Ecology

The custom code used to clean occurrence records and construct SDMs is available at (github.com/RhettRautsaw/ VenomMaps). We used the following R16 packages for data cleaning, manipulation, species distribution modeling, and Shiny app creation: tidyverse17 readxl18, data.table19, sf20, sp21,22, rgdal23, raster24, smoothr25, ape26, phytools27, argparse28, parallel16, memuse29, dismo30, rJava31, concaveman32, spThin33, usdm34, ENMeval35, kuenm36, shiny37, leaflet38, leaflet.extras39, leaflet.extras240, RColorBrewer41, ggpubr42, ggtext43, and patchwork44.Updating occurrence record taxonomyOur goal was to update and reconstruct the distributions of New World pitvipers. We used the Reptile Database45 (May 2021) as our primary source for current taxonomy which included the following genera: Agkistrodon, Atropoides, Bothriechis, Bothrocophias, Bothrops, Cerrophidion, Crotalus, Lachesis, Metlapilcoatlus, Mixcoatlus, Ophryacus, Porthidium, and Sistrurus. However, to ensure we captured all New World pitvipers records, we incorporated all members of the family Viperidae (all vipers and pitvipers) into our pipeline for updating occurrence record taxonomy (i.e., to account for errors in the recorded latitude, longitude, or if subfamily was not recorded).First, we collected global occurrence records for “Viperidae” from GBIF (downloaded 2021-08-19)46, Bison (downloaded 2021-08-19)47, HerpMapper (only New World taxa; downloaded 2021-08-19)48, Brazilian Snake Atlas49, BioWeb (downloaded 2021-07-07)50, unpublished data/databases from RMR, GJV, EPH, LRVA, MM, and CLP, and georeferenced literature records totaling 373,673 species-level records, 292,425 of which are New World pitvipers. Given the fluidity of taxonomy, records were often associated with outdated names. For example, Crotalus mitchelli pyrrhus was elevated to Crotalus pyrrhus51, but may still be recorded as the former in a given repository (e.g., GBIF). To correct taxonomy in our database, we checked records against a list of synonyms found on the Reptile Database and compared them to current taxonomy. If species and subspecies columns matched the same taxon (or no subspecies was recorded), then species IDs were not altered. If species and subspecies IDs did not match the same taxon, we updated taxonomy by minimizing the number of changes required to a given character string. We then manually checked all changes.Constructing distribution mapsNext, we collected preliminary distribution maps from the International Union for Conservation of Nature (IUCN; downloaded 2018-11-27)52, Global Assessment of Reptile Distributions (GARD) v1.153, Heimes54, Campbell and Lamar55, and unpublished maps. We manually curated distribution maps for all New World pitvipers in QGIS using the occurrence records, previous distribution maps, and recent publications for each taxon (note that distributions for Old World Viperidae have not yet been updated). We used a digital relief map (maps-for-free.com) and The Nature Conservancy Terrestrial Ecoregions (TNG.org)56 to identify clear distribution boundaries (e.g., mountains). We then clipped the final distributions to a land boundary (GADM v3.6)57 and smoothed the distribution using the the “chaikin” method in the R package smoothr25.Occurrence-distribution overlapOur initial taxonomy check was only concerned with records for which a subspecies was recorded and had since been elevated to species status. Therefore, many records with no assigned subspecies likely remained associated with an incorrect or outdated generic and/or specific identification. Fortunately, taxonomic changes are typically associated with changes in the species’ expected distribution. For example, when Crotalus simus was resurrected from C. durissus, the distribution of C. durissus was split: the northern portion of its range in Central America now represented the resurrected species (C. simus) and the southern portion of its range remained C. durissus55. Yet, occurrence records in Central America often remain labelled as C. durissus in data repositories. Therefore, we spatially joined records with the newly reconstructed species distribution maps to determine if they overlapped with their expected distribution (Old World taxa were joined with the GARD 1.1 distributions53).Briefly, we developed a custom function (occ_cleaner.R) to perform the spatial join and update taxonomy. First, we calculated the distance for each record to the 20 nearest distributions within 50 km (full overlap resulted in a distance of 0 m). Next, we calculated the phylogenetic distance between the recorded species ID and each species with which that record overlapped using the tree from Zaher et al.58 and adding taxa based on recent clade-specific publications (bind.tip2.R; see github.com/RhettRautsaw/VenomMaps for full list of references and details). If records overlapped with their expected species, no changes were made. If records fell outside of their expected distribution, we filtered the potential overlapping and nearby species (within 50 km) to minimize phylogenetic distance. If multiple species were equally distant (i.e., share the same common ancestor), we attempted to minimize geographic distance. If multiple species remained equally distant in both phylogenetic and geographic distance, we flagged the record to be manually checked. We also flagged records if a species’ taxonomy had changed and records were additionally flagged as potentially dubious if the taxonomic change had a phylogenetic divergence greater than 5 million years. We manually checked all flagged records and returned records to their original species ID if species identity remained uncertain. We flagged these records as potentially dubious, along with records that fell outside of their expected distribution (within 50 km), and removed all flagged records for species distribution modeling. Our final cleaned database contained 344,998 global records, of which 275,087 were New World pitvipers.Species distribution modelingWe attempted to infer SDMs for the 158 species of New World pitvipers currently recognized by the Reptile Database (May 2021) and additionally modeled the three subspecies of Crotalus ravus separately based on recommendations for species status elevation by Blair et al.59 for a total of 160 species. We developed a unix-executable R script (autokuenm.R) designed to take occurrence records, distribution maps, and environmental data and prepare these data for species distribution modeling with kuenm36. We chose to use kuenm – and MaxEnt v3.4.460 – because it has been shown to have good predictive power61 and fine-tuning of this algorithm has performance comparable to more computationally intensive ensembles62,63. Additionally, MaxEnt allows for flexibility in parameter selection64 and can function entirely with presence data14.Prior to autokuenm, to account for sampling/spatial bias during SDM, we created a bias file by using the pooled New World pitviper occurrence records as representative background data65,66,67,68. Specifically, we converted occurrence records to a raster and performed two-dimensional kernel density estimation (kde2d) with the MASS package with default settings69 and rescaled the kernel density by a factor of 1000 and rounded to three decimal places. This was then used as input to factor out sampling bias by MaxEnt. We then ran autokuenm, which is designed to subset/partition the cleaned occurrence records for a given species and prepare additional files for SDM. We first defined M-areas – or areas accessible to a given species – using the World Wildlife Fund Terrestrial Ecoregions70. Biogeographic regions represent distributional limits for many species and are reasonable hypotheses for the areas accessible to a given species71,72. To do this, we created alpha hulls from the subset of occurrence records for a given species using concaveman32 with default settings. We then identified regions with at least 20% of the region covered by the alpha hull and merged these regions together to form our final M-area. All environmental layers and the bias file were cropped to this M-area which was used as the geographic extent for modeling. We then randomly selected 5% of records to function as an independent test set for final model evaluation. Next, we generated 2000 random background points across the cropped environmental layers and used ENMeval to partition occurrence records into four sets using the checkerboard2 pattern35. Note that the background points here were not used in MaxEnt. One of the four partitions was selected at random to be used as the testing set; the remaining three partitions were used for training the MaxEnt models. If the number of occurrence records in the independent test set was less than five, then we used the training partition for final model creation and used the testing partition for final model evaluation.We tested the top-contributing variables from three sets of environmental layers: (1) bioclimatic variables, (2) EarthEnv topographic variables73, and (3) a combination of these variables. To select the top-contributing variables in each set, we wrote a custom function (SelectVariables) which used a combination of MaxEnt permutation importance and Variable Inflation Factors (VIF) to remove collinearity while keeping the variables that contributed the most to the model. Compared with variable selection via principal component analysis loadings, the permutation importance and VIF methodology demonstrated significant improvement in MaxEnt model fit. First, we designed SelectVariables to run MaxEnt using dismo::maxent with default settings and then extracted the permutation importance. We removed variables if they had 0% permutation importance. Next, we calculated VIF with usdm::vif and then iteratively removed variables by selecting the variables with two highest VIF values and removing whichever variable had the lowest permutation importance. We then recalculated VIF and repeated the process until the maximum VIF value was less than 10. Finally, we recalculated permutation importance with the remaining variables using dismo::maxent with default settings and removed variables with less than 1% permutation importance to create the final variable sets. This process was done for each species independently.With the final environmental variable, testing, and training sets, we generated SDMs using kuenm. First, we created candidate calibration models with multiple combinations of regularization multipliers (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 8, 10), feature classes (l, q, h, lq, lp, lt, lh, qp, qt, qh, pt, ph, th, lqp, lqt, lqh, lpt, lph, lth, qpt, qph, qth, pth, lqpt, lqph, lqth, lpth, qpth, lqpth), and sets of environmental predictors (bioclimatic, topographic, combination) totaling 2,958 candidate models per species. We then ran each model in parallel using GNU Parallel74. Next, we evaluated the candidate models and selected the best models using statistical significance (partial ROC), prediction ability (omission rates; OR), and model complexity (AICc) with the “kuenm_ceval” function with default settings. Specifically, models were only considered if they were statistically significant and had an OR less than 5%. If no models passed the OR criteria, the models with the minimal OR were considered. Finally, any remaining models were filtered to those within 2 AICc of the top model (Supplementary Table 1). In addition to evaluating and comparing all models together, we evaluated bioclimatic-only and combination-only models separately since these two sets of environmental variables were expected to be the best performing models given the ubiquity of bioclimatic variables in species distribution modeling (Supplementary Table 1).We generated 10 bootstrap replicates for each of the “best” calibration models using the “kuenm_mod” function. We also performed jackknifing to assess variable importance and models were output in raw format. We evaluated the final models using “kuenm_feval” with default settings. To select the best model for each comparative set (i.e., all, bioclimatic-only, and combination-only sets), we filtered the final evaluation results to minimize the OR and maximize the AUC ratio (Supplementary Table 2). If multiple models remained and were considered equally competitive, we averaged these models together (Supplementary Table 3). Because we performed three different set of comparisons, there were three “best” models per species, so we again aimed to minimize the OR and maximize the AUC ratio to select a final model for each species (Supplementary Table 4). We then converted our final models into cloglog format for visualization and threshold the models using a 10th percentile training presence cutoff (Fig. S2). Both conversion and thresholding functions are provided as R functions (raw2log, raw2clog, raster_threshold in functions.R; github.com/RhettRautsaw/VenomMaps). More

