Philippa Kaur

Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).Article 
CAS 
PubMed 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).Article 
CAS 
PubMed 

Google Scholar 
Fonseca, C. R. et al. Conservation biology: four decades of problem- and solution-based research. Perspect. Ecol. Conserv. 19, 121–130 (2021).
Google Scholar 
Smits, P. & Finnegan, S. How predictable is extinction? Forecasting species survival at million-year timescales. Philos. Trans. R. Soc. B Biol. Sci. 374, 20190392 (2019).Article 

Google Scholar 
Hanski, I. & Ovaskainen, O. Extinction debt at extinction threshold. Conserv. Biol. 16, 666–673 (2002).Article 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
PubMed 

Google Scholar 
Ridding, L. E. et al. Inconsistent detection of extinction debts using different methods. Ecography 44, 33–43 (2021).Article 

Google Scholar 
Berglund, H. & Jonsson, B. G. Verifying an extinction debt among lichens and fungi in northern Swedish boreal forests. Conserv. Biol. 19, 338–348 (2005).Article 

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).Article 

Google Scholar 
Triantis, K. et al. Extinction debt on oceanic islands. Ecography 33, 285–294 (2010).
Google Scholar 
Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian Amazon. Science 337, 228–232 (2012).Article 
CAS 
PubMed 

Google Scholar 
Pan, Y. et al. Spatial and temporal scales of landscape structure affect the biodiversity-landscape relationship across ecologically distinct species groups. Landsc. Ecol. 37, 2311–2325 (2022).Article 

Google Scholar 
Soga, M. & Koike, S. Mapping the potential extinction debt of butterflies in a modern city: Implications for conservation priorities in urban landscapes. Anim. Conserv. 16, 1–11 (2013).Article 

Google Scholar 
Knapp, S., Winter, M. & Klotz, S. Increasing species richness but decreasing phylogenetic richness and divergence over a 320-year period of urbanization. J. Appl. Ecol. 54, 1152–1160 (2017).Article 

Google Scholar 
McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the anthropocene. Trends Ecol. Evol. 30, 104–113 (2015).Article 
PubMed 

Google Scholar 
Chen, Y. & Peng, S. Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals. Sci. Rep. 7, 1–10 (2017).
Google Scholar 
Krauss, J. et al. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecol. Lett. 13, 597–605 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Cowlishaw, G. Predicting the pattern of decline of African primate diversity: An extinction debt from historical deforestation. Conserv. Biol. 13, 1183–1193 (1999).Article 

Google Scholar 
Figueiredo, L., Krauss, J., Steffan-Dewenter, I. & Sarmento Cabral, J. Understanding extinction debts: spatio–temporal scales, mechanisms and a roadmap for future research. Ecography 42, 1973–1990 (2019).Article 

Google Scholar 
Aerts, R. & Honnay, O. Forest restoration, biodiversity and ecosystem functioning. BMC Ecol. 11, 1–21 (2011).Article 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 
PubMed 
PubMed Central 

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

Google Scholar 
IUCN. The IUCN Red List of Threatened Species, Version 2019-1. https://www.iucnredlist.org. Downloaded on 23 February 2022. (2019).Brown, J. L. et al. Spatial biodiversity patterns of Madagascar’s amphibians and reptiles. PLoS ONE 11, e0144076 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Powney, G. D., Grenyer, R., Orme, C. D. L., Owens, I. P. F. & Meiri, S. Hot, dry and different: Australian lizard richness is unlike that of mammals, amphibians and birds. Glob. Ecol. Biogeogr. 19, 386–396 (2010).Article 

Google Scholar 
Pianka, E. R. Desert lizard diversity: additional comments and some data. Am. Nat. 134, 344–364 (1989).Article 

Google Scholar 
Chen, Y. H. Combining the species-area-habitat relationship and environmental cluster analysis to set conservation priorities: A study in the Zhoushan Archipelago, China. Conserv. Biol. 23, 537–545 (2009).Article 
PubMed 

Google Scholar 
Ricklefs, R. E. & Lovette, I. J. The roles of island area per se and habitat diversity in the species-area relationships of four Lesser Antillean faunal groups. J. Anim. Ecol. 68, 1142–1160 (1999).Article 

Google Scholar 
Souza, F. L., Martins, F. I. & Raizer, J. Habitat heterogeneity and anuran community of an agroecosystem in the Pantanal of Brazil. Phyllomedusa 13, 41–50 (2014).Article 

Google Scholar 
Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).Article 
CAS 
PubMed 

Google Scholar 
McNab, B. K. Bioenergetics and the determination of home range size. Am. Nat. 97, 133–140 (1963).Article 

