Philippa Kaur

Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Sci. 42, S3–S15 (2006).
Google Scholar 
Nielsen, U. N. et al. Response of belowground communities to short-term phosphorus addition in a phosphorus-limited woodland. Plant Soil 391, 321–331 (2015).
Google Scholar 
Nielsen, U. N., Ayres, E., Wall, D. H. & Bardgett, R. D. Soil biodiversity and carbon cycling: A review and synthesis of studies examining diversity function relationships. Eur. J. Soil Sci. 62, 105–116 (2011).
Google Scholar 
Lu, P. et al. Composition and structure of soil fauna communities and their relationships with environmental factors in copper mine waste rock after re-vegetation. Glob. Ecol. Conserv. 32, e01889 (2021).
Google Scholar 
Lin, D. et al. Soil fauna promote litter decomposition but do not alter the relationship between leaf economics spectrum and litter decomposability. Soil Biol. Biochem. 136, 107519 (2019).
Google Scholar 
Shao, Y., Zhang, W., Liu, S., Wang, X. & Fu, S. Diversity and function of soil fauna. Acta Ecol. Sin. (in Chinese) 35, 6614–6625 (2015).
Google Scholar 
Voronin, A. N. & Kotyak, P. A. Influence of different agricultural practices on the number of soil fauna and productivity of agricultural crops. Taurida Herald Agrar. Sci. 3, 49–56 (2019).
Google Scholar 
Zhu, X. & Zhu, B. Effect of different fertilization regimes on the main groups of soil fauna in cropland of purple soil. Sci. Agric. Sin. (in Chinese) 45, 911–920 (2015).
Google Scholar 
Islam, M. U., Guo, Z., Jiang, F. & Peng, X. Does straw return increase crop yield in the wheat-maize cropping system in China? A meta-analysis. Field Crop Res. 279, 108447 (2022).
Google Scholar 
Cui, H. et al. Straw return strategies to improve soil properties and crop productivity in a winter wheat-summer maize cropping system. Eur. J. Agron. 133, 126436 (2022).
Google Scholar 
Wang, X. et al. Changes in soil characteristics and maize yield under straw returning system in dryland farming. Field Crop Res. 218, 11–17 (2018).
Google Scholar 
Gai, X. et al. Contrasting impacts of long-term application of manure and crop straw on residual nitrate-N along the soil profile in the North China Plain. Sci. Total Environ. 650, 2251–2259 (2019).ADS 
PubMed 

Google Scholar 
Wang, W. et al. Effects of different fertility-building practices on the meso-micro soil fauna communities in a black soil area. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 1344–1351 (2019).
Google Scholar 
Kautz, T., López-Fando, C. & Ellmer, F. Abundance and biodiversity of soil microarthropods as influenced by different types of organic manure in a long-term field experiment in Central Spain. Appl. Soil Ecol. 33, 278–285 (2006).
Google Scholar 
Zhang, T. et al. Effects of straw returning on soil meso-and micro-arthropod community diversity in wheat-maize fields in North China. Chin. J. Appl. Environ. Biol. (in Chinese) 25, 70–75 (2019).
Google Scholar 
Yang, P., Wang, H. & Yue, J. Ecological distribution of middle-small-size soil faunas under conservation tillage and straw mulch conditions. Res. Soil Water Conserv. (in Chinese) 20, 145–150 (2013).
Google Scholar 
Zhu, Q., Zhu, A., Zhang, J., Zhang, H. & Zhang, C. Effect of conservation tillage on soil fauna in wheat field of Huang-huai-hai Plain. J. Agro Environ. Sci. (in Chinese) 28, 1766–1772 (2009).
Google Scholar 
Cao, Z. et al. Changes in the abundance and structure of a soil mite (Acari) community under long-term organic and chemical fertilizer treatments. Appl. Soil Ecol. 49, 131–138 (2011).
Google Scholar 
Li, Y., Xu, Z., Xu, H., Chen, Y. & Ruan, H. Review of the effect of fertilizer application on the soil fauna in soil ecosystems. J. Nanjing For. Univ. Nat. Sci. Ed. (in Chinese) 42, 179–184 (2018).
Google Scholar 
McGee, K. M. & Eaton, W. D. A comparison of the wet and dry season DNA-based soil invertebrate community characteristics in large patches of the bromeliad Bromelia pinguin in a primary forest in Costa Rica. Appl. Soil Ecol. 87, 99–107 (2015).
Google Scholar 
Zhu, B., Wang, T., You, X. & Gao, M. Nutrient release from weathering of purplish rocks in the Sichuan Basin, China. Pedosphere 18, 257–264 (2008).
Google Scholar 
Zhu, B. et al. Measurements of nitrate leaching from a hillslope cropland in the Central Sichuan Basin, China. Soil Sci. Soc. Am. J. 73, 1419–1426 (2009).ADS 

Google Scholar 
He, Y. Purple Soil of China Part (II) (Science Press, 2003).
Google Scholar 
Huang, R. et al. Responses of soil carbon pool and soil aggregates associated organic carbon to straw and straw-derived biochar addition in a dryland cropping mesocosm system. Agric. Ecosyst. Environ. 265, 576–586 (2018).
Google Scholar 
Zhu, X., Dong, Z., Kuang, F. & Zhu, B. Effects of fertilization regimes on soil faunal communities in cropland of purple soil. Acta Ecol. Sin. (in Chinese) 33, 464–474 (2013).
Google Scholar 
Querner, P. & Bruckner, A. Combining pitfall traps and soil samples to collect Collembola for site scale biodiversity assessments. Appl. Soil. Ecol. 45, 293–297 (2010).
Google Scholar 
Smith, M. A. et al. Extreme diversity of tropical parasitoid wasps exposed by iterative integration of natural history, DNA barcoding, morphology, and collections. PNAS 105, 12359–12364 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, C. A. et al. Meiofaunal diversity in the Atlantic Forest soil: A quest for nematodes in a native reserve using eukaryotic metabarcoding analysis. For. Ecol. Manag. 453, 117591 (2019).
Google Scholar 
Ding, J. et al. Effects of long-term fertilization on the associated microbiota of soil collembolan. Soil Biol. Biochem. 130, 141–149 (2019).
Google Scholar 
Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).
Google Scholar 
McGee, K. M., Porter, T. M., Wright, M. & Hajibabaei, M. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Sci. Rep. 10, 18429 (2020).PubMed 
PubMed Central 

Google Scholar 
Porter, T. M. et al. Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators. Sci. Rep. 9, 18218 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Morise, H., Miyazaki, E., Yoshimitsu, S. & Eki, T. Profiling nematode communities in unmanaged flowerbed and agricultural field soils in Japan by DNA barcode sequencing. PLoS One 7, e51785 (2012).ADS 
PubMed 
PubMed Central 

Google Scholar 
Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
PubMed Central 

Google Scholar 
Dopheide, A. et al. Estimating the biodiversity of terrestrial invertebrates on a forested island using DNA barcodes and metabarcoding data. Ecol. Appl. 29, e01877 (2019).PubMed 

Google Scholar 
Watts, C. et al. DNA metabarcoding as a tool for invertebrate community monitoring: A case study comparison with conventional techniques. Austral Entomol. 58, 675–686 (2019).
Google Scholar 
Kvist, S. Barcoding in the dark? A critical view of the sufficiency of zoological DNA barcoding databases and a plea for broader integration of taxonomic knowledge. Mol. Phylogenet. Evol. 69, 39–45 (2013).PubMed 

Google Scholar 
Shao, Y. et al. Nematodes as indicators of soil recovery in tailings of a lead/zinc mine. Soil Biol. Biochem. 40, 2040–2046 (2008).
Google Scholar 
Neher, D. A., Wu, J., Barbercheck, M. E. & Anas, O. Ecosystem type affects interpretation of soil nematode community measures. Appl. Soil Ecol. 30, 47–64 (2005).
Google Scholar 
Yang, C., Ji, Y., Wang, X., Yang, C. & Yu, D. W. Testing three pipelines for 18S rDNA-based metabarcoding of soil faunal diversity. Sci. China Life Sci. 56, 73–81 (2013).ADS 
PubMed 

Google Scholar 
Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).
Google Scholar 
Geisen, S., Laros, I., Vizcaino, A., Bonkowski, M. & de Groot, G. A. Not all are free-living: High-throughput DNA metabarcoding reveals a diverse community of protists parasitizing soil metazoa. Mol. Ecol. 24, 4556–4569 (2015).PubMed 

Google Scholar 
Clarke, L. J., Soubrier, J., Weyrich, L. S. & Cooper, A. Environmental metabarcodes for insects: In silico PCR reveals potential for taxonomic bias. Mol. Ecol. Resour. 14, 1160–1170 (2014).PubMed 

