse, R. L. Control of rRNA expression in Escherichia coli. Curr. Opin. Microbiol 6, 151–156 (2003).CAS 

Google Scholar 
Gralla, J. D. Escherichia coli ribosomal RNA transcription: regulatory roles for ppGpp, NTPs, architectural proteins and a polymerase-binding protein. Mol. Microbiol 55, 973–977 (2005).CAS 

Google Scholar 
Oliveira, A. et al. Insight of genus corynebacterium: ascertaining the role of pathogenic and non-pathogenic species. Front. Microbiol. 8, 1937–1937 (2017).
Google Scholar 
Wexler, H. M. Bacteroides: the good, the bad, and the nitty-gritty. Clin. Microbiol Rev. 20, 593–621 (2007).CAS 

Google Scholar 
Duar, R. M. et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol. Rev. 41, S27–S48 (2017).
Google Scholar 
Magzal, F. et al. Increased physical activity improves gut microbiota composition and reduces short-chain fatty acid concentrations in older adults with insomnia. Sci. Rep. 12, 2265 (2022).CAS 

Google Scholar 
Wang, L. et al. Increased abundance of Sutterella spp. and Ruminococcus torques in feces of children with autism spectrum disorder. Mol. Autism 4, 42 (2013).CAS 

Google Scholar 
Tavella, T. et al. Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly. Gut microbes 13, 1–19 (2021).
Google Scholar 
Bennur, T., Kumar, A. R., Zinjarde, S. & Javdekar, V. Nocardiopsis species: Incidence, ecological roles and adaptations. Microbiological Res. 174, 33–47 (2015).
Google Scholar 
Jones, S. E. & Elliot, M. A. Streptomyces exploration: competition, volatile communication and new bacterial behaviours. Trends Microbiol. 25, 522–531 (2017).CAS 

Google Scholar 
Gtari, M. et al. Contrasted resistance of stone-dwelling Geodermatophilaceae species to stresses known to give rise to reactive oxygen species. FEMS Microbiol. Ecol. 80, 566–577 (2012).CAS 

Google Scholar 
Weon, H.-Y. et al. Adhaeribacter aerophilus sp. nov., Adhaeribacter aerolatus sp. nov. and Segetibacter aerophilus sp. nov., isolated from air samples. Int. J. Syst. Evolut. Microbiol. 60, 2424–2429 (2010).CAS 

Google Scholar 
Marín, I. et al.) 115-133 (Springer Berlin Heidelberg, 2014).Yoon, J.-H. et al.) 1099-1113 (Springer New York, 2006).Steinberg, J. P. & Burd, E. M. in Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases (Eighth Edition) (eds John E. Bennett, R. Dolin, & M. J. Blaser) 2667-2683.e2664 (W.B. Saunders, 2015).Kelly, D. P., et al.) 232-249 (Springer New York, 2006).Silby, M. W., Winstanley, C., Godfrey, S. A. C., Levy, S. B. & Jackson, R. W. Pseudomonas genomes: diverse and adaptable. FEMS Microbiol. Rev. 35, 652–680 (2011).CAS 

Google Scholar 
Hyeon, J. W. & Jeon, C. O. Roseomonas aerofrigidensis sp. nov., isolated from an air conditioner. Int. J. Syst. Evolut. Microbiol. 67, 4039–4044 (2017).CAS 

Google Scholar 
Battista, J. R. & Rainey, F. A. in Bergey’s Manual of Systematics of Archaea and Bacteria 1-13.Angly, F. E. et al. Marine microbial communities of the Great Barrier Reef lagoon are influenced by riverine floodwaters and seasonal weather events. PeerJ 4, e1511 (2016).
Google Scholar 
Cárdenas, A., Rodriguez-R, L. M., Pizarro, V., Cadavid, L. F. & Arévalo-Ferro, C. Shifts in bacterial communities of two caribbean reef-building coral species affected by white plague disease. ISME J. 6, 502–512 (2012).
Google Scholar 
Kämpfer, P., Lodders, N., Huber, B., Falsen, E. & Busse, H. J. Deinococcus aquatilis sp. nov., isolated from water. Int J. Syst. Evol. Microbiol 58, 2803–2806 (2008).
Google Scholar 
Gallego, V., Sánchez-Porro, C., García, M. T. & Ventosa, A. Roseomonas aquatica sp. nov., isolated from drinking water. Int J. Syst. Evol. Microbiol 56, 2291–2295 (2006).CAS 

Google Scholar 
Roskin, J., Katra, I. & Blumberg, D. G. Particle-size fractionation of eolian sand along the Sinai–Negev erg of Egypt and Israel. GSA Bull. 126, 47–65 (2014).
Google Scholar 
Ganor, E. & Foner, H. A. Mineral dust concentrations, deposition fluxes and deposition velocities in dust episodes over Israel. J. Geophys. Res.: Atmospheres 106, 18431–18437 (2001).CAS 

Google Scholar 
Amir, A., Ozel, E., Haberman, Y. & Shental, N. Achieving pan-microbiome biological insights via the dbBact knowledge base. bioRxiv, 2022.2002.2027.482174 (2022) https://doi.org/10.1101/2022.02.27.482174.Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27, 105–117 (2019).CAS 

Google Scholar 
Labeda, D. P. & Goodfellow, M. in Bergey’s Manual of Systematics of Archaea and Bacteria 1-7.Rickard, A. H. et al. Adhaeribacter aquaticus gen. nov., sp. nov., a Gram-negative isolate from a potable water biofilm. Int J. Syst. Evol. Microbiol 55, 821–829 (2005).CAS 

Google Scholar 
Guo, L. et al. Oligotrophic bacterium Hymenobacter latericoloratus CGMCC 16346 degrades the neonicotinoid imidacloprid in surface water. AMB Express 10, 7 (2020).CAS 