Google Scholar 
Powell, R. A. & Mitchell, M. S. What is a home range? J. Mammal. 93, 948–958 (2012).Article 

Google Scholar 
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Giam, X. et al. Reservoirs of richness: least disturbed tropical forests are centres of undescribed species diversity. Proc. R. Soc. B 279, 67–76 (2012).Article 
PubMed 

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

Google Scholar 
Li, H. et al. Large numbers of vertebrates began rapid population decline in the late 19th century. Proc. Natl Acad. Sci. USA 113, 14079–14084 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pringle, R. M. Upgrading protected areas to conserve wild biodiversity. Nature 546, 91–99 (2017).Article 
CAS 
PubMed 

Google Scholar 
Forzieri, G., Dakos, V., McDowell, N. G., Ramdane, A. & Cescatti, A. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Diamond, J. M. Biogeographic kinetics: estimation of relaxation times for Avifaunas of southwest Pacific islands. Proc. Natl Acad. Sci. USA 69, 3199–3203 (1972).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).Article 
PubMed 

Google Scholar 
Foley, J. A. et al. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front. Ecol. Environ. 5, 25–32 (2007).Article 

Google Scholar 
Asamoah, E. F., Beaumont, L. J. & Maina, J. M. Climate and land-use changes reduce the benefits of terrestrial protected areas. Nat. Clim. Chang. 11, 1105–1110 (2021).Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).Article 

Google Scholar 
Peng, S. et al. Sensitivity of land use change emission estimates to historical land use and land cover mapping. Glob. Biogeochem. Cycles 31, 626–643 (2017).Article 
CAS 

Google Scholar 
Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Chang. Biol. 19, 2893–2906 (2013).Article 
PubMed 

Google Scholar 
Poulter, B. et al. Plant functional type classification for earth system models: results from the European Space Agency’s Land Cover Climate Change Initiative. Geosci. Model Dev. 8, 2315–2328 (2015).Article 

Google Scholar 
Pongratz, J., Reick, C., Raddatz, T. & Claussen, M. A reconstruction of global agricultural areas and land cover for the last millennium. Global Biogeochem. Cycles 22, (2008).Dietz, F. C. The industrial revolution. In the Hands of a Child (1970).Gütschow, J., Jeffery, L. & Gieseke, R. The PRIMAP-hist national historical emissions time series (1850-2016). V. 2.0. GFZ Data Services (2019).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed 9 January 2022); www.protectedplanet.net.Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing Version 4.0.2 (2020). More