Google Scholar 
Kitagami, Y. & Matsuda, Y. High-throughput sequencing covers greater nematode diversity than conventional morphotyping on natural cedar forests in Yakushima Island, Japan. Eur. J. Soil Biol. 112, 103432 (2022).
Google Scholar 
Juliet, W. K., Lisa, B. F., Lamers, J. P. A., Till, S. & Christian, B. Soil fertility and biodiversity on organic and conventional smallholder farms in Kenya. Appl. Soil Ecol. 134, 85–97 (2019).
Google Scholar 
Li, Q., Zhou, D. & Chen, X. The accumulation decomposition and ecological effects of above-ground litter in terrestrial ecosystem. Acta Ecol. Sin. (in Chinese) 34, 3807–3819 (2014).
Google Scholar 
Tie, L. et al. Phosphorus addition reverses the negative effect of nitrogen addition on soil arthropods during litter decomposition in a subtropical forest. Sci. Total. Environ. 781, 146786 (2021).ADS 

Google Scholar 
Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).
Google Scholar 
Xiao, Q. et al. Impact of soil thickness on productivity and nitrate leaching from sloping cropland in the upper Yangtze River Basin. Agric. Ecosyst. Environ. 311, 107266 (2021).
Google Scholar 
Zhu, X. & Zhu, B. Diversity and abundance of soil fauna as influenced by long-term fertilization in cropland of purple soil, China. Soil Till. Res. 146, 39–46 (2015).
Google Scholar 
Wei, K., Wang, J., Dong, Z., Tang, J. & Zhu, B. The combined application of organic materials and chemical fertilizer mitigates the deterioration of the trophic structure of nematode community by increasing soil N concentration. J. Soil Sci. Plant Nutr. 21, 2530–2537 (2021).
Google Scholar 
Kuo, S. Phosphorus. In Methods of Soil Analysis (ed. Sparks, D. L.) 869–919 (Soil Science Society of America, 1996).
Google Scholar 
Nelson, D. W. & Sommers, L. E. Total carbon, organic carbon and organic matter. In Methods of Soil Analysis (ed. Sparks, D. L.) 960–1010 (ASA and SSSA, 1996).
Google Scholar 
Lu, R. Analysis of Soil Agro-Chemistry (Chinese Agricultural Science and Technology Press, 2000).
Google Scholar 
Page, A. L., Miller, R. H. & Keeney, D. R. Chemical and microbiological properties. In Methods of Soil Analysis (ASA and SSSA, 1982).
Google Scholar 
Olsen, S. R., Cole, C. U., Watanabe, F. S. & Deen, L. A. Estimation of Available Phosphorus in Soil by Extracting with Sodium Bicarbonate (USDA Circular 939, 1954).
Google Scholar 
Townshend, J. L. A modification and evaluation of the apparatus for the Oostenbrink direct cottonwool filter extraction method. Nematologica 9, 106–110 (1963).
Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).PubMed 

Google Scholar 
Yang, T., Song, X., Xu, X., Zhou, C. & Shi, A. A comparative analysis of spider prey spectra analyzed through the next-generation sequencing of individual and mixed DNA samples. Ecol. Evol. 11, 15444–15454 (2021).PubMed 
PubMed Central 

Google Scholar 
Chen, H. & Jiang, W. Application of high-throughput sequencing in understanding human oral microbiome related with health and disease. Front. Microbiol. 5, 508 (2014).PubMed 
PubMed Central 

Google Scholar 
Magoc, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).PubMed 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. http://www.r-project.org (2020).Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 

Google Scholar 
Margalef, R. Perspectives in Ecological Theory 111–119 (The University of Chicago Press, 1970).
Google Scholar 
Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 88, 131–144 (1966).ADS 

Google Scholar 
Zhou, Y. et al. Species richness and phylogenetic diversity of seed plants across vegetation zones of Mount Kenya, East Africa. Ecol. Evol. 8, 8930–8939 (2018).PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Nitrogen addition reduces soil bacterial richness, while phosphorus addition alters community composition in an old-growth N-rich tropical forest in southern China. Soil Biol. Biochem. 127, 22–30 (2018).
Google Scholar 
Yang, K. et al. Responses of soil ammonia-oxidizing bacteria and archaea diversity to N, P and NP fertilization: Relationships with soil environmental variables and plant community diversity. Soil Biol. Biochem. 145, 107795 (2020).
Google Scholar 
Zhang, S., Li, Q., Lü, Y., Zhang, X. & Liang, W. Contributions of soil biota to C sequestration varied with aggregate fractions under different tillage systems. Soil Biol. Biochem. 62, 147–156 (2013).
Google Scholar  More

Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More

Meghana, M. & Shastri, Y. Sustainable valorization of sugar industry waste: Status, opportunities, and challenges. Biores. Technol. 303, 122929 (2020).CAS 

Google Scholar 
Petrescu, D. C., Vermeir, I. & Petrescu-Mag, R. M. Consumer understanding of food quality, healthiness, and environmental impact: a cross-national perspective. IJERPH 17, 169 (2019).PubMed Central 

Google Scholar 
Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of conservation agriculture: justification, sustainability and uptake. Int. J. Agric. Sustain. 7, 292–320 (2009).
Google Scholar 
Malviya, M. K. et al. Sugarcane microbiome: role in sustainable production. In Microbiomes and Plant Health 225–242 (Elsevier, 2021). https://doi.org/10.1016/B978-0-12-819715-8.00007-0.Chapter 

Google Scholar 
Sandhu, H. S., Wratten, S. D. & Cullen, R. Organic agriculture and ecosystem services. Environ. Sci. Policy 13, 1–7 (2010).CAS 

Google Scholar 
Schipanski, M. E. et al. Balancing multiple objectives in organic feed and forage cropping systems. Agr. Ecosyst. Environ. 239, 219–227 (2017).
Google Scholar 
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
Chialva, M., Lanfranco, L. & Bonfante, P. The plant microbiota: composition, functions, and engineering. Curr. Opin. Biotechnol. 73, 135–142 (2022).CAS 
PubMed 

Google Scholar 
Dastogeer, K. M. G., Tumpa, F. H., Sultana, A., Akter, M. A. & Chakraborty, A. Plant microbiome–an account of the factors that shape community composition and diversity. Curr. Plant Biol. 23, 100161 (2020).
Google Scholar 
Yang, B., Wang, Y. & Qian, P.-Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformat. 17, 135 (2016).
Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7, 85 (2019).PubMed 
PubMed Central 

Google Scholar 
Praeg, N. & Illmer, P. Microbial community composition in the rhizosphere of Larix decidua under different light regimes with additional focus on methane cycling microorganisms. Sci. Rep. 10, 22324 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Souza, R. S. C. et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci. Rep. 6, 28774 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivars manipulate rhizosphere bacterial communities’ structure and composition of agriculturally important keystone taxa. 3 Biotech. 12, 32 (2022).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivar-dependent changes in assemblage of soil rhizosphere fungal communities in subtropical ecosystem. Environ. Sci. Pollut. Res. 29, 20795–20807 (2022).
Google Scholar 
Dakora, F. D., Matiru, V. N. & Kanu, A. S. Rhizosphere ecology of lumichrome and riboflavin, two bacterial signal molecules eliciting developmental changes in plants. Front. Plant Sci. 6, 700 (2015).PubMed 
PubMed Central 

Google Scholar 
Chapelle, E., Mendes, R., Bakker, P. A. H. & Raaijmakers, J. M. Fungal invasion of the rhizosphere microbiome. ISME J. 10, 265–268 (2016).CAS 
PubMed 

Google Scholar 
Teheran-Sierra, L. G. et al. Bacterial communities associated with sugarcane under different agricultural management exhibit a diversity of plant growth-promoting traits and evidence of synergistic effect. Microbiol. Res. 247, 126729 (2021).CAS 
PubMed 

Google Scholar 
de Carvalho, L. A. L. et al. Farming systems influence the compositional, structural, and functional characteristics of the sugarcane-associated microbiome. Microbiol. Res. 252, 126866 (2021).PubMed 

Google Scholar 
Henneron, L. et al. Fourteen years of evidence for positive effects of conservation agriculture and organic farming on soil life. Agron. Sustain. Dev. 35, 169–181 (2015).
Google Scholar 
Hartmann, M., Frey, B., Mayer, J., Mäder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane monoculture drives microbial community composition, activity and abundance of agricultural-related microorganisms. Environ. Sci. Pollut. Res. 28, 48080–48096 (2021).CAS 

Google Scholar 
Pang, Z. et al. Soil Metagenomics reveals effects of continuous sugarcane cropping on the structure and functional pathway of rhizospheric microbial community. Front. Microbiol. 12, 627569 (2021).PubMed 
PubMed Central 

Google Scholar 
Orr, C. H., Stewart, C. J., Leifert, C., Cooper, J. M. & Cummings, S. P. Effect of crop management and sample year on abundance of soil bacterial communities in organic and conventional cropping systems. J. Appl. Microbiol. 119, 208–214 (2015).CAS 
PubMed 