Google Scholar 
Philippon, T. et al. Denitrifying bio-cathodes developed from constructed wetland sediments exhibit electroactive nitrate reducing biofilms dominated by the genera Azoarcus and Pontibacter. Bioelectrochemistry 140, 107819 (2021).CAS 

Google Scholar 
Jurado, V., Miller, A. Z., Alias-Villegas, C., Laiz, L. & Saiz-Jimenez, C. Rubrobacter bracarensis sp. nov., a novel member of the genus Rubrobacter isolated from a biodeteriorated monument. Syst. Appl Microbiol 35, 306–309 (2012).CAS 

Google Scholar 
de Vries, H. J. et al. Isolation and characterization of Sphingomonadaceae from fouled membranes. npj Biofilms Microbiomes 5, 6 (2019).
Google Scholar 
Vacca, M. et al. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms 8, 573 (2020).CAS 

Google Scholar 
Baldani, J. I. et al. in The Prokaryotes: Alphaproteobacteria and Betaproteobacteria (eds Eugene Rosenberg et al.) 919-974 (Springer Berlin Heidelberg, 2014).Dastager, S. G., et al.) 455-498 (Springer Berlin Heidelberg, 2014).Ivanova, N. et al. Complete genome sequence of Geodermatophilus obscurus type strain (G-20). Stand Genom. Sci. 2, 158–167 (2010).
Google Scholar 
Alonso-Reyes, D. et al. Genomic Insights of an Andean Multi-resistant Soil Actinobacterium of Biotechnological Interest. bioRxiv, 2020.2012.2021.423370 (2020) https://doi.org/10.1101/2020.12.21.423370.Kumar, C. G. & Sujitha, P. Kocuran, an exopolysaccharide isolated from Kocuria rosea strain BS-1 and evaluation of its in vitro immunosuppression activities. Enzym. Micro. Technol. 55, 113–120 (2014).CAS 

Google Scholar 
Raguénès, G. et al. A novel exopolymer-producing bacterium, Paracoccus zeaxanthinifaciens subsp. payriae, isolated from a “kopara” mat located in Rangiroa, an atoll of French Polynesia. Curr. Microbiol 49, 145–151 (2004).
Google Scholar 
Bailey, A. C. et al. Draft Genome Sequence of Massilia sp. Strain BSC265, Isolated from Biological Soil Crust of Moab, Utah. Genome Announc 2, e01199–01114 (2014).
Google Scholar 
Denef, V. J., Fujimoto, M., Berry, M. A. & Schmidt, M. L. Seasonal succession leads to habitat-dependent differentiation in ribosomal RNA:DNA Ratios among freshwater lake bacteria. Front Microbiol 7, 606 (2016).
Google Scholar 
Salazar, G. et al. Particle-association lifestyle is a phylogenetically conserved trait in bathypelagic prokaryotes. Mol. Ecol. 24, 5692–5706 (2015).
Google Scholar 
Schmidt, M. L., White, J. D. & Denef, V. J. Phylogenetic conservation of freshwater lake habitat preference varies between abundant bacterioplankton phyla. Environ. Microbiol. 18, 1212–1226 (2016).
Google Scholar 
Stepanauskas, R. et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).
Google Scholar 
Doughari, H. J., Ndakidemi, P. A., Human, I. S. & Benade, S. The ecology, biology and pathogenesis of Acinetobacter spp.: an overview. Microbes Environ. 26, 101–112 (2011).
Google Scholar 
Bläckberg, A., Falk, L., Oldberg, K., Olaison, L. & Rasmussen, M. infective endocarditis due to corynebacterium species: clinical features and antibiotic resistance. Open Forum Infect. Dis. 8 (2021) https://doi.org/10.1093/ofid/ofab055.Zhang, Q. et al. Hymenobacter xinjiangensis sp. nov., a radiation-resistant bacterium isolated from the desert of Xinjiang, China. Int J. Syst. Evol. Microbiol 57, 1752–1756 (2007).CAS 

Google Scholar 
Lee, J.-J. et al. Hymenobacter aquaticus sp. nov., a radiation-resistant bacterium isolated from a river. Int. J. Syst. Evolut. Microbiol. 67, 1206–1211 (2017).CAS 

Google Scholar 
Alessa, O. et al. Comprehensive comparative genomics and phenotyping of methylobacterium species. Front. Microbiol. 12 (2021) https://doi.org/10.3389/fmicb.2021.740610.Titécat, M., Wallet, F., Vieillard, M. H., Courcol, R. J. & Loïez, C. Ruminococcus gnavus: an unusual pathogen in septic arthritis. Anaerobe 30, 159–160 (2014).
Google Scholar 
Weber, B. S., Harding, C. M. & Feldman, M. F. Pathogenic acinetobacter: from the cell surface to infinity and beyond. J. Bacteriol. 198, 880–887 (2015).
Google Scholar 
Hacker, E., Antunes, C. A., Mattos-Guaraldi, A. L., Burkovski, A. & Tauch, A. Corynebacterium ulcerans, an emerging human pathogen. Future Microbiol. 11, 1191–1208 (2016).CAS 

Google Scholar 
Smith, K. F. & Oram, D. M. in Encyclopedia of Microbiology (Third Edition) (ed Moselio Schaechter) 94-106 (Academic Press, 2009).Kovaleva, J., Degener, J. E. & van der Mei, H. C. Methylobacterium and its role in health care-associated infection. J. Clin. Microbiol 52, 1317–1321 (2014).
Google Scholar 
Dyer, J. & Harris, P. Paracoccus yeei—An emerging pathogen or incidental finding? Pathology 52, S123 (2020).
Google Scholar 
Moradali, M. F., Ghods, S. & Rehm, B. H. A. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front. Cellular Infect. Microbiol. 7 (2017) https://doi.org/10.3389/fcimb.2017.00039.Ryan, M. P. & Adley, C. C. Sphingomonas paucimobilis: a persistent Gram-negative nosocomial infectious organism. J. Hosp. Infect. 75, 153–157 (2010).CAS 