Whale entanglements in fishing gear threaten whale populations, seafood production and long-term sustainability of commercial fisheries. While multiple mitigation strategies to reduce entanglements exist, there has been minimal consideration of the economic impact of these strategies. Here, we estimated retrospective losses to ex-vessel revenues for one of California’s most lucrative fisheries. Overall, we found fishery closures decreased ex-vessel revenue, with results showing some uncertainty due to large model prediction error. Regional differences in losses revealed interesting trends in the capacity for the fishery to recoup costs. For example, in the NMA, relatively small losses at the fishery level were predicted ($0.3 million in total) for the 2019 season despite an early closure to the season due to whale entanglement risk.NMA fishers collectively were able to meet predicted revenue for the season despite a shortening of the fishing 2019 season. In the 2020 season however, the NMA did not experience disturbances due to whale entanglements but larger ex-vessel losses (of $3.9 million) were predicted. This suggests that other disturbances such as a delay to the season due to crab meat quality, lost fishing opportunity related to the COVID-19 pandemic, or other unknown factors, had an influence on ex-vessel revenue during the 2020 season. While most of the 2020 season landings in the NMA occurred before COVID-19 arrived in the US, there is evidence that prices in latter part of the season may have been depressed due to loss of export markets for live crab47.In the CMA however, despite landing the majority of crab available during the 2019 season (see Fig. 2c), losses of $9.4 million were experienced across the fishery. While total fishery catch was not greatly reduced, closure to the fishery in the spring may be responsible for revenue losses through other mechanisms (e.g. price). In the 2020 season, whale entanglement risk substantially shortened the fishing season in the CMA, through a delay at the beginning of the season and an early closure in the spring. Estimated losses were largest ($14.4 million) during this season. It is likely that the COVID-19 pandemic was also responsible for some of this estimated loss in the CMA in the 2020 season47. Our model did not control for impacts of the COVID-19 pandemic. However, price trends suggest that that price of Dungeness Crab in California was not affected until mid-March 2020, at which point the fishery had caught 92% of the seasons catch (see Supplementary File S2). Prices then returned to normal levels in mid-May. If we apply extrapolated prices between mid-March and mid-May by replacing observed prices with linearly increasing prices by week, revenues would have been $753,754 higher in total across the fishery. This rough estimate suggests we can attribute 4.1% of overall estimated revenue losses during the 2020 season to COVID-19 impacts, with the caveat that we do not know what prices would have been in the absence of the pandemic. A counterfactual approach has been used to disentangle multiple stressors to infer causal impacts of management interventions elsewhere48, however as these closures, and the COVID pandemic, potentially impacted all fishers in the California Dungeness crab fishery, there are no control groups available for comparison and therefore this approach would not be appropriate.Closures and other disturbances appear to have been less impactful in the NMA and high price for Dungeness crab may have contributed to the ability of vessels operating in the NMA to withstand disturbances (Supplementary Fig. S2). Prices were particularly high during the summer portion of the season in 2020 during which time the CMA was closed to Dungeness crab fishing (Supplementary Fig. S2). The NMA did not experience closures due to whale entanglement during 2020 and was predicted to have lower than average pre-season abundance (lower catch potential) during 2020 (see Fig. 2.b), while the CMA was predicted to have high catch potential for 2020 (Fig. 2.c), therefore differences in management measures implemented, and seasons’ catch potential, also contributed to differences in losses estimated.The CMA also experienced high prices, including decadal high prices for crab during the November–December of the 2019 fishing season (Supplementary Fig. S2). However, losses observed overall across the two seasons suggest the fishery, unlike the NMA, did not get much overall benefit from the high price in 2019 or the high pre-season abundance of crab (i.e. catch potential) estimated for the 2020 season in the CMA. A number of factors may have contributed to a poor season in the CMA including catchability or biology of Dungeness crab as well as external factors such as the COVID-19 pandemic behavioral choice factors, for example deciding not to fish45. Temporally shifting or reducing the opportunity for participation through closed periods due to whale entanglement risk may have exacerbated other impacts on revenues in the CMA which were not as impactful on revenues in the NMA.The high variability in estimated economic impacts per vessel reported here demonstrates that closures did not affect all vessels equally, similarly to impacts observed following a climate related harmful algal bloom in the 2016 season which were variable by vessel size and between communities45. The estimated losses we present at the fishery level in the NMA and CMA may therefore be underestimated, or overestimated, for particular groups of vessels within those management areas. This reflects the diverse nature of the Dungeness Crab fishery in behaviour and fishing strategy and highlights the importance of capturing impacts at finer scales than the fishery level alone.Limitations to the estimation of closure impactsA limitation of the hurdle model is that there are other latent factors influencing fishery participation and revenues that our model does not incorporate, particularly those determining fisher behavior such as fuel price, shipyard backlogs and market demand. A behavioral choice model, for example one that incorporates location or fishing alternative choice given a closure50,51,52 would be a potential method to better understand how spatial management strategies affect fisher behavior and is recommended as a future analysis to assess trade-offs involving socio-economic risk. Our results, reporting losses from Dungeness crab fishing revenue only, also do not account for the ability of some fishers to mitigate revenue losses by participating in other fisheries. Dungeness crab fishing is highly connected within west coast fishery participation networks44,45. Thus, it is important to note that our results for the 2019 and 2020 seasons present only losses from Dungeness crab fishing and may overestimate total annual revenue losses by some vessels that are able to mitigate impacts with participation in other fisheries.The model, predicting out-of-sample, over-estimated revenues in recent years suggesting that our predictions of revenues may also be over-predicted. An improved estimation at the vessel level, given some over-estimation of vessels that did not fish, could be investigated through a selection model approach rather