Google Scholar 
Brasil. Lei no 10.831, de 23 de dezembro de 2003. Dispõe sobre a agricultura orgânica e dá outras providências. In Publicado no Diário Oficial da União de 24/12/2003 (2003).Europea, C. Reglamento (CE) no 834/2007 del Consejo, de 28 de junio de 2007, sobre producción y etiquetado de los productos ecológicos y por el que se deroga el Reglamento (CEE) no 2092/91. D. Of. Unión Eur. 20, 1–23 (2007).
Google Scholar 
Council of the European Union. 889/2008, “Commission Regulation 889/2008/EC of 5 September 2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007 on organic production and labelling of organic products with regard to organic production, labelling and control”. Off. J. Eur. Union (L) 250, 18–19 (2007).
Google Scholar 
de Andrade, J. C., Cantarella, H. & Quaggio, J. A. Análise química para avaliação da fertilidade de solos tropicais. (2001).Donagema, G. K., de Campos, D. B., Calderano, S. B., Teixeira, W. G. & Viana, J. M. Manual de métodos de análise de solo. In Embrapa Solos-Documentos (INFOTECA-E) (2011).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2020). at R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). At Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).CAS 
PubMed 

Google Scholar 
Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
PubMed Central 

Google Scholar 
Renaud, G., Stenzel, U., Maricic, T., Wiebe, V. & Kelso, J. deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31, 770–772 (2015).CAS 
PubMed 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

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

Google Scholar 
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
PubMed 

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).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lahti, L. & Shetty, S. Microbiome R package. (2012).Oksanen, J. et al. vegan: Community Ecology Package. (2019). At Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2: an improved and extensible approach for metagenome inference. Bioinformatics https://doi.org/10.1101/672295 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kohl, M., Wiese, S. & Warscheid, B. Cytoscape: software for visualization and analysis of biological networks. In Data Mining in Proteomics (eds Hamacher, M. et al.) 291–303 (Humana Press, Totowa, NJ, 2011). https://doi.org/10.1007/978-1-60761-987-1_18.Chapter 

Google Scholar 
Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284 (2008).CAS 
PubMed 

Google Scholar 
Shen, Z. et al. Deep 16S rRNA pyrosequencing reveals a bacterial community associated with banana fusarium wilt disease suppression induced by bio-organic fertilizer application. PLoS One 9, e98420 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yun, Y. et al. The relationship between pH and bacterial communities in a single karst ecosystem and its implication for soil acidification. Front. Microbiol. 7, 1955 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, Y., Zeng, J., Zhu, Q., Zhang, Z. & Lin, X. pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci. Rep. 7, 40093 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, R. et al. Pyrosequencing reveals the influence of organic and conventional farming systems on bacterial communities. PLoS One 7, e51897 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bill, M., Chidamba, L., Gokul, J. K., Labuschagne, N. & Korsten, L. Bacterial community dynamics and functional profiling of soils from conventional and organic cropping systems. Appl. Soil. Ecol. 157, 103734 (2021).
Google Scholar 
Xun, W., Shao, J., Shen, Q. & Zhang, R. Rhizosphere microbiome: Functional compensatory assembly for plant fitness. Comput. Struct. Biotechnol. J. 19, 5487–5493 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Semenov, M. V., Krasnov, G. S., Semenov, V. M. & van Bruggen, A. Mineral and organic fertilizers distinctly affect fungal communities in the crop rhizosphere. JoF 8, 251 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Z., Li, Y., Li, T., Zhao, D. & Liao, Y. Tillage practices with different soil disturbance shape the rhizosphere bacterial community throughout crop growth. Soil Tillage Res. 197, 104501 (2020).
Google Scholar 
Gdanetz, K. & Trail, F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiom. J. 1, 158–168 (2017).
Google Scholar 
Lazcano, C. et al. The rhizosphere microbiome plays a role in the resistance to soil-borne pathogens and nutrient uptake of strawberry cultivars under field conditions. Sci. Rep. 11, 3188 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Leys, N. M. E. J. et al. Occurrence and phylogenetic diversity of Sphingomonas strains in soils contaminated with polycyclic aromatic hydrocarbons. Appl. Environ. Microbiol. 70, 1944–1955 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, C. et al. Role of bacterial communities in the natural suppression of rhizoctonia solani bare patch disease of wheat (Triticum aestivum L.). Appl. Environ. Microbiol. 79, 7428–7438 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart, A. & Hill, R. Applications of trichoderma in plant growth promotion. In Biotechnology and Biology of Trichoderma 415–428 (Elsevier, 2014). https://doi.org/10.1016/B978-0-444-59576-8.00031-X.Chapter 

Google Scholar 
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).CAS 

Google Scholar 
Andargie, M., Congyi, Z., Yun, Y. & Li, J. Identification and evaluation of potential bio-control fungal endophytes against Ustilagonoidea virens on rice plants. World J. Microbiol. Biotechnol. 33, 120 (2017).PubMed 

Google Scholar 
Orrù, L. et al. How tillage and crop rotation change the distribution pattern of fungi. Front. Microbiol. 12, 634325 (2021).PubMed 
PubMed Central 

Google Scholar 
van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).PubMed 
PubMed Central 

Google Scholar 
Wang, W. et al. Consistent responses of the microbial community structure to organic farming along the middle and lower reaches of the Yangtze River. Sci. Rep. 6, 35046 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Silva, T. M. et al. Degradation of 2,4-D herbicide by microorganisms isolated from Brazilian contaminated soil. Braz. J. Microbiol. 38, 522–525 (2007).
Google Scholar 
Laura, M., Snchez-Salinas, E., Gonzlez, E. D. & Luisa, M. Pesticide biodegradation: mechanisms, genetics and strategies to enhance the process. In Biodegradation – Life of Science (ed. Chamy, R.) (InTech, 2013). https://doi.org/10.5772/56098.Chapter 

Google Scholar 
Upadhyay, L. S. B. & Dutt, A. Microbial detoxification of residual organophosphate pesticides in agricultural practices. In Microbial Biotechnology (eds Patra, J. K. et al.) 225–242 (Springer Singapore, Singapore, 2017). https://doi.org/10.1007/978-981-10-6847-8_10.Chapter 

Google Scholar 
Hassan, Y. I., Lepp, D., He, J. & Zhou, T. Draft genome sequences of Devosia sp. strain 17-2-E-8 and Devosia riboflavina strain IFO13584. Genome Announ. https://doi.org/10.1128/genomeA.00994-14 (2014).Article 

Google Scholar 
Talwar, C. et al. Defining the environmental adaptations of genus Devosia: insights into its expansive short peptide transport system and positively selected genes. Sci. Rep. 10, 1151 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, F., Chen, L., Zhang, J., Yin, J. & Huang, S. Bacterial community structure after long-term organic and inorganic fertilization reveals important associations between soil nutrients and specific taxa involved in nutrient transformations. Front. Microbiol. 8, 187 (2017).PubMed 
PubMed Central 

Google Scholar 
Ho, A., Lonardo, D. P. D. & Bodelier, P. L. E. Revisiting life strategy concepts in environmental microbial ecology. Microbiol. Ecol. https://doi.org/10.1093/femsec/fix006 (2017).Article 

Google Scholar 
Lupatini, M., Korthals, G. W., de Hollander, M., Janssens, T. K. S. & Kuramae, E. E. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front. Microbiol. 7, 2064 (2017).PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Eight years of manure fertilization favor copiotrophic traits in paddy soil microbiomes. Eur. J. Soil Biol. 106, 103352 (2021).CAS 

Google Scholar 
Fließbach, A., Oberholzer, H.-R., Gunst, L. & Mäder, P. Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric. Ecosyst. Environ. 118, 273–284 (2007).
Google Scholar 
Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Karanja, E. N. et al. Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya. PLoS One 15, e0236574 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Francioli, D. et al. Mineral versus organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1446 (2016).PubMed 
PubMed Central 

Google Scholar 
Paungfoo-Lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep. 5, 8678 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pang, Z. et al. Liming positively modulates microbial community composition and function of sugarcane fields. Agronomy 9, 808 (2019).CAS 

Google Scholar 
Aira, M., Gómez-Brandón, M., Lazcano, C., Bååth, E. & Domínguez, J. Plant genotype strongly modifies the structure and growth of maize rhizosphere microbial communities. Soil Biol. Biochem. 42, 2276–2281 (2010).CAS 

Google Scholar 
Ma, M. et al. Responses of fungal community composition to long-term chemical and organic fertilization strategies in Chinese Mollisols. MicrobiologyOpen 7, e00597 (2018).PubMed 
PubMed Central 