Google Scholar 
Souto, A., Guinda, M., Mera, A. & Pardo, F. Septic arthritis caused by Sphingomonas paucimobilis in an immunocompetent patient. Reumatol. Clin. 8, 378–379 (2012).
Google Scholar 
Lanoix, J. P. et al. Sphingomonas paucimobilis bacteremia related to intravenous human immunoglobulin injections. Med Mal. Infect. 42, 37–39 (2012).
Google Scholar 
van Bruggen, A. H., Brown, P. R. & Jochimsen, K. N. Corky root of lettuce caused by strains of a gram-negative bacterium from muck soils of Florida, new york, and wisconsin. Appl Environ. Microbiol 55, 2635–2640 (1989).
Google Scholar 
VAN BRUGGEN, A. H. C., JOCHIMSEN, K. N. & BROWN, P. R. Rhizomonas suberifaciens gen. nov., sp. nov., the Causal Agent of Corky Root of Lettuce. Int. J. Syst. Evolut. Microbiol. 40, 175–188 (1990).
Google Scholar 
Davis, J. H. & Williamson, J. R. Structure and dynamics of bacterial ribosome biogenesis. Philos. Trans. R Soc. Lond. B Biol. Sci. 372 (2017) https://doi.org/10.1098/rstb.2016.0181.Maitra, A. & Dill, K. A. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc. Natl Acad. Sci. 112, 406–411 (2015).CAS 

Google Scholar 
Klumpp, S. & Hwa, T. Traffic patrol in the transcription of ribosomal RNA. RNA Biol. 6, 392–394 (2009).CAS 

Google Scholar 
Jia, Y. et al. Rare taxa exhibit disproportionate cell-level metabolic activity in enriched anaerobic digestion microbial communities. mSystems 4, e00208–e00218 (2019).CAS 

Google Scholar 
Zhou, Y. et al. Profiling airborne microbiota in mechanically ventilated buildings across seasons in hong kong reveals higher metabolic activity in low-abundance bacteria. Environ. Sci. Technol. 55, 249–259 (2021).CAS 

Google Scholar 
Fessler, M., Gummesson, B., Charbon, G., Svenningsen, S. L. & Sørensen, M. A. Short-term kinetics of rRNA degradation in Escherichia coli upon starvation for carbon, amino acid or phosphate. Mol. Microbiol. 113, 951–963 (2020).CAS 

Google Scholar 
Lahtinen, S. J. et al. Degradation of 16S rRNA and attributes of viability of viable but nonculturable probiotic bacteria. Lett. Appl Microbiol 46, 693–698 (2008).CAS 

Google Scholar 
Li, R. et al. Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water. Sci. Rep. 7, 5752 (2017).
Google Scholar 
McKillip, J. L., Jaykus, L. A. & Drake, M. rRNA stability in heat-killed and UV-irradiated enterotoxigenic Staphylococcus aureus and Escherichia coli O157:H7. Appl Environ. Microbiol 64, 4264–4268 (1998).CAS 

Google Scholar 
Sheridan, G. E., Masters, C. I., Shallcross, J. A. & MacKey, B. M. Detection of mRNA by reverse transcription-PCR as an indicator of viability in Escherichia coli cells. Appl. Environ. Microbiol. 64, 1313–1318 (1998).CAS 

Google Scholar 
Villarino, A., Bouvet, O. M., Regnault, B., Martin-Delautre, S. & Grimont, P. A. D. Exploring the frontier between life and death in Escherichia coli: evaluation of different viability markers in live and heat- or UV-killed cells. Res Microbiol 151, 755–768 (2000).CAS 

Google Scholar 
Schostag, M. D., Albers, C. N., Jacobsen, C. S. & Priemé, A. Low turnover of soil bacterial rRNA at low temperatures. Front. Microbiol. 11 (2020) https://doi.org/10.3389/fmicb.2020.00962.Emerson, J. B. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 5, 86 (2017).
Google Scholar 
Wang, Y. et al. Characterizing microbial community viability with RNA-based high-throughput sequencing. Microbiome Version 1, posted 22 Jul, 2022 (2022) https://doi.org/10.21203/rs.3.rs-1870950/v1.Mbareche, H., Veillette, M., Bilodeau, G. J., Duchaine, C. & Schaffner, D. W. Bioaerosol sampler choice should consider efficiency and ability of samplers to cover microbial diversity. Appl. Environ. Microbiol. 84, e01589–01518 (2018).CAS 

Google Scholar 
Pan, M., Lednicky, J. A. & Wu, C.-Y. Collection, particle sizing and detection of airborne viruses. J. Appl. Microbiol. 127, 1596–1611 (2019).CAS 

Google Scholar 
Nieto-Caballero, M., Savage, N., Keady, P. & Hernandez, M. High fidelity recovery of airborne microbial genetic materials by direct condensation capture into genomic preservatives. J. Microbiol Methods 157, 1–3 (2019).CAS 