Literature reviewWe conducted a comprehensive review of relevant literature published since 1995. Studies were extracted from the China National Knowledge Infrastructure and Web of Science using the following keywords: “N (nitrogen) loss OR NO (nitric oxide) emission OR N2O (nitrous oxide) emission OR NH3 (ammonia volatilization) emission OR NO3− (nitric leaching) OR N (nitrogen) runoff AND wheat AND China”. We excluded the following types of experiment: experiments not covering the entire wheat growing season, experiments conducted in greenhouses or laboratories, experiments without zero-N control, and experiments including manure, controlled release fertilizer, or inhibitors. In total, we extracted 941 observations from 138 articles, consisting of 121 observations of NO emission, 383 of N2O emission, 185 of NH3 emission, 188 of NO3− leaching, and 64 of Nr runoff. We also extracted data on N application rates, and climate and soil variables (Fig. 1). Missing climate data were obtained from China Meteorological Data Network (https://data.cma.cn/), miss values of soil organic carbon (SOC) and total N content were obtained from the National Scientific Fertilizer Network (http://kxsf.soilbd.com/), and missing soil silt, clay, sand content, bulk density, cation exchange capacity (CEC), and pH data were obtained from the Harmonized World Soil Database (HWSD) v. 1.2 (http://www.fao.org/soils-portal/soil-survey/soilmaps-and-databases/harmonized-world-soildatabase-v12/en). Based on this dataset, the EFs of Nr loss pathways were calculated by the following equation:$$E{F}_{i}=left({E}_{treatment}{rm{-}}{E}_{control}right){rm{/}}N;applied$$
(1)
where i = 1–5, represented NO, N2O, NH3, NO3− leaching and Nr runoff, respectively. Etreatment is the loss rate of experimental treatments with applied N fertilizer, Econtrol is the loss rate of experimental control without applied N fertilizer, and N applied is the N application rate corresponding to Etreatment. The resulting data was used to develop RF models to predict EFs of the five Nr loss pathways.Fig. 1The generate framework of the Nr loss from Chinese wheat system (Nr-Wheat) 1.0 database.Full size imageRF modelsRF models outperformed empirical models in previous studies15,18,19. We employed RF models to predict the EFs of NO, N2O, NH3, NO3− leaching, and Nr runoff. Environmental factors were selected via redundancy analysis20. Redundancy analysis, a basic ordination technique for gradients analysis, produces an ordination summarizing the variation in several response variables that can be best explained by a matrix of explanatory variables based on multiple linear regression. We conducted redundancy analysis using Canoco 5 to further analyze the effects of 10 environmental factors, including 4 soil physical factors (bulk density, silt, clay, and sand content), 4 soil chemical factors (pH, SOC, CEC and total N content), and 2 weather factors (total rainfall and mean temperature during the wheat growing period) of different EFs. Ultimately, the dataset of each pathway contained an ensemble of different environmental factors (Table 1).Table 1 Environmental factors were employed to build RF model for each pathway and total explanatory rates.Full size tableWhen establishing the RF model, the first step was to select k features from a total of m (k  More

Ambitious new targets are needed to conserve nature by protecting parks and species.Credit: Tang Dehong/VCG/Getty

It will take ample time and money to slow the world’s catastrophic loss of plant and animal species — and right now, both are running dangerously low. This year, nations are due to agree to an action plan to protect global biodiversity at the 15th Conference of the Parties (COP15) to the United Nations Convention on Biological Diversity. But the meeting is already two years late because of the pandemic, and China, which will host the conference in Kunming, has yet to set a new date.Now, conflicts over financing are adding to the tension. Conservation groups and advocates suggest that rich nations must donate at least US$60 billion annually to help less-affluent ones to fund projects such as protecting areas where wildlife can thrive and tackling the illegal wildlife trade that is driving hundreds of species to extinction. This is much more than the $4 billion to $10 billion that they are estimated to be spending today, and well below the amount they are giving low- and middle-income countries (LMICs) to fight climate change, which reached around $50 billion in 2019 according to one estimate. Yet limited overseas development funds are spread ever thinner as donors deal with the pandemic and now the fallout from Russia’s invasion of Ukraine. This is where COP15 is meant to deliver: as well as agreeing to the action plan, called the Global Biodiversity Framework, nations will be encouraged to pledge more money.A mix of public and private money has started to trickle in. Currently, biodiversity funding on the table ahead of COP15 amounts to roughly $5.2 billion per year, according to estimates by a group of five leading conservation organizations. Most comes from six governments, including France, the United Kingdom and Japan, and the European Union. In April, the Global Environment Facility (GEF) — a multilateral fund to support international environmental agreements — announced that, over the next four years, around $1.9 billion will go to projects dedicated to biodiversity. However, it’s unclear how much of this will come from the coffers that donor countries have already pledged.Some cash for conservation is coming from private philanthropic donors — such as $2 billion committed by entrepreneur Jeff Bezos last year. And starting in 2020, a group of financial institutions (now 89 of them) promised to annually report their financing activities and investments that affect biodiversity, and to move away from those that do harm — a form of ecological accounting that could help to shrink the budget needed to protect biodiversity. Donors will need to reach much deeper into their pockets to meet the demands of LMICs, the custodians of much of the world’s biodiversity. In March, a group of LMICs, led by Gabon, asked for $100 billion per year in new funding when officials met in Geneva, Switzerland, to discuss progress on the Global Biodiversity Framework. The LMICs want the money placed in a new multilateral fund for biodiversity, separate from, but complementary to, the GEF.Aside from cash, the fund will need to find a new home and structure — and there are a few options. A proposal from Brazil, circulated at the Geneva meeting, suggests the fund be governed by a board of 24 members, with an equal number from rich and lower-income nations. The board would be responsible for funding decisions and would prioritize projects that help to achieve the biodiversity convention’s goals. The pitch generated interest among some countries, but also concerns that it’s an attempt by Brazil to divert attention from its failure over the past few years to protect the Amazon rainforest and prevent other environmental harm.Another option is the Kunming Biodiversity Fund, which China announced in October last year to help LMICs to safeguard their ecosystems. It allocated 1.5 billion yuan (US$223 million) to seed the fund and invited other countries to contribute, but so far none has. Sources knowledgeable about the fund say that donor countries are reluctant to pitch in because China is holding on too tightly to the reins and is not involving others in its deliberations. Details of how the fund will operate are scarce, but Nature has learnt that China is floating the idea of housing it at the Asian Infrastructure Investment Bank (AIIB), based in Beijing. Set up in 2016, the AIIB has $100 billion in total capital and 105 members, including Germany, France and the United Kingdom. The AIIB has big green plans. By 2025, it wants half of all infrastructure projects it finances to focus on climate issues. With rigorous oversight and transparency, the AIIB would make a good home for the Kunming fund.As countries prepare to meet in Nairobi on 20–26 June in a last-ditch attempt to push the biodiversity framework forwards before COP15, China, as the host, must urgently provide stronger leadership on financing, including more transparency and engagement. Progress will require quick, generous contributions from donor nations — which should prioritize grants, not loans, for biodiversity projects.Holding the COP15 meeting must be a priority, too. As China tightens restrictions in the face of a COVID-19 surge, some researchers fear that delays will stretch on, stalling conservation work and leaving less time to meet biodiversity targets. China must either commit to holding the meeting this year or let it proceed elsewhere. One option being quietly discussed is moving the meeting to Canada — home of the United Nations biodiversity convention’s secretariat — and this deserves consideration. The world needs an ambitious biodiversity plan now — nature cannot wait. More

Khamraev, Sh. R. & Bezborodov, Yu. G. Results of research on the reduction of physical evaporation of moisture from the cotton fields. Sci. World 2(33), 86–93 (2016).
Google Scholar 
Khan, A. U. et al. Production of organic fertilizers from rocket seed (Eruca sativa L.), chicken peat and Moringa oleifera leaves for growing linseed under water deficit stress. Sustainability 13(1), 1–19 (2021).CAS 

Google Scholar 
Patil Shirish, S., Kelkar Tushar, S. & Bhalerao Satish, A. Mulching: A soil and water conservation practice. Res. J. Agric For. Sci. 1(3), 26–29 (2013).
Google Scholar 
Matkovic, A. et al. Mulching as a physical weed control method applicable in medicinal plants cultivations. J. Lekovite Sirovine 35, 37–51 (2015).Article 

Google Scholar 
Nawaz, A., Lal, R., Shrestha, R. K. & Farooq, M. Mulching affects soil properties and greenhouse gas emissions under long-term no-till and plough-till systems in alfisol of Central Ohio. Land Degrad. Dev. 28(2), 673–681 (2016).Article 

Google Scholar 
Brant, V. et al. Splash erosion in maize crops under conservation management in combination with Shallow Strip-tillage before Sowing. Soil Water Res. 12(2), 106–116 (2017).CAS 
Article 