Google Scholar 
Bellenger, J. P., Darnajoux, R., Zhang, X. & Kraepiel, A. M. L. Biological nitrogen fixation by alternative nitrogenases in terrestrial ecosystems: a review. Biogeochemistry 149, 53–73 (2020).
Google Scholar 
Schmidt, J. E. et al. Effects of agricultural management on rhizosphere microbial structure and function in processing tomato plants. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01064-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
Lin, Y. et al. Nitrosospira cluster 8a plays a predominant role in the nitrification process of a subtropical Ultisol under long-term inorganic and organic fertilization. Appl. Environ. Microbiol. 84, e01031-e1118 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chu, H. et al. Community structure of ammonia-oxidizing bacteria under long-term application of mineral fertilizer and organic manure in a sandy loam soil. Appl. Environ. Microbiol. 73, 485–491 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Xun, W. et al. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 9, 35 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Urbanization is fast progressing in the Global South, requiring new solutions for infrastructure, services, industrial development and land and energy use for these regions. In this context, fast-growing cities in Africa can take on a leadership role in driving climate change mitigation and adaptation, disaster risk reduction and sustainable development.
Credit: Stefan Rotter / Alamy Stock PhotoCities in Africa and elsewhere in the Global South continue to grapple with the challenge of delivering equitable services, infrastructure, housing and action to respond to climate change extremes and disasters. One well-known problem is a mismatch between the pace of urban growth and the slower development of basic services and critical infrastructure. This results in, for example, deficient sanitation, water supply systems and localized waste management for large parts of the population, which in turn contribute substantially to heightened poverty and inequality. For inclusive, equitable, prosperous and climate-resilient cities, urban management needs to integrate low-income communities into the urban economy by ensuring access to water, sanitation, energy transition, waste management, poverty reduction and by improving resilience through innovative solutions.
Credit: Patrick J. Endres/Corbis Documentary/GettySuch an equitable urban transition requires changes in the urban infrastructure, and land and energy use, as well as water and ecosystem management. The key research question in this field is to find ways to ensure city-wide access to infrastructure and services, while minimizing emissions and resource use, and building resilience to climate change impacts. In this regard, cities in the Global South and Africa in particular can serve as examples for other parts of the world as they have the potential to adopt disruptive, innovative yet practical solutions to low emissions, resource minimization and resilience building.
Credit: Nature Picture Library / Alamy Stock PhotoFor example, rapid urbanization creates the opportunity to develop economic structures in African cities that strongly integrate waste by promoting recovery, recycling, re-use and repair for lengthening lifecycles. Such a circular economy can create business opportunities, while also reducing resource use, thus creating a pathway for sustainable development. Another potential solution is hybrid systems for urban water management that are off-grid and utilize multiple water sources and treatment but that can also connect to centralized water systems. Business models for micro-to-medium enterprises have the potential to integrate some of the low-income groups through these kinds of technology and building social resilience.
Credit: Images of Africa Photobank / Alamy Stock PhotoThese examples are part of a broader assessment of urban infrastructure innovations, their disruption of centralized systems and rethinking of urban form for more compact, walkable, co-located land use for low carbon intensity towards net-zero cities. However, to translate research on these new solutions into action, a shift is necessary in the planning, governing and managing of cities so as to allow for opportunities for leapfrogging to emerge and expand the possibilities of urban development for inclusive and resilient African cities. More

Theurillat, J.-P. & Guisan, A. Potential impact of climate change on vegetation in the European Alps: A review. Clim. Change 50, 77–109 (2001).CAS 

Google Scholar 
Diaz, H. F. & Eischeid, J. K. Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophys. Res. Lett. 34, L18707 (2007).ADS 

Google Scholar 
Dirnböck, T., Essl, F. & Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Change Biol. 17, 990–996 (2011).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Pauli, H., Gottfried, M., Dirnböck, T., Dullinger, S. & Grabherr, G. Assessing the long-term dynamics of endemic plants at summit habitats. In Alpine Biodiversity in Europe (eds. Nagy, L., Grabherr, G., Körner, C., & Thompson, D. B.) 195–207 (Springer, 2003).Cogoni, D., Sulis, E., Bacchetta, G. & Fenu, G. The unpredictable fate of the single population of a threatened narrow endemic Mediterranean plant. Biodivers. Conserv. 28, 1799–1813 (2019).
Google Scholar 
Cursach, J., Besnard, A., Rita, J. & Fréville, H. Demographic variation and conservation of the narrow endemic plant Ranunculus weyleri. Acta Oecol. 53, 102–109 (2013).ADS 

Google Scholar 
Dibner, R. R., DeMarche, M. L., Louthan, A. M. & Doak, D. F. Multiple mechanisms confer stability to isolated populations of a rare endemic plant. Ecol. Monogr. 89, e01360 (2019).
Google Scholar 
Boyce, M. S., Haridas, C. V., Lee, C. T., NCEAS Stochastic Demography Working Group. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 

Google Scholar 
Buckley, Y. M. et al. Causes and consequences of variation in plant population growth rate: A synthesis of matrix population models in a phylogenetic context. Ecol. Lett. 13, 1182–1197 (2010).PubMed 

Google Scholar 
Abbott, R. E., Doak, D. F. & DeMarche, M. L. Portfolio effects, climate change, and the persistence of small populations: Analyses on the rare plant Saussurea weberi. Ecology 98, 1071–1081 (2017).PubMed 

Google Scholar 
Villellas, J., Doak, D. F., García, M. B. & Morris, W. F. Demographic compensation among populations: What is it, how does it arise and what are its implications?. Ecol. Lett. 18, 1139–1152 (2015).PubMed 

Google Scholar 
Doak, D. F. & Morris, W. F. Demographic compensation and tipping points in climate-induced range shifts. Nature 467, 959–962 (2010).ADS 
CAS 
PubMed 

Google Scholar 
García-Camacho, R., Albert, M. J. & Escudero, A. Small-scale demographic compensation in a high-mountain endemic: The low edge stands still. Plant Ecol. Divers. 5, 37–44 (2012).
Google Scholar 
Andrello, M. et al. Accounting for stochasticity in demographic compensation along the elevational range of an alpine plant. Ecol. Lett. 23, 870–880 (2020).PubMed 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 

Google Scholar 
Ægisdóttir, H. H., Kuss, P. & Stöcklin, J. Isolated populations of a rare alpine plant show high genetic diversity and considerable population differentiation. Ann. Bot. 104, 1313–1322 (2009).PubMed 
PubMed Central 

Google Scholar 
Morente-López, J. et al. Geography and environment shape landscape genetics of Mediterranean alpine species Silene ciliata Poiret. (Caryophyllaceae). Front. Plant Sci. 9, 1698 (2018).PubMed 
PubMed Central 

Google Scholar 
Franks, S. J., Weber, J. J. & Aitken, S. N. Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol. Appl. 7, 123–139 (2014).PubMed 

Google Scholar 
Jeong, H., Cho, Y.-C. & Kim, E. Differential plastic responses to temperature and nitrogen deposition in the subalpine plant species, Primula farinosa subsp. modesta. AoB Plants 13, plab061 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sulis, E., Bacchetta, G., Cogoni, D. & Fenu, G. From global to local scale: Where is the best for conservation purpose?. Biodivers. Conserv. 30, 183–200 (2021).
Google Scholar 
Hambler, D. & Dixon, J. Primula farinosa L. J. Ecol. 91, 694–705 (2003).
Google Scholar 
Arnold, E. & Richards, A. On the occurrence of unilateral incompatibility in Primula section Aleuritia Duby and the origin of Primula scotica Hook. Bot. J. Linn. Soc. 128, 359–368 (1998).
Google Scholar 
Tribsch, A. Areas of endemism of vascular plants in the eastern Alps in relation to Pleistocene glaciation. J. Biogeogr. 31, 747–760 (2004).
Google Scholar 
Chung, J.-M., Son, S.-W., Kim, S.-Y., Park, G.-W. & Kim, S.-S. Genetic diversity and geographic differentiation in the endangered Primula farinosa subsp. modesta, a subalpine endemic to Korea. Korean J. Plant. Taxon. 43, 236–243 (2013).
Google Scholar 
Lindborg, R. & Ehrlén, J. Evaluating the extinction risk of a perennial herb: Demographic data versus historical records. Conserv. Biol. 16, 683–690 (2002).
Google Scholar 
Caswell, H. Matrix Population Models, 2nd ed (Sinauer Associates Inc, 2000).Salguero-Gómez, R. & De Kroon, H. Matrix projection models meet variation in the real world. J. Ecol. 98, 250–254 (2010).
Google Scholar 
Jongejans, E. et al. Region versus site variation in the population dynamics of three short-lived perennials. J. Ecol. 98, 279–289 (2010).
Google Scholar 
Jongejans, E. & De Kroon, H. Space versus time variation in the population dynamics of three co-occurring perennial herbs. J. Ecol. 93, 681–692 (2005).
Google Scholar 
Suggitt, A. J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8 (2011).
Google Scholar 
Tomimatsu, H. & Ohara, M. Demographic response of plant populations to habitat fragmentation and temporal environmental variability. Oecologia 162, 903–911 (2010).ADS 
PubMed 