Google Scholar 
Šantl-Temkiv, T., Gosewinkel, U., Starnawski, P., Lever, M. & Finster, K. Aeolian dispersal of bacteria in southwest Greenland: their sources, abundance, diversity and physiological states. FEMS Microbiol Ecol 94 (2018) https://doi.org/10.1093/femsec/fiy031.Maki, T. et al. Aeolian dispersal of bacteria associated with desert dust and anthropogenic particles over continental and oceanic surfaces. J. Geophys. Res.: Atmospheres 124, 5579–5588 (2019).
Google Scholar 
Gonzalez-Martin, C., Teigell-Perez, N., Valladares, B. & Griffin, D. W. in Advances in Agronomy Vol. 127 (ed Donald Sparks) 1-41 (Academic Press, 2014).Tisch Environmental, I. (2004).Krasnov, H., Katra, I. & Friger, M. Increase in dust storm related PM10 concentrations: A time series analysis of 2001–2015. Environ. Pollut. 213, 36–42 (2016).CAS 

Google Scholar 
Varga, G., Újvári, G. & Kovács, J. Spatiotemporal patterns of Saharan dust outbreaks in the Mediterranean Basin. Aeolian Res. 15, 151–160 (2014).
Google Scholar 
Dayan, U. & Levy, I. Relationship between synoptic-scale atmospheric circulation and ozone concentrations over Israel. J. Geophys. Res.: Atmospheres 107, ACL 31-31–ACL 31-12 (2002).
Google Scholar 
Klein, A. M., Bohannan, B. J. M., Jaffe, D. A., Levin, D. A. & Green, J. L. Molecular Evidence for Metabolically Active Bacteria in the Atmosphere. Front. Microbiol. 7 (2016) https://doi.org/10.3389/fmicb.2016.00772.Luhung, I. et al. Experimental parameters defining ultra-low biomass bioaerosol analysis. npj Biofilms Microbiomes 7, 37 (2021).CAS 

Google Scholar 
Stein, A. F. et al. Noaa’s Hysplit Atmospheric Transport and Dispersion Modeling System. Bull. Am. Meteorological Soc. 96, 2059–2077 (2015).
Google Scholar 
Rolph, G., Stein, A. & Stunder, B. Real-time Environmental Applications and Display sYstem: READY. Environ. Model. Softw. 95, 210–228 (2017).
Google Scholar 
Acker, J. G. & Leptoukh, G. Online analysis enhances use of NASA Earth science data. Eos, Trans. Am. Geophys. Union 88, 14–17 (2007).
Google Scholar 
Brauer, S. L. et al. Culturable Rhodobacter and Shewanella species are abundant in estuarine turbidity maxima of the Columbia River. Environ. Microbiol. 13, 589–603 (2011).CAS 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. Isme J. 6, 1621–1624 (2012).CAS 

Google Scholar 
Soergel, D. A. W., Dey, N., Knight, R. & Brenner, S. E. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 6, 1440–1444 (2012).CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
Google Scholar 
Martin-Fernandez, J. A., Hron, K., Templ, M., Filzmoser, P. & Palarea-Albaladejo, J. Bayesian-multiplicative treatment of count zeros in compositional data sets. Stat. Model. 15 (2015) https://doi.org/10.1177/1471082×14535524.Palarea-Albaladejo, J. & Martin-Fernandez, J. A. zCompositions—R Package for multivariate imputation of left-censored data under a compositional approach. Chemometrics Intell. Lab. Syst. 143, 85–96 (2015).CAS 

Google Scholar 
van den Boogaart, K. G. & Tolosana-Delgado, R. “compositions”: A unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).
Google Scholar 
Amato, P. et al. In Microbiology of Aerosols 1–21 (2017).Rao, A. K. & Whitby, K. T. Nonideal collection characteristics of single stage and cascade impactors. Am. Ind. Hyg. Assoc. J. 38, 174–179 (1977).CAS 

Google Scholar 
Jari Oksanen, F. G. B. et al. vegan: Community Ecology Package. (2020).Gilmour, S. G. In Wiley StatsRef: Statistics Reference Online.Margolin, B. H. In Wiley StatsRef: Statistics Reference Online.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).
Google Scholar 
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLOS Comput. Biol. 17, e1009442 (2021).CAS 

Google Scholar  More

In 2008, I had just begun volunteering at Equilibrio Azul — a non-profit marine-research and -conservation organization based in Quito — when colleagues discovered a hawksbill sea turtle (Eretmochelys imbricata) nesting at La Playita beach in Ecuador. The eastern Pacific population of hawksbill sea turtles is one of the most endangered in the world and was considered functionally extinct in the region before this turtle and others were observed.That discovery was a tipping point for hawksbill research in Ecuador and throughout the Pacific Ocean. Since 2008, we’ve found about 20 nests each year at La Playita, and one season, we documented 50.We have tagged 11 adult females with satellite transmitters. Previously, most of our understanding of these turtles had been based on observations in the Caribbean, where the reptiles are strictly coral-reef dwellers. But Ecuador’s reefs are mostly rocky, with patches of coral, and we were surprised to see females migrate south to mangroves, mainly for food.
Women in science
In this image, we have just attached a transmitter to a baby turtle — a first for hawksbill turtles this young and in the eastern Pacific region. We did not know much about hawksbills at this young age. It is tricky working with baby turtles, because they grow very fast, and the transmitters, which give us location data, can easily fall off. We’ve used cement to glue the devices to the shells of six newborns so far. The longest the transmitters have lasted is three months and the shortest period was only six weeks — but the devices provided our first insights into the ‘lost years’ of sea-turtle biology.Our findings have overturned assumptions that neonates were just carried along by currents. Instead, we found that one-day-old turtles can swim against the current. They aim for a specific direction — north by northwest — as they learn to dive and swim. We tracked one-year-old hawksbills to Costa Rican waters, a journey of roughly 2,000 kilometres, before we lost their signal.Cristina Miranda is a scientific coordinator at Equilibrio Azul in Quito, Ecuador. Interview by Virginia Gewin. More

Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
CAS 

Google Scholar 
Gadgil, M., Berkes, F. & Folke, C. Indigenous knowledge for biodiversity conservation. Ambio 22, 151–156 (1993).
Google Scholar 
Gadgil, M., Berkes, F. & Folke, C. Indigenous knowledge: from local to global. Ambio 50, 967–969 (2021).Article 

Google Scholar 
The IPBES regional assessment report on biodiversity and ecosystem services for the Americas. IPBES https://doi.org/10.5281/zenodo.3236252 (2018).Claes, J. et al. Valuing nature conservation: a methodology for quantifying the benefits of protecting the planet’s natural capital (McKinsey & Company, 2020).Retsa, A., Schelske, O., Wilke, B., Rutherford, G. & de Jong, R. Biodiversity and ecosystem services: a business case for re/insurance (Swiss Re, 2020).Petersson, M. & Stoett, P. Lessons learnt in global biodiversity governance. Int. Environ. Agreem. Polit. Law Econ. 22, 333–352 (2022).
Google Scholar 
Dasgupta, P. The economics of biodiversity: the Dasgupta review. GOV.UK www.gov.uk/official-documents. (2021).Furumo, P. R. & Lambin, E. F. Scaling up zero-deforestation initiatives through public-private partnerships: a look inside post-conflict Colombia. Glob. Environ. Change 62, 1–13 (2020).Article 

Google Scholar 
Hale, T. & Roger, C. Orchestration and transnational climate governance. Rev. Int. Organ. 9, 59–82 (2014).Article 

Google Scholar 
Ring, I. & Barton, D. N. Economic instruments in policy mixes for biodiversity conservation and ecosystem governance. in Handbook of Ecological Economics (eds Martinez-Alier, J. & Muradian, R.) Ch, 17 (Edward Elgar, 2015).Von Essen, M. & Lambin, E. Jurisdictional approaches to sustainable resource use. Front. Ecol. Environ. 19, 159–167 (2021).Article 

Google Scholar 
Taylor, C., Pollard, S., Rocks, S. & Angus, A. Selecting policy instruments for better environmental regulation: a critique and future research agenda. Environ. Policy Gov. 22, 268–292 (2012).Article 

Google Scholar 
Ring, I. & Schröter-Schlaack, C. Instrument mixes for biodiversity policies. POLICYMIX Report https://policymix.nina.no (2011).Howlett, M. & Rayner, J. Design principles for policy mixes: cohesion and coherence in ‘new governance arrangements’. Policy Soc. 26, 1–18 (2007).
Google Scholar 
Soulé, M. The “new conservation”. Conserv. Biol. 27, 895–897 (2013).Article 

Google Scholar 
Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).Article 
CAS 

Google Scholar 
Runhaar, H., Driessen, P. & Uittenbroek, C. Towards a systematic framework for the analysis of environmental policy integration. Environ. Policy Gov. 24, 233–246 (2014).Article 

Google Scholar 
Visseren-Hamakers, I. J. Integrative governance: the relationships between governance instruments taking center stage. Environ. Plan. C. Polit. Space 36, 1341–1354 (2018).Article 

Google Scholar 
Lafferty, W. & Hovden, E. Environmental policy integration: towards an analytical framework. Environ. Polit. 12, 1–22 (2003).Article 

Google Scholar 
Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 

Google Scholar 
Decision adopted by the conference of the parties to the Convention on Biological Diversity. 14/3 Mainstreaming biodiversity in the energy and mining, infrastructure, manufacturing and processing sectors. Convention on Biological Diversity https://www.cbd.int/doc/decisions/cop-14/cop-14-dec-03-en.pdf (2018).Update of the zero draft of the post-2020 global biodiversity framework. Convention on Biological Diversity https://www.cbd.int/doc/c/3064/749a/0f65ac7f9def86707f4eaefa/post2020-prep-02-01-en.pdf (2020).Whitehorn, P. R. et al. Mainstreaming biodiversity: a review of national strategies. Biol. Conserv. 235, 157–163 (2019).Article 

Google Scholar 
Alpízar, F. et al. Mainstreaming of natural capital and biodiversity into planning and decision-making: cases from Latin America and the Caribbean (IDB, 2020).Daily, G. Nature’s Services (Island Press, 1997).Hill, R. et al. Working with indigenous, local and scientific knowledge in assessments of nature and nature’s linkages with people. Curr. Opin. Environ. Sustain. 43, 8–20 (2020).Article 

Google Scholar 
Baptiste, B. et al. Greening peace in Colombia. Nat. Ecol. Evol. 1, 1–3 (2017).Article 

Google Scholar 
Biodiversidad en cifras. Instituto Alexander von Humboldt https://cifras.biodiversidad.co/ (2022).Censo nacional de población y vivienda. Estadísticas para grupos étnicos. DANE https://www.dane.gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/grupos-etnicos/informacion-tecnica (2018).Boyd, E., Corbera, E. & Estrada, M. UNFCCC negotiations (pre-Kyoto to COP-9): what the process says about the politics of CDM-sinks. Int. Environ. Agreem. Polit. Law Econ. 8, 95–112 (2008).
Google Scholar 
Alvarez, C. F. et al. Evaluación nacional de biodiversidad y servicios ecosistémicos: resumen para tomadores de decisión. Instituto Alexander von Humboldt. http://www.humboldt.org.co/images/pdf/10721/RTDFinalv290621.pdf (2021).Lambin, E. F. et al. Effectiveness and synergies of policy instruments for land use governance in tropical regions. Glob. Environ. Change 28, 129–140 (2014).Article 

Google Scholar 
Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol. Conserv. 248, 108654 (2020).Article 