Google Scholar 
Kumar, R. et al. Effect of plant spacing and organic mulch on growth, yield and quality of natural sweetener plant Stevia and soil fertility in western Himalayas. Int. J. Plant Prod. 8(3), 311–334 (2014).ADS 

Google Scholar 
Seleiman, M. F. & Kheir, A. M. S. Maize productivity, heavy metals uptake and their availability in contaminated clay and sandy alkaline soils as affected by inorganic and organic amendments. Chemosphere 204, 514–522 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Seleiman, M. F. & Kheir, A. M. S. Saline soil properties, quality and productivity of wheat grown with bagasse ash and thiourea in different climatic zones. Chemosphere 193, 538–546 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chakraborty, D. et al. Effect of mulching on soil and plant water status, and the growth and yield of wheat (Triticum aestivum L.) in a semi-arid environment. Agric. Water Manag. 95(12), 1323–1334 (2008).Article 

Google Scholar 
Ahmad, Z. I., Ansar, M., Iqbal, M. & Minhas, N. M. Effect of planting geometry and mulching on moisture conservation, weed control and wheat growth under rainfed conditions. Pak. J. Bot. 39(4), 1189–1195 (2007).
Google Scholar 
Teame, G. Effect of organic mulches and land preparation methods on soil moisture and sesame productivity. Afr. J. Agric. Res. 12(38), 2836–2843 (2017).Article 

Google Scholar 
Lehar, L., Wardiyati, T., Moch Dawam, M. & Suryanto, A. Influence of mulch and plant spacing on yield of Solanum tuberosum L. cv. Nadiya at medium altitude. Int. Food Res. J. 24(3), 1338–1344 (2017).CAS 

Google Scholar 
Arash, K. The evaluation of water use efficiency in common bean (Phaseolus vulgaris L.) in irrigation condition and mulch. Sci. Agric. 2(3), 60–64 (2013).
Google Scholar 
Artyszak, A., Gozdowski, D. & Kucińska, K. The yield and technological quality of sugar beet roots cultivated in mulches. Plant Soil Environ. 60(10), 464–469 (2014).Article 

Google Scholar 
Brittaine, R. & Lutaladio, N. Jatropha: A Smallholder Bioenergy Crop. The Potential for Pro-poor Development Integrated Crop Management, Vol. 8 (IFAD/FAO, 2010). http://www.fao.orgElbehri, A., Segerstedt, A. & Liu, P. Biofuels and the sustainability challenge: A global assessment of sustainability issues, trends and policies for biofuels and related feedstocks. Food and Agric. Organ. United Nations (FAO) xvi-174 (2013).King, A. J. et al. Potential of Jatropha curcas as a source of renewable oil and animal feed. J. Exp. Bot. 60(10), 2897–2905 (2009).CAS 
PubMed 
Article 

Google Scholar 
Raheman, H. 14 Jatropha. Handbook of Bioenergy Crop Plants, 315–345 (2012).Ullah, F., Bano, A. & Nosheen, A. Sustainable measures for biodiesel. Effects 36(23), 2621–2628 (2014).CAS 

Google Scholar 
Irshad, M. et al. Evaluation of Jatropha curcas L. leaves mulching on wheat growth and biochemical attributes under water stress. BMC Plant Biol. 21(1), 1–12 (2021).Article 
CAS 

Google Scholar 
Dieye, T. et al. The effect of Jatropha curcas L. leaf litter decomposition on soil carbon and nitrogen status and bacterial community structure (Senegal). J. Soil Sci. Environ Manag. 7(3), 32–44 (2016).CAS 
Article 

Google Scholar 
Kafi, M. & Salehi, M. Kochia scoparia as a model plant to explore the impact of water deficit on halophytic communities. Pak. J. Bot. 44, 257–262 (2012).
Google Scholar 
Yang, Y. M., Liu, X. J., Li, W. Q. & Li, C. Z. Effect of different mulch materials on winter wheat production in desalinized soil in Heilonggang region of North China. J. Zhejiang Univ. Sci. B 7(11), 858–867 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, Z. K., Wang, Y. J. & Li, F. M. Effect of plastic mulching on soil water use and spring wheat yield in arid region of northwest China. Agric. Water Manag. 75(1), 71–83 (2005).Article 

Google Scholar 
Khan, R. H., Anwar-ul-Haq, K. & Sajjad, M. R. Effect of different types of mulches on grain yield and yield components of wheat (Triticum aestivum) under rainfed condition. J. Biol. Agric. Healthc. 4(12), 85–91 (2014).
Google Scholar 
Weidhuner, A., Afshar, R. K., Luo, Y., Battaglia, M. & Sadeghpour, A. Particle size affects nitrogen and carbon estimate of a wheat cover crop. Agron. J. 111(6), 3398–3402 (2019).CAS 
Article 

Google Scholar 
Ding, Z. et al. The integrated effect of salinity, organic amendments, phosphorus fertilizers, and deficit irrigation on soil properties, phosphorus fractionation and wheat productivity. Sci. Rep. 10(1), 1–13 (2020).Article 
CAS 

Google Scholar 
Rummana, S., Amin, A. K. M. R., Islam, M. S. & Faruk, G. M. Effect of irrigation and mulch materials on growth and yield of wheat. Bang. Agron. J. 21(1), 71–76 (2018).Article 

Google Scholar 
Richard, L. A. Diagnosis and improvement of saline and alkaline soils. Handbook No. 60 (US Depart. Agric., 1954).McLean, E. O. Soil pH and lime requirement. Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, Vol. 9, 199–224 (1983).Walkley, A. A critical examination of a rapid method for determining organic carbon in soils—Effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci. 63, 251–264 (1947).ADS 
CAS 
Article 

Google Scholar 
Singleton, V. L., Orthofer, R. & Lamuela-Raventos, R. M. Analysis of total phenols and other oxidation substrates and antioxidants by means of Folin–Ciocalteu reagent. Methods Enzymol. 299, 152–178 (1999).CAS 
Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
Bremner, J. M. & Mulvaney, C. S. Nitrogen-total. In Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (eds Page, A. L. et al.) 595–624 (Soil Sci. Society America, 1982).
Google Scholar 
Steel, R. G. D., Torrie, J. H. & Dickey, D. A. Principles and Procedures of Statistics: A Biometrical Approach 3rd edn, 246 (McGraw-Hill, 1997).
Google Scholar 
Brady, N. C. & Weil, R. R. Soil colloids: Seat of soil chemical and physical acidity. Nat. Prop. Soils 5(13), 311–358 (2008).
Google Scholar 
Scharenbroch, B. C. & Lloyd, J. E. Particulate organic matter and soil nitrogen availability in urban landscapes. Arboricul. Urb. For. 32(4), 180–191 (2006).Article 

Google Scholar 
Bhadha, J. H., Capasso, J. M., Khatiwada, R., Swanson, S. & LaBorde, C. Raising soil organic matter content to improve water holding capacity. UF/IFAS 1–5 (2017).Chalker-Scott, L. Impact of mulches on landscape plants and the environment—A review. J. Environ. Hortic. 25(4), 239–249 (2007).Article 

Google Scholar 
Liu, Z., Fu, B., Zheng, X. & Liu, G. Plant biomass, soil water content and soil N:P ratio regulating soil microbial functional diversity in a temperate steppe: A regional scale study. Soil Biol. Biochem. 42(3), 445–450 (2010).CAS 
Article 

Google Scholar 
Bai, S. H., Blumfield, T. J. & Reverchon, F. The impact of mulch type on soil organic carbon and nitrogen pools in a sloping site. Biol. Fertil. Soils 50(1), 37–44 (2014).Article 

Google Scholar 
Yang, H. et al. The combined effects of maize straw mulch and no-tillage on grain yield and water and nitrogen use efficiency of dry-land winter wheat (Triticum aestivum L.). Soil Tillage Res. 197, 104485 (2020).Article 

Google Scholar 
Li, X. J. et al. Abscisic acid pretreatment enhances salt tolerance of rice seedlings: Proteomic evidence. Biochim. Biophys. Acta (BBA) Proteins Proteomics 1804(4), 929–940 (2010).CAS 
Article 

Google Scholar 
Fang, S., Xie, B., Liu, D. & Liu, J. Effects of mulching materials on nitrogen mineralization, nitrogen availability and poplar growth on degraded agricultural soil. New For. 41(2), 147–162 (2011).Article 

Google Scholar 
Houghton, J. T. Climate Change 2001: The Scientific Basis 419–470 (2001).Johnson, D. et al. Plant community composition affects the biomass, activity and diversity of microorganisms in limestone grassland soil. Eur. J. Soil Sci. 54(4), 671–678 (2003).Article 

Google Scholar 
Johnson, M. J., Lee, K. Y. & Scow, K. M. DNA finger printing reveals links among agricultural crops, soil properties, and the composition of soil microbial communities. Geoderma 114, 279–303 (2003).ADS 
Article 

Google Scholar 
Nielsen, N. M., Winding, A. & Binnerup, S. Microorganisms as Indicators of Soil Health 15–16 (Ministry of the Environment, National Environ. Res. Inst., 2002).
Google Scholar 
Wilkinson, S. C. et al. PLFA profiles of microbial communities in decomposing conifer litters subject to moisture stress. Soil Biol. Biochem. 34(2), 189–200 (2002).CAS 
Article 

Google Scholar 
Drenovsky, R. E., Vo, D., Graham, K. J. & Scow, K. M. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48(3), 424–430 (2004).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y. Y., Yao, H. Y. & Huang, C. Y. Influence of soil moisture regime on microbial community diversity and activity in a paddy soil. Acta Pedol. Sin. 43, 828–834 (2006).
Google Scholar 
Jensen, K. D., Beier, C., Michelsen, A. & Emmett, B. A. Effects of experimental drought on microbial processes in two temperate heathlands at contrasting water conditions. Appl. Soil Ecol. 24(2), 165–176 (2003).Article 