Google Scholar 
Kudernatsch, T., Fischer, A., Bernhardt-Römermann, M. & Abs, C. Short-term effects of temperature enhancement on growth and reproduction of alpine grassland species. Basic Appl. Ecol. 9, 263–274 (2008).
Google Scholar 
Kim, E. & Donohue, K. Local adaptation and plasticity of Erysimum capitatum to altitude: Its implications for responses to climate change. J. Ecol. 101, 796–805 (2013).
Google Scholar 
Forbis, T. A. Seedling demography in an alpine ecosystem. Am. J. Bot. 90, 1197–1206 (2003).PubMed 

Google Scholar 
Yenni, G., Adler, P. B. & Ernest, S. M. Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461 (2012).PubMed 

Google Scholar 
Doak, D. F. Source-sink models and the problem of habitat degradation: General models and applications to the Yellowstone grizzly. Conserv. Biol. 9, 1370–1379 (1995).
Google Scholar 
Lesica, P. & Crone, E. E. Arctic and boreal plant species decline at their southern range limits in the Rocky Mountains. Ecol. Lett. 20, 166–174 (2017).PubMed 

Google Scholar 
Oldfather, M. F. & Ackerly, D. D. Microclimate and demography interact to shape stable population dynamics across the range of an alpine plant. New Phytol. 222, 193–205 (2019).PubMed 

Google Scholar 
Ågren, J., Fortunel, C. & Ehrlén, J. Selection on floral display in insect-pollinated Primula farinosa: Effects of vegetation height and litter accumulation. Oecologia 150, 225–232 (2006).ADS 
PubMed 

Google Scholar 
Ehrlén, J., Syrjänen, K., Leimu, R., Begona Garcia, M. & Lehtilä, K. Land use and population growth of Primula veris: An experimental demographic approach. J. Appl. Ecol. 42, 317–326 (2005).
Google Scholar 
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).PubMed 
PubMed Central 

Google Scholar 
Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio package in R. J. Stat. Softw. 22, 1–23 (2007).
Google Scholar 
Weiss, N. Package ‘wPerm’. https://cran.r-project.org/web/packages/wPerm/wPerm.pdf. (2015).Frossard, J. & Renaud, O. Permutation tests for regression, ANOVA, and comparison of signals: The permuco package. J. Stat. Softw. 99, 1–32 (2021).
Google Scholar  More

Bascompte, J. & Jordano, P. Mutualistic Networks (Princeton Univ. Press, Princeton, NJ, 2013).Cordeiro, N. J. & Howe, H. F. Forest fragmentation severs mutualism between seed dispersers and an endemic African tree. Proc. Natl Acad. Sci. USA 100, 14052–14056 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wandrag, E. M., Dunham, A. E., Duncan, R. P. & Rogers, H. S. Seed dispersal increases local species richness and reduces spatial turnover of tropical tree seedlings. Proc. Natl Acad. Sci. USA 114, 10689–10694 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).PubMed 
PubMed Central 

Google Scholar 
Fricke, E. C. & Svenning, J. C. Accelerating homogenization of the global plant-frugivore meta-network. Nature 585, 74–78 (2020).CAS 
PubMed 

Google Scholar 
Fontúrbel, F. E. et al. Meta-analysis of anthropogenic habitat disturbance effects on animal-mediated seed dispersal. Glob. Change Biol. 21, 3951–3960 (2015).
Google Scholar 
Poisot, T. et al. Global knowledge gaps in species interaction networks data. J. Biogeogr. 48, 1552–1563 (2021).
Google Scholar 
Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).
Google Scholar 
Magrach, A., Laurance, W. F., Larrinaga, A. R. & Santamaria, L. Meta-analysis of the effects of forest fragmentation on interspecific interactions. Conserv. Biol. 28, 1342–1348 (2014).PubMed 

Google Scholar 
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).CAS 
PubMed 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 

Google Scholar 
de Assis Bomfim, J., Guimarães, P. R. Jr., Peres, C. A., Carvalho, G. & Cazetta, E. Local extinctions of obligate frugivores and patch size reduction disrupt the structure of seed dispersal networks. Ecography 41, 1899–1909 (2018).
Google Scholar 
Emer, C. et al. Seed dispersal networks in tropical forest fragments: Area effects, remnant species, and interaction diversity. Biotropica 52, 81–89 (2020).
Google Scholar 
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).PubMed 

Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).PubMed 

Google Scholar 
Neff, F. M. et al. Changes in plant-herbivore network structure and robustness along land-use intensity gradients in grasslands and forests. Sci. Adv. 7, eabf3985 (2021).PubMed 
PubMed Central 

Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Google Scholar 
James, A., Pitchford, J. W. & Plank, M. J. Disentangling nestedness from models of ecological complexity. Nature 487, 227–230 (2012).CAS 
PubMed 

Google Scholar 
Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).
Google Scholar 
Vieira, M. C. & Almeida-Neto, M. A simple stochastic model for complex coextinctions in mutualistic networks: robustness decreases with connectance. Ecol. Lett. 18, 144–152 (2015).PubMed 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. USA 104, 19891–19896 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gilarranz, L. J., Rayfield, B., Liñán-Cembrano, G., Bascompte, J. & Gonzalez, A. Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science 357, 199–201 (2017).CAS 
PubMed 

Google Scholar 
Liu, H. et al. Geographic variation in the robustness of pollination networks is mediated by modularity. Glob. Ecol. Biogeogr. 30, 1447–1460 (2021).
Google Scholar 
Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).CAS 
PubMed 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. B. 271, 2605–2611 (2004).PubMed 
PubMed Central 

Google Scholar 
Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. 9, 16–36 (2019).
Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).PubMed 

Google Scholar 
Song, C., Rohr, R. P. & Saavedra, S. Why are some plant-pollinator networks more nested than others? J. Anim. Ecol. 86, 1417–1424 (2017).PubMed 

Google Scholar 
Schleuning, M., Böhning-Gaese, K., Dehling, D. M. & Burns, K. C. At a loss for birds: insularity increases asymmetry in seed-dispersal networks. Glob. Ecol. Biogeogr. 23, 385–394 (2014).
Google Scholar 
Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).CAS 
PubMed 

Google Scholar 
Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).
Google Scholar 
Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).PubMed 

Google Scholar 
Traveset, A. et al. Bird-flower visitation networks in the Galápagos unveil a widespread interaction release. Nat. Commun. 6, 6376 (2015).CAS 
PubMed 

Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 

Google Scholar 
Monteiro, E. C. S., Pizo, M. A., Vancine, M. H. & Ribeiro, M. C. Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks. Oikos 2022, e08240 (2022).
Google Scholar 
Whittaker, R. J., Fernández-Palacios, J. M., Matthews, T. J., Borregaard, M. K. & Triantis, K. A. Island biogeography: taking the long view of nature’s laboratories. Science 357, eaam8326 (2017).PubMed 

Google Scholar 
Vizentin-Bugoni, J. et al. Structure, spatial dynamics, and stability of novel seed dispersal mutualistic networks in Hawai’i. Science 364, 78–82 (2019).CAS 
PubMed 

Google Scholar 
Diamond, J. Dammed experiments! Science 294, 1847–1848 (2001).CAS 
PubMed 

Google Scholar 
Jones, I. L., Bunnefeld, N., Jump, A. S., Peres, C. A. & Dent, D. H. Extinction debt on reservoir land-bridge islands. Biol. Conserv. 199, 75–83 (2016).
Google Scholar 
Wu, J., Huang, J., Han, X., Xie, Z. & Gao, X. Three-Gorges dam–experiment in habitat Fragmentation? Science 300, 1239–1240 (2003).CAS 
PubMed 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).
Google Scholar 
Trøjelsgaard, K. et al. Island biogeography of mutualistic interaction networks. J. Biogeogr. 40, 2020–2031 (2013).
Google Scholar 
Emer, C., Venticinque, E. M. & Fonseca, C. R. Effects of dam-induced landscape fragmentation on amazonian ant-plant mutualistic networks. Conserv. Biol. 27, 763–773 (2013).PubMed 

Google Scholar 
Zhu, C. et al. Arboreal camera trapping: a reliable tool to monitor plant-frugivore interactions in the trees on large scales. Remote Sens. Ecol. Conserv. 8, 92–104 (2022).
Google Scholar 
Zhu, C., Li, W., Wang, D., Ding, P. & Si, X. Plant-frugivore interactions revealed by arboreal camera trapping. Front. Ecol. Environ. 19, 149–151 (2021).
Google Scholar 
Galiana, N. et al. The spatial scaling of species interaction networks. Nat. Ecol. Evol. 2, 782–790 (2018).PubMed 

Google Scholar 
Hanski, I., Zurita, G. A., Bellocq, M. I. & Rybicki, J. Species-fragmented area relationship. Proc. Natl Acad. Sci. USA 110, 12715–12720 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sugiura, S. Species interactions-area relationships: biological invasions and network structure in relation to island area. Proc. R. Soc. B. 277, 1807–1815 (2010).PubMed 
PubMed Central 

Google Scholar 
Galiana, N. et al. Ecological network complexity scales with area. Nat. Ecol. Evol. 6, 307–314 (2022).PubMed 