Google Scholar 
Laikre, L. Genetic diversity is overlooked in international conservation policy implementation. Conserv. Genet. 11, 349–354 (2010).Article 

Google Scholar 
Ministerio de Ambiente y Desarrollo Sostenible. Resolución 1912 del 15 de Septiembre de 2017, listado de especies silvestres amenazadas de la diversidad biológica colombiana continental y marino costera en el territorio nacional. (2017). https://www.minambiente.gov.co/wp-content/uploads/2021/10/resolucion-1912-de-2017.pdfNewton, A. C. Biodiversity risks of adopting resilience as a policy goal. Conserv. Lett. 9, 369–376 (2016).Article 

Google Scholar 
Jeanrenaud, S. Changing people/nature representations in international conservation discourses. IDS Bull. 33, 111–122 (2002).Article 

Google Scholar 
Louder, E. & Wyborn, C. Biodiversity narratives: stories of the evolving conservation landscape. Environ. Conserv. 47, 251–259 (2020).Article 

Google Scholar 
Bonilla-Mejía, L. & Higuera-Mendieta, I. Protected areas under weak institutions: evidence from Colombia. World Dev. 122, 585–596 (2019).Article 

Google Scholar 
African Development Bank Group et al. Joint statement by the Multilateral Development Banks at Paris, COP21. European Investment Bank https://www.eib.org/attachments/press/joint-mdb-statement-climate_nov-28_final.pdf (2021).Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

Google Scholar 
Friedman, K., Garcia, S. M. & Rice, J. Mainstreaming biodiversity in fisheries. Mar. Policy 95, 209–220 (2018).Article 

Google Scholar 
Turismo de naturaleza, oportunidad para conocer y proteger la biodiversidad de Colombia. MADS https://www.minambiente.gov.co/negocios-verdes/turismo-de-naturaleza-oportunidad-para-conocer-y-proteger-la-biodiversidad-de-colombia/ (2022).Pacheco, P., Schoneveld, G., Dermawan, A., Komarudin, H. & Djama, M. Governing sustainable palm oil supply: disconnects, complementarities, and antagonisms between state regulations and private standards. Regul. Gov. 14, 568–598 (2020).Article 

Google Scholar 
Peters, B. G. & Pierre, J. Developments in intergovernmental relations: towards multi-level governance. Policy Polit. 29, 131–135 (2001).Article 

Google Scholar 
Lustig, N. Fiscal redistribution in middle income countries. OECD Social, Employment and Migration Working Papers. 171 (2015).Mooney, H. A. & Cleland, E. E. The evolutionary impact of invasive species. Proc. Natl Acad. Sci. USA 98, 5446–5451 (2001).Article 
CAS 

Google Scholar 
Rule of law index 2020. World Justice Project https://worldjusticeproject.org/sites/default/files/documents/WJP-ROLI-2020-Online_0.pdf (2020).Recommendation of the council on policy coherence for sustainable development OECD/LEGAL/0381. OECD https://www.oecd.org/gov/pcsd/recommendation-on-policy-coherence-for-sustainable-development-eng.pdf (2019).Arellana, J., Oviedo, D., Guzman, L. A. & Alvarez, V. Urban transport planning and access inequalities: a tale of two Colombian cities. Res. Transp. Bus. Manag. https://doi.org/10.1016/j.rtbm.2020.100554 (2020).Leyes | Ministerio de Ambiente y Desarrollo Sostenible. MADS https://www.minambiente.gov.co/index.php/normativa/leyes (2021).Cavelier Adarve, I. & Rodríguez Becerra, M. in Nuevos Enfoques para el Estudio de las Relaciones Internacionales de Colombia (eds Tickner A.B. & Bitar, S.) Ch. 4 (Ediciones Uniandes-Universidad de los Andes, 2017).Política Nacional para la Gestión Integral de la biodiversidad y los Servicios Ecosistémicos (PNGIBSE) MADS (2012). https://www.minambiente.gov.co/wp-content/uploads/2021/10/Poli%CC%81tica-Nacional-de-Gestio%CC%81n-Integral-de-la-Biodiver.pdfPotts, J., Wenban-Smith, M. & Turley, L. State of sustainability initiatives review: standards and the extractive economy (IISD, 2018).Junguito Bonnet, R. El papel de los gremios en la economía colombiana. Rev. Desarro. Soc. 82, 103–131 (2019).Article 

Google Scholar 
Savvidou, G., Dzebo, A. & Atteridge, A. Aid Atlas: new tool to visualize development finance flows. JSTOR https://www.jstor.org/stable/resrep22982 (2019).BIOFIN- Movilizando recursos para la biodiversidad en Colombia, plan financiero. UNDP https://www.biofin.org/sites/default/files/content/knowledge_products/Plan%20Financiero%20Movilizando%20recursos%20para%20la%20biodiversidad%20en%20Colombia.pdf (2018).Echeverri, A. et al. Data for: a policy mix approach to biodiversity governance in Colombia (Dryad, 2022).Gibbs, G. Analyzing Qualitative Data (SAGE Publications, 2007).Maxwell, J. A. Qualitative Research Design: An Interactive Approach (SAGE Publications, 2012).Gould, R. K. et al. A protocol for eliciting nonmaterial values through a cultural ecosystem services frame. Conserv. Biol. 29, 575–586 (2015).Article 

Google Scholar 
Kremen, C. Managing ecosystem services: what do we need to know about their ecology? Ecol. Lett. 8, 468–479 (2005).Article 

Google Scholar 
Robinson, J. G. Ethical pluralism, pragmatism, and sustainability in conservation practice. Biol. Conserv. 144, 958–965 (2011).Article 

Google Scholar 
Sandbrook, C. What is conservation? Oryx 49, 565–566 (2015).Article 