Google Scholar 
Stoklosa, A., Hura, T., Stupnicka-Rodzynkiewicz, E., Dabkowska, T. & Lepiarczyk, A. The influence of plant mulches on the content of phenolic compounds in soil and primary weed infestation of maize. Acta. Agron. Bot. 61(2), 205–219 (2008).
Google Scholar 
Ohno, T. Oxidation of phenolic acid derivatives by soil and its relevance to allelopathic activity. J. Environ. Qual. 30(5), 1631–1635 (2001).CAS 
PubMed 
Article 

Google Scholar 
Farooq, S., Shahid, M., Khan, M. B., Hussain, M. & Farooq, M. Improving the productivity of bread wheat by good management practices under terminal drought. J. Agric. Crop Sci. 201(3), 173–188 (2015).Article 

Google Scholar 
Madani, A., Rad, A. S., Pazoki, A., Nourmohammadi, G. & Zarghami, R. Wheat (Triticum aestivum L.) grain filling and dry matter partitioning responses to source: Sink modifications under postanthesis water and nitrogen deficiency. Acta Sci. Agron. 32, 145–151 (2010).CAS 
Article 

Google Scholar 
Deng, X. P., Shan, L., Zhang, H. & Turner, N. C. Improving agricultural water use efficiency in arid and semiarid areas of China. Agric. Water Manag. 80(1–3), 23–40 (2006).Article 

Google Scholar 
Athar, H. R., Khan, A. & Ashraf, M. Inducing salt tolerance in wheat by exogenously applied ascorbic acid through different modes. J. Plant Nutr. 32, 1799–1817 (2009).CAS 
Article 

Google Scholar 
Luo, et al. Dual plastic film and straw mulching boosts wheat productivity and soil quality under the El Nino in semiarid Kenya. Sci. Total Environ. 738, 139808 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duan, et al. Improvement of wheat productivity and soil quality by arbuscular mycorrhizal fungi is density-and moisture-dependent. Agron. Sustain. Dev. 41(1), 1–12 (2021).Article 
CAS 

Google Scholar  More

Mesocosm experimentsSi:Nexport measurementsBetween 2010 and 2014, we conducted five in situ mesocosm experiments to assess impacts of OA on natural plankton communities. Study sites covered a large latitudinal gradient (28 °N–79 °N) and diverse oceanic environments/ecosystems (Extended Data Fig. 1 and Extended Data Table 1). Sample collection and processing was conducted every 1 or 2 days throughout the experiments. Sinking particulate matter was obtained from sediment traps attached to the bottom of each mesocosm, thereby collecting the entire material sinking down in the enclosed water column36. Processing of sediment trap samples followed a previous protocol37. Samples for particulate matter suspended in the water column were collected with depth-integrating water samplers (HYDRO-BIOS) and filtered following standard procedures. Biogenic silica was leached from the sediment trap samples and filters by alkaline pulping (0.1 M NaOH at 85 °C). After 135 min the leaching process was terminated with 0.05 M H2SO4 and dissolved silica was measured spectrophotometrically38. Carbon and nitrogen content were determined using an elemental CN analyser (EuroEA)39.Analysis of OA impactsTo test for a systemic influence of OA on Si:Nexport, we synthesized the datasets from the different experiments and (i) conducted a meta-analysis to quantify effect sizes, and (ii) computed probability density estimates. Because the experimental design, the range of CO2 treatments, and the time periods for our analysis of Si:Nexport varied to some extent among experiments (Extended Data Table 1), we pooled mesocosms for ambient conditions and in the ({{p}}_{{{rm{CO}}}_{2}}) range of ~700–1,000 μatm (‘OA treatment’), corresponding to end-of-century values according to RCP 6.0 and 8.5 emission scenarios15. Effect sizes were calculated as log-transformed response ratios lnRR, an approach commonly used in meta-analysis40:$${rm{l}}{rm{n}}{rm{R}}{rm{R}}={rm{l}}{rm{n}}{X}_{{rm{O}}{rm{A}}}-{rm{l}}{rm{n}}{X}_{{rm{c}}{rm{o}}{rm{n}}{rm{t}}{rm{r}}{rm{o}}{rm{l}}},$$where X is the arithmetic mean of Si:Nexport ratios under OA and ambient conditions (Extended Data Table 1). Effect sizes 0 indicate that the effect was positive. Effects are considered statistically significant when 95% confidence intervals (calculated from pooled standard deviations) do not overlap with zero. The overall effect size across all studies was computed by weighing individual effect sizes according to their variance, following the common methodology for meta-analyses40. In addition, we computed probability densities of Si:Nexport based on kernel density estimation, which better accounts for data with skewed or multimodal distributions41. Another advantage of this approach is that it does not require the calculation of temporal means. Instead, the entire data timeseries can be incorporated into the analysis, thus retaining information about temporal variability. Confidence intervals of the density estimates were calculated with a bootstrapping approach using data resampling (1,000 permutations)41. The resulting probability density plots can be interpreted analogously to histograms. Differences among ambient and OA conditions are considered statistically significant when confidence intervals of the probability density distributions do not overlap. Numbers for suspended and sinking Si, C and N (and their respective ratios) for the analysis period are given in Extended Data Table 2.Analysis of pH effects on Si:N in global sediment trap dataWe analysed a recent compilation of global sediment trap data (674 locations collected between 1976 and 2012)35. The aim of this analysis was to assess the influence of pH on opal dissolution in the world ocean. In contrast to the mesocosm experiments, where export fluxes were measured only at one depth, the global dataset provides depth-resolved information, enabling us to examine the vertical change in the Si:N ratio of sinking particulate matter and how this correlates with pH. It has long been known that the silica content of sinking particles increases with depth, as opal dissolution is less efficient than organic matter remineralization25,42. The resulting accumulation of Si relative to N can be quantified as the change in Si:N with increasing depth, that is, the slope of the relationship of depth versus Si:N (ΔSi:N, in units of m−1). Our approach is analogous to previous studies, which used vertical profiles of Si:C as a proxy for differential dissolution/remineralization of opal and organic matter, and its regional variability in the ocean24,42. We extracted all data that (I) included simultaneous measurements of Si and N, and (II) contained vertical profiles with at least three depth levels (so that ΔSi:N [m−1] can be calculated). We then calculated linear regressions for individual Si:N profiles and subsequently extracted those for which Si:N displayed a statistically significant relationship with depth (p  More

Traynor, K. S. et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends Parasitol. 36, 592–606. https://doi.org/10.1016/j.pt.2020.04.004 (2020).CAS 
Article 
PubMed 

Google Scholar 
Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119. https://doi.org/10.1016/j.jip.2009.07.016 (2010).Article 
PubMed 

Google Scholar 
Noel, A., Le Conte, Y. & Mondet, F. Varroa destructor: how does it harm Apis mellifera honey bees and what can be done about it?. Emerg. Top. Life Sci. 4, 45–57. https://doi.org/10.1042/ETLS20190125 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boncristiani, H. et al. World honey bee health: the global distribution of western honey bee (Apis mellifera L.) pests and pathogens. Bee World 98, 2–6 (2020).Article 

Google Scholar 
Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U.S.A. 116, 1792–1801. https://doi.org/10.1073/pnas.1818371116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Di Prisco, G. et al. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health. Proc. Natl. Acad. Sci. USA 113, 3203–3208. https://doi.org/10.1073/pnas.1523515113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mondet, F. et al. Antennae hold a key to Varroa-sensitive hygiene behaviour in honey bees. Sci. Rep. 5, 10454. https://doi.org/10.1038/srep10454 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McMenamin, A. J. & Genersch, E. Honey bee colony losses and associated viruses. Curr. Opin. Insect Sci. 8, 121–129. https://doi.org/10.1016/j.cois.2015.01.015 (2015).Article 
PubMed 

Google Scholar 
Beaurepaire, A. et al. Diversity and global distribution of viruses of the western honey bee Apis mellifera. Insects 11, 239. https://doi.org/10.3390/insects11040239 (2020).Article 
PubMed Central 

Google Scholar 
Levin, S. et al. New viruses from the ectoparasite mite Varroa destructor infesting Apis mellifera and Apis cerana. Viruses 11, 94. https://doi.org/10.3390/v11020094 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Chen, G. et al. A new strain of virus discovered in China specific to the parasitic mite Varroa destructor poses a potential threat to honey bees. Viruses 13, 679. https://doi.org/10.3390/v13040679 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kraberger, S. et al. Genome sequences of two single-stranded DNA viruses identified in Varroa destructor. Genome Announc. 6, e00107-00118. https://doi.org/10.1128/genomeA.00107-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Haddad, N., Horth, L., Al-Shagour, B., Adjlane, N. & Loucif-Ayad, W. Next-generation sequence data demonstrate several pathogenic bee viruses in Middle East and African honey bee subspecies (Apis mellifera syriaca, Apis mellifera intermissa) as well as their cohabiting pathogenic mites (Varroa destructor). Virus Genes 54, 694–705. https://doi.org/10.1007/s11262-018-1593-9 (2018).CAS 
Article 
PubMed 

Google Scholar 
Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 351, 594–597. https://doi.org/10.1126/science.aac9976 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Remnant, E. J., Mather, N., Gillard, T. L., Yagound, B. & Beekman, M. Direct transmission by injection affects competition among RNA viruses in honeybees. Proc. Royal Soc. B 286, 20182452. https://doi.org/10.1098/rspb.2018.2452 (2019).CAS 
Article 