Google Scholar 
Santos, M., Cagnolo, L., Roslin, T., Marrero, H. J. & Vázquez, D. P. Landscape connectivity explains interaction network patterns at multiple scales. Ecology 100, e02883 (2019).PubMed 

Google Scholar 
Si, X., Pimm, S. L., Russell, G. J. & Ding, P. Turnover of breeding bird communities on islands in an inundated lake. J. Biogeogr. 41, 2283–2292 (2014).
Google Scholar 
Si, X. et al. Functional and phylogenetic structure of island bird communities. J. Anim. Ecol. 86, 532–542 (2017).PubMed 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).
Google Scholar 
Sebastián-González, E. Drivers of species’ role in avian seed-dispersal mutualistic networks. J. Anim. Ecol. 86, 878–887 (2017).PubMed 

Google Scholar 
Donoso, I. et al. Downsizing of animal communities triggers stronger functional than structural decay in seed-dispersal networks. Nat. Commun. 11, 1582 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).PubMed 

Google Scholar 
Dalsgaard, B. et al. Opposed latitudinal patterns of network-derived and dietary specialization in avian plant-frugivore interaction systems. Ecography 40, 1395–1401 (2017).
Google Scholar 
Borrvall, C., Ebenman, B. & Jonsson, T. Biodiversity lessens the risk of cascading extinction in model food webs. Ecol. Lett. 3, 131–136 (2000).
Google Scholar 
Liao, J. et al. Robustness of metacommunities with omnivory to habitat destruction: disentangling patch fragmentation from patch loss. Ecology 98, 1631–1639 (2017).PubMed 

Google Scholar 
Rumeu, B. et al. Predicting the consequences of disperser extinction: richness matters the most when abundance is low. Funct. Ecol. 31, 1910–1920 (2017).
Google Scholar 
Wong, B. B. M. & Candolin, U. Behavioral responses to changing environments. Behav. Ecol. 26, 665–673 (2015).
Google Scholar 
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239 (2019).CAS 
PubMed 

Google Scholar 
Menke, S., Böhning-Gaese, K. & Schleuning, M. Plant-frugivore networks are less specialized and more robust at forest–farmland edges than in the interior of a tropical forest. Oikos 121, 1553–1566 (2012).
Google Scholar 
Redhead, J. W. et al. Potential landscape-scale pollinator networks across Great Britain: structure, stability and influence of agricultural land cover. Ecol. Lett. 21, 1821–1832 (2018).PubMed 

Google Scholar 
Si, X. et al. The importance of accounting for imperfect detection when estimating functional and phylogenetic community structure. Ecology 99, 2103–2112 (2018).PubMed 

Google Scholar 
Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).
Google Scholar 
Liu, J. et al. The distribution of plants and seed dispersers in response to habitat fragmentation in an artificial island archipelago. J. Biogeogr. 46, 1152–1162 (2019).
Google Scholar 
Olson, E. R. et al. Arboreal camera trapping for the Critically Endangered greater bamboo lemur Prolemur simus. Oryx 46, 593–597 (2012).
Google Scholar 
Li, H.-D. et al. The functional roles of species in metacommunities, as revealed by metanetwork analyses of bird-plant frugivory networks. Ecol. Lett. 23, 1252–1262 (2020).PubMed 

Google Scholar 
Snow, B. & Snow, D. Birds and berries: a study of an ecological interaction (T & AD Poyser, Calton, 1988).Si, X., Kays, R. & Ding, P. How long is enough to detect terrestrial animals? Estimating the minimum trapping effort on camera traps. PeerJ 2, e374 (2014).PubMed 
PubMed Central 

Google Scholar 
Vázquez, D. P. et al. Species abundance and asymmetric interaction strength in ecological networks. Oikos 116, 1120–1127 (2007).
Google Scholar 
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).PubMed 

Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open. Sci. 3, 140536 (2016).PubMed 
PubMed Central 

Google Scholar 
Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Modell. Softw. 26, 173–178 (2011).
Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).CAS 
PubMed 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Nic Lughadha, E. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).PubMed 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
Rogers, H. S., Donoso, I., Traveset, A. & Fricke, E. C. Cascading impacts of seed disperser loss on plant communities and ecosystems. Annu. Rev. Ecol. Evol. Syst. 52, 641–666 (2021).
Google Scholar 
Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).
Google Scholar 
Patefield, W. M. Algorithm AS 159: An efficient method of generating random R × C tables with given row and column totals. Appl. Stat. 30, 91–97 (1981).
Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
Google Scholar 
Kabacoff, R. R in Action: Data Analysis and Graphics with R (Manning Publications Co, 2015).R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021). More

Precht, W. F., Gintert, B. E., Robbart, M. L., Fura, R. & van Woesik, R. Unprecedented disease-related coral mortality in Southeastern Florida. Sci. Rep. 6, 31374 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
NOAA. Stony Coral Tissue Loss Disease Case Definition. NOAA, Silver Spring, MD 10 (2018).Aeby, G. S. et al. Pathogenesis of a tissue loss disease affecting multiple species of corals along the Florida Reef Tract. Front Mar. Sci. 6, 00678 (2019).
Google Scholar 
Landsberg, J. H. et al. Stony coral tissue loss disease in Florida is associated with disruption of host–zooxanthellae physiology. Front Mar. Sci. 7, 576013 (2020).
Google Scholar 
Neely, K. L., Macaulay, K. A., Hower, E. K. & Dobler, M. A. Effectiveness of topical antibiotics in treating corals affected by Stony Coral Tissue Loss Disease. PeerJ 8, 9289 (2020).
Google Scholar 
Shilling, E. N., Combs, I. R. & Voss, J. D. Assessing the effectiveness of two intervention methods for stony coral tissue loss disease on Montastraea cavernosa. Sci. Rep. 11, 8566 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker, B. K., Turner, N. R., Noren, H. K. G., Buckley, S. F. & Pitts, K. A. Optimizing stony coral tissue loss disease (SCTLD) intervention treatments on Montastraea cavernosa in an endemic zone. Front Mar. Sci. 8, 666224 (2021).
Google Scholar 
Work, T. M. et al. Viral-like particles are associated with endosymbiont pathology in Florida corals affected by stony coral tissue loss disease. Front Mar. Sci. 8, 750658 (2021).
Google Scholar 
Veglia, A. J. et al. Alphaflexivirus genomes in stony coral tissue loss disease-affected, disease-exposed, and disease-unexposed coral colonies in the U.S. Virgin Islands. Microbiol. Resource Announc. 11, e01199-e1221 (2022).CAS 

Google Scholar 
Rosales, S. M. et al. Bacterial metabolic potential and micro-eukaryotes enriched in stony coral tissue loss disease lesions. Front Mar. Sci. 8, 776859 (2022).
Google Scholar 
Rosales, S. M., Clark, A. S., Huebner, L. K., Ruzicka, R. R. & Muller, E. M. Rhodobacterales and Rhizobiales are associated with stony coral tissue loss disease and its suspected sources of transmission. Front. Microbiol. 11, 681 (2020).PubMed 
PubMed Central 

Google Scholar 
Studivan, M. S. et al. Reef sediments can act as a stony coral tissue loss disease vector. Front Mar. Sci. 8, 815698 (2022).
Google Scholar 
Meyer, J. L. et al. Microbial community shifts associated with the ongoing stony coral tissue loss disease outbreak on the Florida Reef Tract. Front. Microbiol. 10, 2244 (2019).PubMed 
PubMed Central 

Google Scholar 
Ushijima, B. et al. Disease diagnostics and potential coinfections by Vibrio coralliilyticus during an ongoing coral disease outbreak in Florida. Front. Microbiol. 11, 2682 (2020).
Google Scholar 
Meiling, S. S. et al. Variable species responses to experimental stony coral tissue loss disease (SCTLD) exposure. Front Mar. Sci. 8, 670829 (2021).
Google Scholar 
Becker, C. C., Brandt, M., Miller, C. A. & Apprill, A. Microbial bioindicators of stony coral tissue loss disease identified in corals and overlying waters using a rapid field-based sequencing approach. Environ. Microbiol. 24, 1166–1182 (2021).PubMed 