Google Scholar 
Ricotta, C. et al. Measuring the functional redundancy of biological communities: a quantitative guide. Methods Ecol. Evol. 7, 1386–1395 (2016).Article 

Google Scholar 
Dı́az, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).Article 

Google Scholar  More

Levett, R. Sustainability indicators—integrating quality of life and environmental protection. J. R. Stat. Soc. A 161, 291–302 (1998).Article 

Google Scholar 
Harrison, P. A. Ecosystem services and biodiversity conservation: an introduction to the RUBICODE project. Biodivers. Conserv. 19, 2767–2772 (2010).Article 

Google Scholar 
Otero, I. et al. Biodiversity policy beyond economic growth. Conserv. Lett. 13, e12713 (2020).Article 

Google Scholar 
Seppelt, R., Lautenbach, S. & Volk, M. Identifying trade-offs between ecosystem services, land use, and biodiversity: a plea for combining scenario analysis and optimization on different spatial scales. Curr. Opin. Environ. Sustain. 5, 458–463 (2013).Article 

Google Scholar 
Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); accessed from https://ipbes.net/document-library-categoriesDinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).Article 

Google Scholar 
Ostrom, E. A general framework for analyzing sustainability of social-ecological systems. Science 325, 419–422 (2009).Article 
CAS 

Google Scholar 
Bennett, E. M. et al. Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Curr. Opin. Environ. Sustain. 14, 76–85 (2015).Article 

Google Scholar 
Haines-Young, R & Potschin, M. in Ecosystem Ecology: A New Synthesis (eds Raffaelli, D. & Frid, C.) 110–139 (Cambridge Univ. Press, 2010).
Google Scholar 
Tallis, H. M. & Kareiva, P. Shaping global environmental decisions using socio-ecological models. Trends Ecol. Evol. 21, 562–568 (2006).Article 

Google Scholar 
Steffen, W. et al. Trajectories of the Earth System in the Anthropocene. Proc. Natl Acad. Sci. USA 115, 8252–8259 (2018).Article 
CAS 

Google Scholar 
Wilson, K. A. et al. Conserving biodiversity efficiently: what to do, where, and when. PLoS Biol. 5, e223 (2007).Article 

Google Scholar 
Moilanen, A. et al. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proc. R. Soc. B 272, 1885–1891 (2005).Article 

Google Scholar 
Moilanen, A. et al. Balancing alternative land uses in conservation prioritization. Ecol. Appl. 21, 1419–1426 (2011).Article 

Google Scholar 
Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320, 222–226 (2008).Article 
CAS 

Google Scholar 
Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).Article 

Google Scholar 
Sayer, J. et al. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl Acad. Sci. USA 110, 8349–8356 (2013).Article 
CAS 

Google Scholar 
Watts, M. E. et al. Marxan with Zones: software for optimal conservation based land- and sea-use zoning. Environ. Model. Softw. 24, 1513–1521 (2009).Article 

Google Scholar 
Arkema, K. K. et al. Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc. Natl Acad. Sci. USA` 112, 7390–7395 (2015).Article 
CAS 

Google Scholar 
Wyborn, C. & Evans, M. C. Conservation needs to break free from global priority mapping. Nat. Ecol. Evol. 5, 1322–1324 (2021).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, e2602–e2610 (2013).Article 
CAS 

Google Scholar 
Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).Article 
CAS 

Google Scholar 
Silveira, F. A. et al. Biome Awareness Disparity is BAD for tropical ecosystem conservation and restoration. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.14060 (2021).Article 

Google Scholar 
Bond, W. J. & Parr, C. L. Beyond the forest edge: ecology, diversity and conservation of the grassy biomes. Biol. Conserv. 143, 2395–2404 (2010).Article 

Google Scholar 
Veach, V., Di Minin, E., Pouzols, F. M. & Moilanen, A. Species richness as criterion for global conservation area placement leads to large losses in coverage of biodiversity. Divers. Distrib. 23, 715–726 (2017).Article 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 

Google Scholar 
Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5, 1499–1509 (2021).Article 

Google Scholar 
First Draft of the Post-2020 Global Biodiversity Framework (CBD, 2021); accessed from www.cbd.int/conferences/post2020Westgate, M. J., Barton, P. S., Lane, P. W. & Lindenmayer, D. B. Global meta-analysis reveals low consistency of biodiversity congruence relationships. Nat. Commun. 5, 3899 (2014).Article 
CAS 

Google Scholar 
Cadotte, M. W. & Tucker, C. M. Difficult decisions: strategies for conservation prioritization when taxonomic, phylogenetic and functional diversity are not spatially congruent. Biol. Conserv. 225, 128–133 (2018).Article 

Google Scholar 
Madhusudhan, M. D. & Vanak, A. T. (2022). Mapping the distribution and extent of India’s semi-arid open natural ecosystems. Journal of Biogeography 00, 1–11; https://doi.org/10.1111/jbi.14471Wastelands Atlas of India 2019 (Department of Land Resources, Ministry of Rural Development and the National Remote Sensing Centre, Indian Space Research Organisation, Department of Space, Government of India, 2019); www.dolr.gov.in/documents/wasteland-atlas-of-indiaKrishnaswamy, J., John, R. & Joseph, S. Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Glob. Change Biol. 20, 203–215 (2014).Article 

Google Scholar 
Parida, B. R., Pandey, A. C. & Patel, N. R. Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. Climate 8, 92 (2020).Article 

Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

Google Scholar 
Martin, D. A. et al. Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot. Proc. Natl Acad. Sci. USA 119, e2107747119 (2022).Article 
CAS 

Google Scholar 
Pandit, M. K. & Grumbine, R. E. Potential effects of ongoing and proposed hydropower development on terrestrial biological diversity in the Indian Himalaya. Conserv. Biol. 26, 1061–1071 (2012).Article 