Google Scholar 
Martin, S. J. & Brettell, L. E. Deformed wing virus in honeybees and other insects. Ann. Rev. Virol. 6, 49–69. https://doi.org/10.1146/annurev-virology-092818-015700 (2019).CAS 
Article 

Google Scholar 
Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306. https://doi.org/10.1126/science.1220941 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Loope, K. J., Baty, J. W., Lester, P. J. & Wilson Rankin, E. E. Pathogen shifts in a honeybee predator following the arrival of the Varroa mite. Proc. Royal Soc. B 286, 20182499. https://doi.org/10.1098/rspb.2018.2499 (2019).CAS 
Article 

Google Scholar 
McMahon, D. P. et al. Elevated virulence of an emerging viral genotype as a driver of honeybee loss. Proc. Royal Soc. B https://doi.org/10.1098/rspb.2016.0811 (2016).Article 

Google Scholar 
Grindrod, I., Kevill, J. L., Villalobos, E. M., Schroeder, D. C. & Ten Martin, S. J. years of Deformed wing virus (DWV) in Hawaiian honey bees (Apis mellifera), the dominant DWV-A variant is potentially being replaced by variants with a DWV-B coding sequence. Viruses 13, 969. https://doi.org/10.3390/v13060969 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kevill, J. L., Stainton, K. C., Schroeder, D. C. & Martin, S. J. Deformed wing virus variant shift from 2010 to 2016 in managed and feral UK honey bee colonies. Arch. Virol. 166, 2693–2702. https://doi.org/10.1007/s00705-021-05162-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226. https://doi.org/10.1146/annurev-ento-011118-111942 (2019).CAS 
Article 
PubMed 

Google Scholar 
Natsopoulou, M. E. et al. The virulent, emerging genotype B of Deformed wing virus is closely linked to overwinter honeybee worker loss. Sci. Rep. 7, 5242. https://doi.org/10.1038/s41598-017-05596-3 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Iwasaki, J. M., Barratt, B. I., Lord, J. M., Mercer, A. R. & Dickinson, K. J. The New Zealand experience of varroa invasion highlights research opportunities for Australia. Ambio 44, 694–704. https://doi.org/10.1007/s13280-015-0679-z (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Solignac, M. et al. The invasive Korea and Japan types of Varroa destructor, ectoparasitic mites of the Western honeybee (Apis mellifera), are two partly isolated clones. Proc. Royal Soc. B 272, 411–419. https://doi.org/10.1098/rspb.2004.2853 (2005).Article 

Google Scholar 
Hall, R. J. et al. Apicultural practice and disease prevalence in Apis mellifera, New Zealand: A longitudinal study. J. Apic. Res. 60, 644–658. https://doi.org/10.1080/00218839.2021.1936422 (2021).Article 

Google Scholar 
Mondet, F., de Miranda, J. R., Kretzschmar, A., Le Conte, Y. & Mercer, A. R. On the front line: quantitative virus dynamics in honeybee (Apis mellifera L) colonies along a new expansion front of the parasite Varroa destructor. PLoS Pathog. 10, e1004323 (2014).Article 

Google Scholar 
McFadden, A. M. J. et al. Israeli acute paralysis virus not detected in Apis mellifera in New Zealand in a national survey. J. Apic. Res. 53, 520–527. https://doi.org/10.3896/ibra.1.53.5.03 (2015).Article 

Google Scholar 
Dobelmann, J., Felden, A. & Lester, P. J. Genetic strain diversity of multi-host RNA viruses that infect a wide range of pollinators and associates is shaped by geographic origins. Viruses 12, 358. https://doi.org/10.3390/v12030358 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Gruber, M. A. M. et al. Single-stranded RNA viruses infecting the invasive argentine ant Linepithema humile. Sci. Rep. 7, 3304. https://doi.org/10.1038/s41598-017-03508-z (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brenton-Rule, E. C. et al. The origins of global invasions of the German wasp (Vespula germanica) and its infection with four honey bee viruses. Biol. Invasions 20, 3445–3460. https://doi.org/10.1007/s10530-018-1786-0 (2018).Article 

Google Scholar 
Lester, P. J., Buick, K. H., Baty, J. W., Felden, A. & Haywood, J. Different bacterial and viral pathogens trigger distinct immune responses in a globally invasive ant. Sci. Rep. 9, 5780. https://doi.org/10.1038/s41598-019-41843-5 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lester, P. J. et al. No evidence of enemy release in pathogen and microbial communities of common wasps (Vespula vulgaris) in their native and introduced range. PLoS ONE 10, e0121358. https://doi.org/10.1371/journal.pone.0121358 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Levin, S., Sela, N. & Chejanovsky, N. Two novel viruses associated with the Apis mellifera pathogenic mite Varroa destructor. Sci. Rep. 6, 37710. https://doi.org/10.1038/srep37710 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cornman, S. R. et al. Genomic survey of the ectoparasitic mite Varroa destructor, a major pest of the honey bee Apis mellifera. BMC Genom. 11, 602. https://doi.org/10.1186/1471-2164-11-602 (2010).CAS 
Article 

Google Scholar 
Gauthier, L. et al. The Apis mellifera filamentous virus genome. Viruses 7, 3798–3815. https://doi.org/10.3390/v7072798 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Giuffre, C., Lubkin, S. R. & Tarpy, D. R. Does viral load alter behavior of the bee parasite Varroa destructor?. PLoS ONE 14, e0217975. https://doi.org/10.1371/journal.pone.0217975 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
De Smet, L. et al. BeeDoctor, a versatile MLPA-based diagnostic tool for screening bee viruses. PLoS ONE 7, e47953. https://doi.org/10.1371/journal.pone.0047953 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Runckel, C. et al. Temporal analysis of the honey bee microbiome reveals four novel viruses and seasonal prevalence of known viruses, Nosema, and Crithidia. PLoS ONE 6, e20656. https://doi.org/10.1371/journal.pone.0020656 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Remnant, E. J. et al. A diverse range of novel RNA viruses in geographically distinct honey bee populations. J. Virol. 91, e00158. https://doi.org/10.1128/JVI.00158-17 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Navajas, M. et al. New Asian types of Varroa destructor: a potential new threat for world apiculture. Apidologie 41, 181–193. https://doi.org/10.1051/apido/2009068 (2010).CAS 
Article 

Google Scholar 
Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755. https://doi.org/10.1093/bioinformatics/17.8.754 (2001).CAS 
Article 
PubMed 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. https://doi.org/10.1038/nmeth.3317 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genom. 20, 275. https://doi.org/10.1186/s12864-019-5642-0 (2019).Article 

Google Scholar 
Techer, M. A. et al. Divergent evolutionary trajectories following speciation in two ectoparasitic honey bee mites. Commun. Biol. 2, 357. https://doi.org/10.1038/s42003-019-0606-0 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512. https://doi.org/10.1038/nprot.2013.084 (2013).CAS 
Article 
PubMed 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using Diamond. Nat. Methods 12, 59–60. https://doi.org/10.1038/nmeth.3176 (2015).CAS 
Article 
PubMed 

Google Scholar 
National Center for Biotechnology Information (NCBI). Bethesda (MD), National Library of Medicine (US), National Center for Biotechnology Information; [1988]–[cited 2017 Apr 06]. Available from: https://www.ncbi.nlm.nih.gov/.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421. https://doi.org/10.1186/1471-2105-10-421 (2009).CAS 
Article 

Google Scholar 
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R: A language and environment for statistical computing v. 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria, 2020).Oksanen, J. et al. vegan: community ecology package. (R package version 2.4–0. https://CRAN.R-project.org/package=vegan., 2016).Li, D. et al. Molecular detection of small hive beetle Aethina tumida Murray (Coleoptera: Nitidulidae): DNA barcoding and development of a real-time PCR assay. Sci. Rep. 8, 9623. https://doi.org/10.1038/s41598-018-27603-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189. https://doi.org/10.1023/a:1006456720416 (2000).CAS 
Article 
PubMed 

Google Scholar 
Bradford, E. L., Christie, C. R., Campbell, E. M. & Bowman, A. S. A real-time PCR method for quantification of the total and major variant strains of the Deformed wing virus. PLoS ONE 12, e0190017. https://doi.org/10.1371/journal.pone.0190017 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dynes, T. L. et al. Fine scale population genetic structure of Varroa destructor, an ectoparasitic mite of the honey bee (Apis mellifera). Apidologie 48, 93–101. https://doi.org/10.1007/s13592-016-0453-7 (2017).Article 

Google Scholar 
Maggi, M. et al. Genetic structure of Varroa destructor populations infesting Apis mellifera colonies in Argentina. Exp. Appl. Acarol. 56, 309–318. https://doi.org/10.1007/s10493-012-9526-0 (2012).CAS 
Article 
PubMed 

Google Scholar 
Hasegawa, N., Techer, M. & Mikheyev, A. S. A toolkit for studying Varroa genomics and transcriptomics: preservation, extraction, and sequencing library preparation. BMC Genom. 22, 54. https://doi.org/10.1186/s12864-020-07363-7 (2021).CAS 
Article 

Google Scholar 
Gisder, S. & Genersch, E. Direct evidence for infection of Varroa destructor mites with the bee-pathogenic Deformed wing virus variant B, but not variant A, via fluorescence in situ hybridization analysis. J. Virol. 95, e01786. https://doi.org/10.1128/JVI.01786-20 (2021).CAS 
Article 
PubMed Central 

Google Scholar 
Gisder, S., Aumeier, P. & Genersch, E. Deformed wing virus: replication and viral load in mites (Varroa destructor). J. Gen. Virol. 90, 463–467. https://doi.org/10.1099/vir.0.005579-0 (2009).CAS 
Article 
PubMed 