Google Scholar 
Dobbelaere, T., Muller, E. M., Gramer, L. J., Holstein, D. M. & Hanert, E. Coupled epidemio-hydrodynamic modeling to understand the spread of a deadly coral disease in Florida. Front Mar. Sci. 7, 591881 (2020).
Google Scholar 
Dobbelaere, T. et al. Connecting the dots: Transmission of stony coral tissue loss disease from the Marquesas to the Dry Tortugas. Front Mar. Sci. 9, 778938 (2022).
Google Scholar 
Muller, E. M., Sartor, C., Alcaraz, N. I. & van Woesik, R. Spatial epidemiology of the stony-coral-tissue-loss disease in Florida. Front Mar. Sci. 7, 00163 (2020).
Google Scholar 
Sharp, W. C., Shea, C. P., Maxwell, K. E., Muller, E. M. & Hunt, J. H. Evaluating the small-scale epidemiology of the stony-coral-tissue-loss-disease in the middle Florida Keys. PLoS ONE 15, e0241871 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, O. M., Dennison, C. E., O’Neil, K. L. & Baker, A. C. Susceptibility of Caribbean brain coral recruits to stony coral tissue loss disease (SCTLD). Front Mar. Sci. 9, 821165 (2022).
Google Scholar 
Noonan, K. R. & Childress, M. J. Association of butterflyfishes and stony coral tissue loss disease in the Florida Keys. Coral Reefs 39, 1581–1590 (2020).
Google Scholar 
Dahlgren, C., Pizarro, V., Sherman, K., Greene, W. & Oliver, J. Spatial and temporal patterns of stony coral tissue loss disease outbreaks in the Bahamas. Front Mar. Sci. 8, 682114 (2021).
Google Scholar 
Rosenau, N. A. et al. Considering commercial vessels as potential vectors of stony coral tissue loss disease. Front Mar. Sci. 8, 709764 (2021).
Google Scholar 
Roth, L., Kramer, P., Doyle, E. & O’Sullivan, C. Caribbean SCTLD Dashboard. Available www.agrra.org. Accessed 06 Mar 2021. (2020).Brandt, M. E. et al. The emergence and initial impact of stony coral tissue loss disease (SCTLD) in the United States Virgin Islands. Front Mar. Sci. 8, 715329 (2021).
Google Scholar 
Bailey, S. A. et al. Trends in the detection of aquatic non-indigenous species across global marine, estuarine and freshwater ecosystems: A 50-year perspective. Divers. Distrib. 26, 1780–1797 (2020).MathSciNet 

Google Scholar 
Hewitt, C. L., Gollasch, S. & Minchin, D. The vessel as a vector: Biofouling, ballast water and sediments. In Biological Invasions in Marine Ecosystems Vol. 204 (eds Rilov, G. & Crooks, J. A.) 117–131 (Springer, 2009).
Google Scholar 
Zabin, C. J. et al. Small boats provide connectivity for nonindigenous marine species between a highly invaded international port and nearby coastal harbors. Manag. Biol. Invas. 5, 97–112 (2014).
Google Scholar 
Ashton, G. V., Zabin, C. J., Davidson, I. C. & Ruiz, G. M. Recreational boats routinely transfer organisms and promote marine bioinvasions. Biol. Invas. 24, 1083–1096 (2022).
Google Scholar 
Drake, L. A., Doblin, M. A. & Dobbs, F. C. Potential microbial bioinvasions via ships’ ballast water, sediment, and biofilm. Mar. Pollut. Bull. 55, 333–341 (2007).CAS 
PubMed 

Google Scholar 
Pagenkopp Lohan, K. M., Fleischer, R. C., Carney, K. J., Holzer, K. K. & Ruiz, G. M. Amplicon-based pyrosequencing reveals high diversity of protistan parasites in ships’ ballast water: Implications for biogeography and infectious diseases. Microb. Ecol. 71, 530–542 (2015).PubMed 

Google Scholar 
Ruiz, G. M. et al. Global spread of microorganisms by ships. Nature 408, 49–50 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Hwang, J., Park, S. Y., Lee, S. & Lee, T. K. High diversity and potential translocation of DNA viruses in ballast water. Mar. Pollut. Bull. 137, 449–455 (2018).CAS 
PubMed 

Google Scholar 
Shikuma, N. J. & Hadfield, M. G. Marine biofilms on submerged surfaces are a reservoir for Escherichia coli and Vibrio cholerae. Biofouling 26, 39–46 (2009).
Google Scholar 
Aguirre-Macedo, M. L. et al. Ballast water as a vector of coral pathogens in the Gulf of Mexico: The case of the Cayo Arcas coral reef. Mar. Pollut. Bull. 56, 1570–1577 (2008).CAS 
PubMed 

Google Scholar 
Bruno, J. F. The coral disease triangle. Nat. Clim. Chang. 5, 302–303 (2015).ADS 

Google Scholar 
Lakshmi, E., Priya, M. & Achari, V. S. An overview on the treatment of ballast water in ships. Ocean Coast. Manag. 199, 105296 (2021).
Google Scholar 
Petersen, N. B., Madsen, T., Glaring, M. A., Dobbs, F. C. & Jørgensen, N. O. G. Ballast water treatment and bacteria: Analysis of bacterial activity and diversity after treatment of simulated ballast water by electrochlorination and UV exposure. Sci. Total Environ. 648, 408–421 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Romero-Martínez, L., Moreno-Andrés, J., Acevedo-Merino, A. & Nebot, E. Evaluation of ultraviolet disinfection of microalgae by growth modeling: Application to ballast water treatment. J. Appl. Phycol. 28, 2831–2842 (2016).
Google Scholar 
First, M. R. et al. Stratification of living organisms in ballast tanks: How do organism concentrations vary as ballast water is discharged?. Environ. Sci. Technol. 47, 4442–4448 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Drake, L. A. et al. Microbial ecology of ballast water during a transoceanic voyage and the effects of open-ocean exchange. Mar. Ecol. Prog. Ser. 233, 13–20 (2002).ADS 

Google Scholar 
Khandeparker, L., Kuchi, N., Desai, D. V. & Anil, A. C. Changes in the ballast water tank bacterial community during a trans-sea voyage: Elucidation through next generation DNA sequencing. J. Environ. Manag. 273, 111018 (2020).
Google Scholar 
Ruiz, G. M., Lorda, J., Arnwine, A. & Lion, K. Shipping patterns associated with the Panama Canal: Effects on biotic exchange? In Bridging Divides Vol. 83 (eds Gollasch, S. et al.) 113–126 (Springer, 2006).
Google Scholar 
Pagano, A., Wang, G., Sánchez, O., Ungo, R. & Tapiero, E. The impact of the Panama Canal expansion on Panama’s maritime cluster. Marit. Policy Manag. 43, 164–178 (2016).
Google Scholar 
Muirhead, J. R., Minton, M. S., Miller, W. A. & Ruiz, G. M. Projected effects of the Panama Canal expansion on shipping traffic and biological invasions. Divers. Distrib. 21, 75–87 (2015).
Google Scholar 
Ros, M. et al. The Panama Canal and the transoceanic dispersal of marine invertebrates: Evaluation of the introduced amphipod Paracaprella pusilla Mayer, 1890 in the Pacific Ocean. Mar. Environ. Res. 99, 204–211 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Stehouwer, P. P., Buma, A. & Peperzak, L. A comparison of six different ballast water treatment systems based on UV radiation, electrochlorination and chlorine dioxide. Environ. Technol. 36, 2094–2104 (2015).CAS 
PubMed 

Google Scholar 
Wu, Y., Li, Z., Du, W. & Gao, K. Physiological response of marine centric diatoms to ultraviolet radiation, with special reference to cell size. J. Photochem. Photobiol., B 153, 1–6 (2015).CAS 

Google Scholar 
Aguirre, L. E. et al. Diatom frustules protect DNA from ultraviolet light. Sci. Rep. 8, 5138 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
First, M. R. & Drake, L. A. Life after treatment: Detecting living microorganisms following exposure to UV light and chlorine dioxide. J. Appl. Phycol. 26, 227–235 (2014).CAS 

Google Scholar 
Liebich, V., Stehouwer, P. P. & Veldhuis, M. Re-growth of potential invasive phytoplankton following UV-based ballast water treatment. Aquat. Invas. 7, 29–36 (2012).
Google Scholar 
Hess-Erga, O. K., Moreno-Andrés, J., Enger, Ø. & Vadstein, O. Microorganisms in ballast water: Disinfection, community dynamics, and implications for management. Sci. Total Environ. 657, 704–716 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Endresen, Ø., Lee Behrens, H., Brynestad, S., Bjørn Andersen, A. & Skjong, R. Challenges in global ballast water management. Mar. Pollut. Bull. 48, 615–623 (2004).CAS 
PubMed 

Google Scholar 
Vorkapić, A., Radonja, R. & Zec, D. Cost efficiency of ballast water treatment systems based on ultraviolet irradiation and electrochlorination. Promet Traffic Transp. 30, 343–348 (2018).
Google Scholar 
King, D., Hagan, P., Riggio, M. & Wright, D. Preview of global ballast water treatment markets. J. Mar. Eng. Technol. 11, 3–15 (2012).
Google Scholar 
Wang, Z., Saebi, M., Corbett, J. J., Grey, E. K. & Curasi, S. R. Integrated biological risk and cost model analysis supports a geopolitical shift in ballast water management. Environ. Sci. Technol. 55, 12791–12800 (2021).CAS 
PubMed 

Google Scholar 
Moreno-Andrés, J. & Peperzak, L. Operational and environmental factors affecting disinfection byproducts formation in ballast water treatment systems. Chemosphere 232, 496–505 (2019).ADS 
PubMed 