Google Scholar 
Nayak, R. et al. Bits and pieces: forest fragmentation by linear intrusions in India. Land Use Policy 99, 104619 (2020).Article 

Google Scholar 
Srinivasan, U. et al. Oil palm cultivation can be expanded while sparing biodiversity in India. Nat. Food 2, 442–447 (2021).Article 

Google Scholar 
Vasudev, D., Goswami, V. R., Srinivas, N., Syiem, B. L. N. & Sarma, A. Identifying important connectivity areas for the wide‐ranging Asian elephant across conservation landscapes of Northeast India. Divers. Distrib. 27, 2510–2526 (2021).Article 

Google Scholar 
Goswami, V. R., Vasudev, D., Joshi, B., Hait, P. & Sharma, P. Coupled effects of climatic forcing and the human footprint on wildlife movement and space use in a dynamic floodplain landscape. Sci. Total Environ. 758, 144000 (2021).Article 
CAS 

Google Scholar 
Rodrigues, R. G., Srivathsa, A. & Vasudev, D. Dog in the matrix: envisioning countrywide connectivity conservation for an endangered carnivore. J. Appl. Ecol. 59, 223–237 (2022).Article 

Google Scholar 
Ghosh-Harihar, M. et al. Protected areas and biodiversity conservation in India. Biol. Conserv. 237, 114–124 (2019).Article 

Google Scholar 
Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

Google Scholar 
Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).Article 
CAS 

Google Scholar 
Alves-Pinto, H. et al. Opportunities and challenges of other effective area-based conservation measures (OECMs) for biodiversity conservation. Perspect. Ecol. Conserv. 19, 115–120 (2021).
Google Scholar 
Joshi, A. A., Sankaran, M. & Ratnam, J. ‘Foresting’ the grassland: historical management legacies in forest-grassland mosaics in southern India, and lessons for the conservation of tropical grassy biomes. Biol. Conserv. 224, 144–152 (2018).Article 

Google Scholar 
Chisholm, R. A. Trade-offs between ecosystem services: water and carbon in a biodiversity hotspot. Ecol. Econ. 69, 1973–1987 (2010).Article 

Google Scholar 
Clark, B., DeFries, R. & Krishnaswamy, J. India’s commitments to increase tree and forest cover: consequences for water supply and agriculture production within the Central Indian Highlands. Water 13, 959 (2021).Article 

Google Scholar 
Paul, S., Ghosh, S., Rajendran, K. & Murtugudde, R. Moisture supply from the Western Ghats forests to water deficit east coast of India. Geophys. Res. Lett. 45, 4337–4344 (2018).Article 

Google Scholar 
Almond, R. E. A, Grooten, M., Juffe Bignoli, D. & Petersen, T. (eds) Living Planet Report 2022—Building a Nature-Positive Society (WWF, 2022).Srivathsa, A. et al. Opportunities for prioritizing and expanding conservation enterprise in India using a guild of carnivores as flagships. Environ. Res. Lett. 15, 064009 (2020).Article 

Google Scholar 
Vira, B. et al., Negotiating trade-offs: choices about ecosystem services for poverty alleviation. Econ. Polit. Wkly 67–75 (2012).Ravindranath, N. H. & Murthy, I. K. Greening India mission. Curr. Sci. 99, 444–449 (2010).
Google Scholar 
Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Glob. Environ. Change 71, 102368 (2021).Article 

Google Scholar 
Strassburg, B. B. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).Article 
CAS 

Google Scholar 
Belote, R. T. et al. Beyond priority pixels: delineating and evaluating landscapes for conservation in the contiguous United States. Landsc. Urban Plan. 209, 104059 (2021).Article 

Google Scholar 
Bawa, K. S. et al. Securing biodiversity, securing our future: a national mission on biodiversity and human well-being for India. Biol. Conserv. 253, 108867 (2021).Article 

Google Scholar 
Rodgers, W. A. & Panwar, H. S. Planning a Wildlife Protected Area Network in India. Vol. 1. A Report (Wildlife Institute of India, 1988).Watts, M., Klein, C. J., Tulloch, V. J., Carvalho, S. B. & Possingham, H. P. Software for prioritizing conservation actions based on probabilistic information. Conserv. Biol. 35, 1299–1308 (2021).Article 

Google Scholar 
Moilanen, A. et al. Zonation: spatial conservation planning methods and software. Version 4. User Manual. C-BIG; https://core.ac.uk/download/pdf/33733621.pdf (2014).Sierra-Altamiranda, A. et al. Spatial conservation planning under uncertainty using modern portfolio theory and Nash bargaining solution. Ecol. Model. 423, 109016 (2020).Article 

Google Scholar 
Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5, 415–424 (2022).Article 

Google Scholar 
Delavenne, J. et al. Systematic conservation planning in the eastern English Channel: comparing the Marxan and Zonation decision-support tools. ICES J. Mar. Sci. 69, 75–83 (2012).Article 

Google Scholar 
Roy, P. S. et al. Development of decadal (1985–1995–2005) land use and land cover database for India. Remote Sens. 7, 2401–2430 (2015).Article 

Google Scholar 
Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Government of India, 1968).BirdLife International World Database of Key Biodiversity Areas (KBA Partnership, version March 2021); accessed from www.keybiodiversityareas.org/kba-data/requestKoschke, L., Fürst, C., Frank, S. & Makeschin, F. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning. Ecol. Indic. 21, 54–66 (2012).Article 

Google Scholar 
Sarkar, T., Mishra, M. & Singh, R. B. in Regional Development Planning and Practice (eds Mishra, M. et al.) 205–232 (Springer, 2022). More