Google Scholar 
Yue, C. & Genersch, E. RT-PCR analysis of Deformed wing virus in honeybees (Apis mellifera) and mites (Varroa destructor). J. Gen. Virol. 86, 3419–3424. https://doi.org/10.1099/vir.0.81401-0 (2005).CAS 
Article 
PubMed 

Google Scholar 
Posada-Florez, F. et al. Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner. Sci. Rep. 9, 12445. https://doi.org/10.1038/s41598-019-47447-3 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Budge, G. E. et al. Chronic bee paralysis as a serious emerging threat to honey bees. Nat. Commun. 11, 2164. https://doi.org/10.1038/s41467-020-15919-0 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Graystock, P. et al. The Trojan hives: pollinator pathogens, imported and distributed in bumblebee colonies. J. Appl. Ecol. 50, 1207–1215. https://doi.org/10.1111/1365-2664.12134 (2013).Article 

Google Scholar 
Roberts, J. M. K., Simbiken, N., Dale, C., Armstrong, J. & Anderson, D. L. Tolerance of honey bees to Varroa mite in the absence of deformed wing virus. Viruses https://doi.org/10.3390/v12050575 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Brettell, L. E. & Martin, S. J. Oldest Varroa tolerant honey bee population provides insight into the origins of the global decline of honey bees. Sci. Rep. 7, 45953. https://doi.org/10.1038/srep45953 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Herrero, S. et al. Identification of new viral variants specific to the honey bee mite Varroa destructor. Exp. Appl. Acarol. 79, 157–168. https://doi.org/10.1007/s10493-019-00425-w (2019).CAS 
Article 
PubMed 

Google Scholar 
Dobelmann, J. et al. Fitness in invasive social wasps: the role of variation in viral load, immune response and paternity in predicting nest size and reproductive output. Oikos 126, 1208–1218. https://doi.org/10.1111/oik.04117 (2017).CAS 
Article 

Google Scholar 
Shojaei, A., Nourian, A., Khanjani, M. & Mahmoodi, P. The first molecular characterization of Lake Sinai virus in honey bees (Apis mellifera) and Varroa destructor mites in Iran. J. Apic. Res. https://doi.org/10.1080/00218839.2021.1921467 (2021).Article 

Google Scholar 
Hartmann, U., Forsgren, E., Charriere, J. D., Neumann, P. & Gauthier, L. Dynamics of Apis mellifera filamentous Virus (AmFV) infections in honey bees and relationships with other parasites. Viruses 7, 2654–2667. https://doi.org/10.3390/v7052654 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Nanetti, A., Bortolotti, L. & Cilia, G. Pathogens spillover from honey bees to other arthropods. Pathogens 10, 1044. https://doi.org/10.3390/pathogens10081044 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Norton, A. M., Remnant, E. J., Buchmann, G. & Beekman, M. Accumulation and competition amongst Deformed wing virus genotypes in naive Australian honeybees provides insight into the increasing global prevalence of genotype B. Front. Microbiol. 11, 620. https://doi.org/10.3389/fmicb.2020.00620 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Mordecai, G. J. et al. Superinfection exclusion and the long-term survival of honey bees in Varroa-infested colonies. ISME J. 10, 1182–1191. https://doi.org/10.1038/ismej.2015.186 (2016).CAS 
Article 
PubMed 

Google Scholar  More

Simberloff, D. et al. Impacts of biological invasions: what’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).PubMed 
Article 

Google Scholar 
Pyšek, P. et al. Scientists’ warning on invasive alien species. Biol. Rev. 95, 1511–1534 (2020).PubMed 
Article 

Google Scholar 
Daru, B. H. et al. Widespread homogenization of plant communities in the Anthropocene. Nat. Commun. 12, 6983 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bradley, B. A. et al. Global change, global trade, and the next wave of plant invasions. Front. Ecol. Environ. 10, 20–28 (2012).Article 

Google Scholar 
Pyšek, P. & Richardson, D. M. Traits associated with invasiveness in alien plants: Where do we stand? In Biological Invasions (ed. Nentwig, W.) 97–125 (Springer, Berlin, 2007).Chapter 

Google Scholar 
Pyšek, P. et al. Naturalization of central European plants in North America: Species traits, habitats, propagule pressure, residence time. Ecology 96, 762–774 (2015).PubMed 
Article 

Google Scholar 
Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: A null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).Article 

Google Scholar 
Richardson, D. M. & Pyšek, P. Naturalization of introduced plants: Ecological drivers of biogeographic patterns. New Phytol. 196, 383–396 (2012).PubMed 
Article 

Google Scholar 
Moravcová, L., Pyšek, P., Jarošík, V. & Pergl, J. Getting the right traits: Reproductive and dispersal characteristics predict the invasiveness of herbaceous plant species. PLoS ONE 10, e0123634 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thompson, K., Bakker, J. P. & Bekker, R. M. Soil Seed Banks of NW Europe: Methodology, Density and Longevity (Cambridge University Press, Cambridge, 1997).
Google Scholar 
Walck, J. L., Baskin, J. M., Baskin, C. C. & Hidayati, S. N. Defining transient and persistent seed banks in species with pronounced seasonal dormancy and germination patterns. Seed Sci. Res. 15, 189–196 (2005).Article 

Google Scholar 
Gioria, M., Le Roux, J. J., Hirsch, H., Moravcová, L. & Pyšek, P. Characteristics of the soil seed bank of invasive and non-invasive plants in their native and alien distribution range. Biol. Invasions 21, 2313–2332 (2019).Article 

Google Scholar 
Gioria, M. et al. Persistent soil seed banks promote naturalization and invasiveness in flowering plants. Ecol. Lett. 24, 1655–1667 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gioria, M., Pyšek, P. & Moravcová, L. Soil seed banks in plant invasions: Promoting species invasiveness and long-term impact on plant community dynamics. Preslia 84, 327–350 (2012).
Google Scholar 
Venable, D. L. Bet hedging in a guild of desert annuals. Ecology 88, 1086–1090 (2007).PubMed 
Article 

Google Scholar 
Venable, D. L. & Brown, J. S. The selective interactions of dispersal, dormancy, and seed size as adaptations for reducing risk in variable environments. Am. Nat. 131, 360–384 (1988).Article 

Google Scholar 
Adams, V. M., Marsh, D. M. & Knox, J. S. Importance of the seed bank for population viability and population monitoring in a threatened wetland herb. Biol. Conserv. 124, 425–436 (2005).Article 

Google Scholar 
Harper, J. The Population Biology of Plants (Academic Press, London, 1977).
Google Scholar 
Warr, S. J., Thompson, K. & Kent, M. Seed banks as a neglected area of biogeographic research: A review of literature and sampling techniques. Progr. Phys. Geogr. 17, 329–347 (1993).Article 

Google Scholar 
Thompson, K., Bakker, J. P., Bekker, R. M. & Hodgson, J. Ecological correlates of seed persistence in soil in the north-west European flora. J. Ecol. 86, 163–169 (1998).Article 

Google Scholar 
Gioria, M., Pyšek, P., Baskin, C. & Carta, A. Phylogenetic relatedness mediates persistence and density of soil seed banks. J. Ecol. 108, 2121–2131 (2020).Article 

Google Scholar 
Pyšek, P. et al. The global invasion success of Central European plants is related to distribution characteristics in their native range and species traits. Divers. Distrib. 15, 891–903 (2009).Article 

Google Scholar 
Gallagher, R. V., Randall, R. P. & Leishman, M. R. Trait differences between naturalized and invasive plant species independent of residence time and phylogeny. Conserv. Biol. 29, 360–369 (2015).CAS 
PubMed 
Article 

Google Scholar 
Chesson, P. L. & Warner, R. R. Environmental variability promotes coexistence in lottery competitive systems. Am. Nat. 117, 923–943 (1981).MathSciNet 
Article 

Google Scholar 
Gioria, M. & Pyšek, P. Early bird catches the worm: Germination as a critical step in plant invasion. Biol. Invasions 19, 1055–1080 (2017).Article 

Google Scholar 
Gioria, M., Pyšek, P. & Osborne, B. Timing is everything: Does early and late germination favor invasions by herbaceous alien plants?. J. Plant Ecol. 11, 4–16 (2018).
Google Scholar 
Gioria, M. & Osborne, B. A. Resource competition in plant invasions: Emerging patterns and research needs. Front. Plant Sci. 5, 1–21 (2014).Article 

Google Scholar 
D’Antonio, C. M., Dudley, T. L. & Mack, M. C. Disturbance and biological invasions: Direct effects and feedbacks. In Ecosystems of Disturbed Ground (ed. Walker, L.) 413–452 (Elsevier, Oxford, 1999).
Google Scholar 
Davis, M. A., Grime, J. P. & Thompson, K. Fluctuating resources in plant communities: A general theory of invasibility. J. Ecol. 88, 528–534 (2000).Article 

Google Scholar 
Hierro, J. L., Villarreal, D., Eren, Ö., Graham, J. M. & Callaway, R. M. Disturbance facilitates invasion: The effects are stronger abroad than at home. Am. Nat. 168, 144–156 (2006).PubMed 
Article 

Google Scholar 
Chytrý, M. et al. Habitat invasions by alien plants: A quantitative comparison among Mediterranean, subcontinental and oceanic regions of Europe. J. Appl. Ecol. 45, 448–458 (2008).Article 

Google Scholar 
Templeton, A. & Levin, D. Evolutionary consequences of seed pools. Am. Nat. 114, 232–249 (1979).Article 

Google Scholar 
Honnay, O., Bossuyt, B., Jacquemyn, H., Shimono, A. & Uchiyama, K. Can a seed bank maintain the genetic variation in the above ground plant population?. Oikos 117, 1–5 (2008).Article 