Google Scholar 
David, M., Linders, J., Gollasch, S. & David, J. Is the aquatic environment sufficiently protected from chemicals discharged with treated ballast water from vessels worldwide? A decadal environmental perspective and risk assessment. Chemosphere 207, 590–600 (2018).ADS 
CAS 
PubMed 

Google Scholar 
U.S. Environmental Protection Agency. Generic protocol for the verification of ballast water treatment technology, version 5.1. Report number EPA/600/R-10/146. Washington, D.C. 157 (2010).Evans, J. S., Paul, V. J., Ushijima, B. & Kellogg, C. A. Combining tangential flow filtration and size fractionation of mesocosm water as a method for the investigation of waterborne coral diseases. Biol. Methods Protocols 7, bpac007 (2022).
Google Scholar 
Fujimoto, M. et al. Application of Ion Torrent sequencing to the assessment of the effect of alkali ballast water treatment on microbial community diversity. PLoS ONE 9, e107534 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
United States Coast Guard. Ballast Water Best Management Practices to Reduce the Likelihood of Transporting Pathogens That May Spread Stony Coral Tissue Loss Disease. Marine Safety Information Bulletin 07–19. Washington, D.C. 2 (2019).Bolton, J. R. & Linden, K. G. Standardization of methods for fluence (UV dose) determination in bench-scale UV experiments. J. Environ. Eng. 129, 209–215 (2003).CAS 

Google Scholar 
Enochs, I. C. et al. The influence of diel carbonate chemistry fluctuations on the calcification rate of Acropora cervicornis under present day and future acidification conditions. J. Exp. Mar. Biol. Ecol. 506, 135–143 (2018).CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Preprint at https://www.r-project.org/ (2019).Therneau, T. M. survival: A package for survival analysis in R. R package version 3.2–13. (2021).Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing survival curves using “ggplot2”. R package version 0.4.9. (2021).Bakalar, G. Review of interdisciplinary devices for detecting the quality of ship ballast water. Springerplus 3, 468 (2014).PubMed 
PubMed Central 

Google Scholar 
Water Environmental Federation & American Public Health Association. Standard methods for the examination of water and wastewater. Washington, D.C. 21 (2005).Steinberg, M. K., Lemieux, E. J. & Drake, L. A. Determining the viability of marine protists using a combination of vital, fluorescent stains. Mar. Biol. 158, 1431–1437 (2011).
Google Scholar 
Oksanen, J. et al. vegan: Community ecology package. R package version 2.0–10. (2015).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4. (2020).Studivan, MS. Mstudiva/SCTLD-ballast-transmission: Stony coral tissue loss disease ballast transmission and treatment (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.6561517 (2022). More

Wright, S. Evolution in Mendelian Populations. Genetics 16, 97–159 (1931).CAS 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).
Google Scholar 
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).CAS 
PubMed 

Google Scholar 
Nei, M., Maruyama, T. & Chakraborty, R. The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10 (1975).PubMed 

Google Scholar 
Frankham, R. Stress and adaptation in conservation genetics. J. Evol. Biol. 18, 750–755 (2005).CAS 
PubMed 

Google Scholar 
Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).PubMed 

Google Scholar 
Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).CAS 
PubMed 

Google Scholar 
Hughes, A., Inouye, B., Johnson, M., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).PubMed 

Google Scholar 
Hughes, A. R., Stachowicz, J. J. & Williams, S. L. Morphological and physiological variation among seagrass (Zostera marina) genotypes. Oecologia 159, 725–733 (2009).PubMed 

Google Scholar 
Schweitzer, J. A. et al. Genetically based trait in a dominant tree affects ecosystem processes: Plant genetics impact ecosystems. Ecol. Lett. 7, 127–134 (2004).
Google Scholar 
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl Acad. Sci. USA 101, 8998–9002 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wimp, G. M. et al. Conserving plant genetic diversity for dependent animal communities. Ecol. Lett. 7, 776–780 (2004).
Google Scholar 
Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc. Natl Acad. Sci. 102, 2826–2831 (2005).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Salo, T. & Gustafsson, C. The Effect of Genetic Diversity on Ecosystem Functioning in Vegetated Coastal Ecosystems. Ecosystems 19, 1429–1444 (2016).
Google Scholar 
Zettlemoyer, M. A. & Peterson, M. L. Does Phenological Plasticity Help or Hinder Range Shifts Under Climate Change? Front. Ecol. Evol. 9, 392 (2021).
Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 

Google Scholar 
Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021).
Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic Consequences of Range Expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Alsos, I. G. et al. Genetic consequences of climate change for northern plants. Proc. R. Soc. B Biol. Sci. 279, 2042–2051 (2012).
Google Scholar 
Stahl, U., Reu, B. & Wirth, C. Predicting species’ range limits from functional traits for the tree flora of North America. Proc. Natl Acad. Sci. 111, 13739–13744 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).PubMed 
PubMed Central 

Google Scholar 
Peterson, M. L., Angert, A. L. & Kay, K. M. Experimental migration upward in elevation is associated with strong selection on life history traits. Ecol. Evol. 10, 612–625 (2019).PubMed 
PubMed Central 

Google Scholar 
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. 115, 1004–1008 (2018).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Chen, I.-C., Hill, J., Ohlemüller, R., Roy, D. B. & Thomas, C. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333, 1024–6 (2011).CAS 
PubMed 

Google Scholar 
Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946 (2013).PubMed 

Google Scholar 
De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021).PubMed 
PubMed Central 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).PubMed 

Google Scholar 
DeMarche, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change Biol. 25, 775–793 (2019).
Google Scholar 
Bothwell, H. M. et al. Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts. Ecol. Appl. 31, e02254 (2021).Syfert, M. M., Brummitt, N. A., Coomes, D. A., Bystriakova, N. & Smith, M. J. Inferring diversity patterns along an elevation gradient from stacked SDMs: A case study on Mesoamerican ferns. Glob. Ecol. Conserv. 16, e00433 (2018).
Google Scholar 
Mateo, R. G., Felicísimo, Á. M., Pottier, J., Guisan, A. & Muñoz, J. Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns? PLOS ONE 7, e32586 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).
Google Scholar 
Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines (Princeton University Press, 1977).May, R. M. & Godfrey, J. Biological Diversity: Differences between Land and Sea [and Discussion]. Philos. Trans. Biol. Sci. 343, 105–111 (1994).
Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

Google Scholar 
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).
Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).
Google Scholar 
Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–321 (2009).CAS 
PubMed 

Google Scholar 
Huntington, T. G. CO2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311–314 (2008).
Google Scholar 
Kim, J. H. et al. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. J. Geophys. Res. Biogeosciences 123, 1960–1975 (2018).
Google Scholar 
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).
Google Scholar 
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).PubMed 

Google Scholar 
Naiman, R. J., Décamps, H. & McClain, M. E. Riparia: ecology, conservation, and management of streamside communities (Elsevier Academic Press, 2005).Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant-Environ. Interact. 1, 166–180 (2020).
Google Scholar 
Cooke, J. E. K. & Rood, S. B. Trees of the people: the growing science of poplars in Canada and worldwide. This commentary is one of a selection of papers published in the Special Issue on Poplar Research in Canada. Can. J. Bot. 85, 1103–1110 (2007).
Google Scholar 
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host- associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).
Google Scholar 
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hargreaves, A. L., Samis, K. E., Eckert, C. G., Schmitz, A. E. O. J. & Bronstein, E. J. L. Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range. Am. Nat. 183, 157–173 (2014).PubMed 

Google Scholar 
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).
Google Scholar 
Cushman, S. A. et al. Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks. Ecol. Appl. 24, 1000–1014 (2014).PubMed 

Google Scholar 
Bothwell, H. M. et al. Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia). Mol. Ecol. 26, 5114–5132 (2017).PubMed 

Google Scholar 
Jimenez-Valverde, A. Sample Size for the evaluation of presence-absence models. Ecol. Indic. 114, 106289 (2020).
Google Scholar 
Hamann, A., Wang, T., Spittlehouse, D. L. & Murdock, T. Q. A Comprehensive, High-Resolution Database of Historical and Projected Climate Surfaces for Western North America. Bull. Am. Meteorol. Soc. 94, 1307–1309 (2013).
Google Scholar 
Lucinda. M. et al. NHDPlus version 2: user guide (Horizon Systems Corporation, 2012).ESRI. ArcMap (ESRI, 2018).Bayliss, S. L. J., Papeş, M., Schweitzer, J. A. & Bailey, J. K. Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree. PLoS One. 17, e0274892 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, 2009).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015). (1).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Google Scholar 
Swets, J. A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293 (1988).CAS 
PubMed 

Google Scholar 
Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).
Google Scholar 
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).
Google Scholar 
Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).
Google Scholar 
Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity Models: What If Unsaturation Is the Rule? Trends Ecol. Evol. 32, 556–566 (2017).PubMed 
PubMed Central 

Google Scholar 
R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Oksanen, J. et al. vegan: community ecology package (2020) http://CRAN.R-project.org/package=vegan. More