Google Scholar 
Donohue, K., Rubio de Casas, R., Burghardt, L., Kovach, K. & Willis, C. G. Germination, post-germination adaptation, and species ecological ranges. Annu. Rev. Ecol. Evol. Syst. 41, 293–319 (2010).Article 

Google Scholar 
Gioria, M., Osborne, B. & Pyšek, P. Soil seed banks under a warming climate. In Plant Regeneration from Seeds: A global Warming Perspective (eds Baskin, C. & Baskin, J.) 285–298 (Academic Press, London, 2022).Chapter 

Google Scholar 
Blossey, B., Nuzzo, V. & Davalos, A. Climate and rapid local adaptation as drivers of germination and seed bank dynamics of Alliaria petiolata (garlic mustard) in North America. J. Ecol. 105, 1485–1495 (2017).Article 

Google Scholar 
Hamilton, M. A. et al. Life-history correlates of plant invasiveness at regional and continental scales. Ecol. Lett. 8, 1066–1074 (2005).Article 

Google Scholar 
Richardson, D. M. & Kluge, R. L. Seed banks of invasive Australian Acacia species in South Africa: Role in invasiveness and options for management. Persp. Plant Ecol. Evol. Syst. 10, 161–177 (2008).Article 

Google Scholar 
Hartzler, R. G., Buhler, D. D. & Stoltenberg, D. E. Emergence characteristics of four annual weed species. Weed Sci. 47, 578–584 (1999).CAS 
Article 

Google Scholar 
Skálová, H., Moravcová, L., Čuda, J. & Pyšek, P. Seed-bank dynamics of native and invasive Impatiens species during a five-year field experiment under various environmental conditions. NeoBiota 50, 75–95 (2019).Article 

Google Scholar 
Moravcová, L. et al. Seed germination, dispersal and seed bank in Heracleum mantegazzianum. In Ecology and Management of Giant Hogweed (Heracleum mantegazzianum) (eds Pyšek, P. et al.) 74–91 (CAB International, Wallingford, 2007).Chapter 

Google Scholar 
Gioria, M. & Osborne, B. Assessing the impact of plant invasions on soil seed bank communities: Use of univariate and multivariate statistical approaches. J. Veg. Sci. 20, 547–556 (2009).Article 

Google Scholar 
Long, R. L. et al. Seed persistence in the field may be predicted by laboratory-controlled aging. Weed Sci. 56, 523–528 (2008).ADS 
CAS 
Article 

Google Scholar 
Carta, A., Bottega, S. & Spanò, C. Aerobic environment ensures viability and antioxidant capacity when seeds are wet with negative effect when moist: Implications for persistence in the soil. Seed Sci. Res. 28, 16–23 (2018).CAS 
Article 

Google Scholar 
Pyšek, P. et al. Catalogue of alien plants of the Czech Republic (2nd edition): Checklist update, taxonomic diversity and invasion patterns. Preslia 84, 155–255 (2012).
Google Scholar 
Thompson, K., Band, S. & Hodgson, J. Seed size and shape predict persistence in soil. Funct. Ecol. 7, 236–241 (1993).Article 

Google Scholar 
Moles, A. T., Hodson, D. W. & Webb, C. J. Seed size and shape and persistence in the soil in the New Zealand flora. Oikos 89, 541–545 (2000).Article 

Google Scholar 
Leon, R. G. & Owen, M. D. K. Artificial and natural seed banks differ in seedling emergence patterns. Weed Sci. 52, 531–537 (2004).CAS 
Article 

Google Scholar 
Thompson, K. & Grime, P. J. Seasonal variation in seed banks of herbaceous species in ten contrasting habitats. J. Ecol. 67, 893–921 (1979).Article 

Google Scholar 
Lambrinos, J. G. Spatially variable propagule pressure and herbivory influence invasion of chaparral shrubland by an exotic grass. Oecologia 147, 327–334 (2006).ADS 
PubMed 
Article 

Google Scholar 
Wainwright, C. E., Wolkovich, E. M. & Cleland, E. E. Seasonal priority effects: Implications for invasion and restoration in a semi-arid system. J. Appl. Ecol. 49, 234–241 (2012).Article 

Google Scholar 
Moravcová, L., Pyšek, P., Jarošík, V., Havlíčková, V. & Zákravský, P. Reproductive characteristics of neophytes in the Czech Republic: Traits of invasive and non-invasive species. Preslia 82, 365–390 (2010).
Google Scholar 
Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties 2nd edn. (John Wiley & Sons, Oxford, 2001).
Google Scholar 
Mihulka, S., Pyšek, P. & Pyšek, A. Oenothera coronifera, a new alien species for the Czech flora, and Oenothera stricta, recorded again after two centuries. Preslia 75, 263–270 (2003).
Google Scholar 
Fenner, M. & Thompson, K. The Ecology of Seeds (Cambridge University Press, Cambridge, 2005).Book 

Google Scholar 
Grime, J. P., Hodgson, J. G. & Hunt, R. Comparative Plant Ecology: A Functional Approach to Common British Species 2nd edn. (Castlepoint Press, Colvend, Dalbeattie, Kirkcudrightshire, Scotland, 2007).
Google Scholar 
Gioria, M. & Osborne, B. Similarities in the impact of three large invasive plant species on soil seed bank communities. Biol. Invasions 12, 1671–1683 (2010).Article 

Google Scholar 
Gioria, M. & Pyšek, P. The legacy of plant invasions: Changes in the soil seed bank of invaded plant communities. Bioscience 66, 40–53 (2016).Article 

Google Scholar 
Carta, A., Hanson, S. & Müller, J. V. Plant regeneration from seeds responds to phylogenetic relatedness and local adaptation in Mediterranean Romulea (Iridaceae) species. Ecol. Evol. 6, 4166–4178 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Arène, F., Affre, L., Doxa, A. & Saatkamp, A. Temperature but not moisture response of germination shows phylogenetic constraints while both interact with seed mass and lifespan. Seed Sci. Res. 27, 110–120 (2017).Article 

Google Scholar 
Zhang, C., Willis, C. G., Donohue, K., Ma, Z. & Du, G. Effects of environment, life-history and phylogeny on germination strategy of 789 angiosperms species on the eastern Tibetan Plateau. Ecol. Indic. 129, 107974 (2021).Article 

Google Scholar 
Zheng, J., Guo, Z. & Wang, X. Seed mass of angiosperm woody plants better explained by life history traits than climate across China. Sci. Rep. 7, 2741 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thompson, K., Ceriani, R. M., Bakker, J. P. & Bekker, R. M. Are seed dormancy and persistence in soil related?. Seed Sci. Res. 13, 97–100 (2003).Article 

Google Scholar 
Long, R. L. et al. The ecophysiology of seed persistence: A mechanistic view of the journey to germination or demise. Biol. Rev. Camb. Philos. Soc. 90, 31–59 (2015).PubMed 
Article 

Google Scholar 
Moodley, D., Geerts, S., Richardson, D. M. & Wilson, J. R. U. Different traits determine introduction, naturalization and invasion success in woody plants: Proteaceae as a test case. PLoS ONE 8, e75078 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pyšek, P., Sádlo, J. & Mandák, B. Catalogue of alien plants of the Czech Republic. Preslia 74, 97–186 (2002).
Google Scholar 
Pyšek, P. et al. Alien plants in checklists and floras: Towards better communication between taxonomists and ecologists. Taxon 53, 131–143 (2004).Article 

Google Scholar 
WFO World Flora Online. http://www.worldfloraonline.org (2021).Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 
PubMed 
Article 

Google Scholar 
Ellis, R. H. & Roberts, E. H. Improved equations for the prediction of seed longevity. Ann. Bot. 45, 13–30 (1980).Article 

Google Scholar 
Butler, L. H., Hay, F. R., Ellis, R. H., Smith, R. D. & Murray, T. B. Priming and re-drying improve the survival of mature seeds of Digitalis purpurea during storage. Ann. Bot. 103, 1261–1270 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jin, Y. & Qian, H. V. PhyloMaker: An R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).Article 

Google Scholar 
Qian, H. & Jin, Y. Are phylogenies resolved at the genus level appropriate for studies on phylogenetic structure of species assemblages?. Plant Divers. 43, 255–263 (2021).PubMed 
Article 

Google Scholar 
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).PubMed 
Article 

Google Scholar 
de Villemereuil, P. & Nakagawa, S. General quantitative genetic methods for comparative biology. In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 287–303 (Springer-Verlag, Berlin, 2014).Chapter 

Google Scholar 
Blomberg, S. P., Garland, T. Jr. & Ives, A. R. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution 57, 717–745 (2003).PubMed 
Article 

Google Scholar 
Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57, 591–601 (2008).PubMed 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2022). Available online at: https://www.R-project.org More

Storey, M. et al. Timing of hot spot—Related volcanism and the breakup of Madagascar and India. Science (80-) 267, 852–855 (1995).CAS 
Article 
ADS 

Google Scholar 
Wilmé, L., Goodman, S. M. & Ganzhorn, J. U. Biogeographic evolution of Madagascar’s microendemic biota. Science (80-) 312, 1063–1065 (2006).Article 
ADS 
CAS 

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

Google Scholar 
Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 

Google Scholar 
Rakotomanana, H., Jenkins, R. K. B. & Ratsimbazafy, J. Conservation challenges for Madagascar in the next decade. In Conservation Biology: Voices from the Tropics (eds Raven, P. H., Sodhi, N. S. & Gibson, L.) 33–39 (Wiley-Blackwell, 2013). https://doi.org/10.1002/9781118679838.ch5.Jenkins, R. K. B. et al. Extinction risks and the conservation of Madagascar’s reptiles. PLoS ONE 9, 1 – 14 (2014). https://doi.org/10.1371/journal.pone.0100173Velosoa, J. et al. An integrated research, management, and community conservation program for the Rere (Madagascar Big-headed turtle), Erymnochelys madagascariensis. In Chelonian Research Monographs, Contributions in Turtle and Tortoise Research (eds Rhodin, A. G. J.) 171–177 (Chelonian Research Foundation, 2014). https://doi.org/10.3854/crm.6.a27p171.Leuteritz, T., Kuchling, G., Garcia, G. & Velosoa, J. Erymnochelys madagascariensis. In Chelonian Research Monographs, Contributions in Turtle and Tortoise Research (eds Rhodin, A. G. J.) 56–58 (Chelonian Research Foundation, 2014). https://doi.org/10.3854/crm.6.a11p56.Rafeliarisoa, T., Shore, G., Engberg, S., Louis, E. & Brenneman, R. Characterization of 11 microsatellite marker loci in the Malagasy big-headed turtle (Erymnochelys madagascariensis). Mol. Ecol. Notes 6, 1228–1230 (2006).CAS 
Article 

Google Scholar 
Roca, V., García, G. & Montesinos, A. Gastrointestinal helminths found in the three freshwater turtles (Erymnochelys madagascariensis, Pelomedusa subrufa and Pelusios castanoides) from Ankarafantsika National Park, Madagascar. Helminthologia 44, 177–182 (2007).Article 

Google Scholar 
Kuchling, G. & Garcia, G. Pelomedusidae, freshwater turtles. In The Natural History of Madagascar (eds Goodman, S. M. & Benstead, J. P.) 956–960 (University of Chicago Press, 2003).
Google Scholar 
Pedrono, M. & Smith, L. Overview of the natural history of Madagascar’s endemic tortoises and freshwater turtles: Essential components for effective conservation. In Chelonian Research Monographs, Contributions in Turtle and Tortoise Research (eds Rhodin, A. G. J.) 59–66 (Chelonian Research Foundation, 2014). https://doi.org/10.3854/crm.6.a12p59.Kuchling, G. Population structure, reproductive potential and increasing exploitation of the freshwater turtle Erymnochelys madagascariensis. Biol. Conserv. 43, 107–113 (1988).Article 

Google Scholar 
Allnutt, T. F. et al. A method for quantifying biodiversity loss and its application to a 50-year record of deforestation across Madagascar. Conserv. Lett. 1, 173–181 (2008).Article 

Google Scholar 
Leuteritz, T., Kuchling, G., Garcia, G. & Velosoa, J. Erymnochelys madagascariensis (errata version published in 2016). The IUCN Red List of Threatened Species. 2008, 1–3 (2008).Kuchling, G. Concept and design of the Madagascar side-necked turtle Erymnochelys madagascariensis breeding facility at Ampijoroa, Madagascar. Dodo 36, 62–74 (2000).
Google Scholar 
Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: A review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20, 1843–1861 (2011).Article 

Google Scholar 
Stanton, D. W. G. et al. Genetic structure of captive and free-ranging okapi (Okapia johnstoni) with implications for management. Conserv. Genet. 16, 1115–1126 (2015).Article 

Google Scholar 
Boumans, L., Vieites, D. R., Glaw, F. & Vences, M. Geographical patterns of deep mitochondrial differentiation in widespread Malagasy reptiles. Mol. Phylogenet. Evol. 45, 822–839 (2007).CAS 
PubMed 
Article 

Google Scholar 
Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 

Google Scholar 
Pearson, R. G. & Raxworthy, C. J. The evolution of local endemism in Madagascar: Watershed versus climatic gradient hypotheses evaluated by null biogeographic models. Evolution (New York) 63, 959–967 (2009).
Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: Analysis of data for pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Hulce, D., Li, X., Snyder-Leiby, T. & Liu, J. GeneMarker® genotyping software: Tools to increase the statistical power of DNA fragment analysis. J. Biomol. Tech. https://doi.org/10.1002/wps.20394 (2011).Article 
PubMed Central 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
CAS 

Google Scholar 
Carlsson, J. Effects of microsatellite null alleles on assignment testing. J. Hered. 99, 616–623 (2008).CAS 
PubMed 
Article 

Google Scholar 
Bossuyt, F. & Milinkovitch, M. C. Convergent adaptive radiations in Madagascan and Asian ranid frogs reveal covariation between larval and adult traits. Proc. Natl. Acad. Sci. U. S. A. 97, 6585–6590 (2000).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
Beaumont, M. A. Detecting population expansion and decline using microsatellites. Genetics 153, 2013–2029 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bulut, Z. et al. Microsatellite mutation rates in the eastern tiger salamander (Ambystoma tigrinum tigrinum) differ 10-fold across loci. Genetica 136, 501–504 (2009).CAS 
PubMed 
Article 

Google Scholar 
Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
Plummer, M. & Murrell, P. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News. 6, 7–11 (2006).R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2008). https://doi.org/10.1017/CBO9781107415324.004.Book 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of Population Structure Using Multilocus Genotype Data. Genetics 155, 945–959 (2000). https://doi.org/10.1111/j.1471-8286.2007.01758.x.Puechmaille, S. J. The program structure does not reliably recover the correct population structure when sampling is uneven: Subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16, 608–627 (2016).PubMed 
Article 

Google Scholar 
Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS ONE 7, e45170 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Francis, R. M. Pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Res. 17, 27–32 (2017).Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 

Google Scholar 
Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3, 167–169 (2003).CAS 
Article 

Google Scholar 
Narum, S. R. Beyond Bonferroni: Less conservative analyses for conservation genetics. Conserv. Genet. 7, 783–787 (2006).CAS 
Article 

Google Scholar 
Goudet, J. FSTAT (version 1.2): A computer program to calculate F-statistics. J. Hered. 86, 485–486 (1995).Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
Article 

Google Scholar 
Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).CAS 
PubMed 
Article 

Google Scholar 
Prost, S. & Anderson, C. N. K. TempNet: A method to display statistical parsimony networks for heterochronous DNA sequence data. Methods Ecol. Evol. 2, 663–667 (2011).Article 

Google Scholar 
Paquette, S. R. et al. Riverbeds demarcate distinct conservation units of the radiated tortoise (Geochelone radiata) in southern Madagascar. Conserv. Genet. 8, 797–807 (2007).CAS 
Article 

Google Scholar 
Bouchard, C., Tessier, N. & Lapointe, F. J. Watersheds influence the wood turtle’s (Glyptemys insculpta) genetic structure. Conserv. Genet. 20, 653–664 (2019).Article 

Google Scholar 
Perlman, S. J., Hodson, C. N., Hamilton, P. T., Opit, G. P. & Gowen, B. E. Maternal transmission, sex ratio distortion, and mitochondria. Proc. Natl. Acad. Sci. U. S. A. 112, 10162–10168 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Pearse, D. E. et al. Estimating population structure under nonequilibrium conditions in a conservation context: Continent-wide population genetics of the giant Amazon river turtle, Podocnemis expansa (Chelonia; Podocnemididae). Mol. Ecol. 15, 985–1006 (2006).CAS 
PubMed 
Article 

Google Scholar 
Pearse, D. E. & Avise, J. C. Turtle mating systems: Behavior, sperm storage, and genetic paternity. J. Hered. 92, 206–211 (2001).CAS 
PubMed 
Article 

Google Scholar 
Claussen, M. et al. Simulation of an abrupt change in Saharan vegetation in the mid-Holocene. Geophys. Res. Lett. 26, 2037–2040 (1999).Article 
ADS 

Google Scholar 
Virah-Sawmy, M., Willis, K. J. & Gillson, L. Threshold response of Madagascar’s littoral forest to sea-level rise. Glob. Ecol. Biogeogr. 18, 98–110 (2009).Article 

Google Scholar 
Wahlund, S. Zusammensetzung von populationen und korrelationserscheinungen vom standpunkt der vererbungslehre aus betrachtet. Hereditas 11, 65–106 (1928).Article 

Google Scholar 
Hurst, G. D. D. & Jiggins, F. M. Problems with mitochondrial DNA as a marker in population, phylogeographic and phylogenetic studies: The effects of inherited symbionts. Proc. R. Soc. B Biol. Sci. 272, 1525–1534 (2005).CAS 
Article 

Google Scholar 
Hill, W. G. & Robertson, A. The effect of linkage on limits to artificial selection. Genet. Res. (Camb.) 89, 311–336 (2008).Article 

Google Scholar 
Valenzuela, N. Multiple paternity in side-neck turtles Podocnemis expansa: Evidence from microsatellite DNA data. Mol. Ecol. 9, 99–105 (2000).CAS 
PubMed 
Article 

Google Scholar 
Moritz, C. Defining ‘Evolutionarily Significant Units’ for conservation. Trends Ecol. Evol. 9, 373–375 (1994).CAS 
PubMed 
Article 

Google Scholar 
Volkmann, L., Martyn, I., Moulton, V., Spillner, A. & Mooers, A. O. Prioritizing populations for conservation using phylogenetic networks. PLoS ONE 9, 1–10 (2014). https://doi.org/10.1371/journal.pone.0088945Article 
CAS 

Google Scholar 
García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).Article 

Google Scholar 
Frankham, R. Genetic rescue of small inbred populations: Meta-analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).PubMed 
Article 

Google Scholar 
Teixeira, J. C. & Huber, C. D. The inflated significance of neutral genetic diversity in conservation genetics. Proc. Natl. Acad. Sci. U. S. A. 118, 1–10 (2021). https://doi.org/10.1073/pnas.2015096118Araki, H., Cooper, B. & Blouin, M. S. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science (80-) 318, 100–103 (2007).CAS 
Article 
ADS 

Google Scholar  More