than a two-part model approach54. However, two-part models are most appropriate for estimation of conditional (actual) outcomes as was intended here rather than unconditional (potential) outcomes and they do not require separate drivers for the selection and estimation model, which we did not have available54. When the impacts of policy interventions are difficult to disentangle from other impacts, approaches such as a counterfactual synthetic control48 approach could be used to separate the impacts of the policy alone. In this context, however, it is useful to report the cumulative impact of disturbances given that these disturbances (e.g., delays due to crab quality, harmful algal blooms) happen frequently and therefore the closures will rarely happen in isolation.Whilst there are limitations to our approach, revenue predictions presented here offer more insight compared to predicting revenues based only on a 5-year average of total fishery revenues (Supplementary Table S3) as is commonly conducted to calculate disaster assistance requirement, as our analysis includes an estimation of crab abundance as well as historical vessel level data in its estimation. Accounting for the influence of crab abundance is critical in this fishery given abundance is highly variable and the majority of fishable biomass is taken each year. Estimation of revenue at the individual vessel level allows for consideration of fishery heterogeneity (e.g., by vessel size). Revenues calculated on a 5-year average would suggest total California Commercial Dungeness crab fishery revenues would have been $10.62 million higher than observed in 2019 and $12.73 million higher than observed in 2020 (Supplementary Table S3). Thus, revenues estimated on the 5-year average suggest that losses would have been $0.97 million higher than our model prediction across the fishery for 2019 and $5.56 million lower than our model prediction for 2020. Our predictions suggest that delays and closures due to whale entanglement mitigation and other disturbances in to the 2019 and 2020 seasons were similar to the impact of closures due to the HAB in the 2016 season, which were estimated at $13.6 million in losses from Dungeness Crab revenues across the fishery38.Economic cost of mitigationMany strategies that prevent fishery interactions with marine mammals exist, including gear reductions or modifications, depth limitations and dynamic or seasonal time-area closures13,14,22,23,24,25,26,55. Whilst the fishery does implement pro-active gear modification measures set out in the best practices guide34, only two management intervention options were enacted in the 2019 and 2020 seasons to mitigate against entanglements of marine life with Dungeness crab gear; delays to the start of the crab season in the winter and early closures in spring due to overlap with whale distribution in fishing grounds. These delays and closures can have differential impacts on the fishery as the fishing season is not heterogeneously prosperous. An example is that closures during the holiday season (Nov–Dec) when Dungeness crab is traditionally consumed can cause substantial lost revenue opportunity for fishers at a time when price and demand are highest35,49. The fishery operates as a derby in which the majority of revenues are made in the first month of the fishery being open. The strong seasonal dynamics of the Dungeness crab fishery, largely driven by rapid depletion of legal sized crab, mean that the timing of management actions can have important impacts on fishing revenues. Across the fishery, based on observed vessel level revenues during the 2011–2018 baseline period, vessels earned an average of 62.33% (SD 24.04) of annual ex-vessel revenue during the first month of the season (15th Nov–15th Dec for the CMA/1st Dec–31st Dec for the NMA). After April 1st, vessels on average earn 10.54% (SD 18.98) of annual ex-vessel revenue. This average, based only on vessels that historically have actively participate past April 1st, (283 vessels in the NMA, 346 vessels in the CMA) rises to 20.36% (SD 13.37) of ex-vessel revenue. Thus, while the majority of the overall fisheries revenue is taken at the start of the season, an April 1st closure could still have a substantial impact on the revenues of active fishing vessels in the spring. Determination of economic risk for the fishery, at a minimum, should consider timing of closures in addition to total revenue losses, in order to quantify losses that will be felt at the individual vessel level. We suggest further research to investigate how closures affect different groups of fishers through stakeholder participation.Socio-economic impacts from whale mitigation measures could permeate into communities further than our analysis (based on ex-vessel revenue only) conveys35,36,37,49, and further investigation into these community level impacts is necessary to understand and sustain an equitable fishery supply chain even where there is no absolute revenue loss. Some of the communities influenced by whale entanglement mitigation in California rely heavily on ocean resources for employment, through fishing occupations but also through hospitality and tourism. Managing this issue in a way that minimizes the burden on resource dependent communities is strongly in line with the objectives set out in the UN Sustainable Development Goals (SDG’s), especially SDG 14 (life below water) but also related goals such as human well-being, reducing inequality and reducing the impacts of climate change56.Management ImplicationsBalancing socio-economic impacts against whale entanglement risk is challenging given the legally protected status of whale populations. However, potential economic losses reported here should motivate the development of mitigation measures (through cooperative innovation between industry, researchers and managers) that allow fishery production to be optimized whilst ensuring successful whale protection. At present, entire management areas, which constitute large regions of the coast, are closed in response to whale entanglement risk in California. Investigating how to minimize the spatiotemporal footprint of closures, such as by defining high risk zones dynamically based on fine-scale information of whale density and fishing effort, could provide an alternative mitigation structure. This could better consider the economic and conservation trade-offs while still being sensitive to changing environmental conditions. The introduction of dynamic zone closures, often broadly referred to as dynamic ocean management, has been demonstrated to reduce risk whilst minimizing lost fishing opportunities12,26,57,58, especially when environmental variability is high or species have a dynamic distribution59. Moreover, analysis of policy instruments to reduce whale entanglements with the American lobster fishery on the US Northeast coast found that economic costs of risk reduction could be 20% lower when mitigation decisions considered fishing opportunity costs alongside non-monetary benefits (biological risk), compared to non-monetary benefits alone12. This is promising for the implementation of such strategies in the California Current System.The caveat of this strategy is that dynamic zone closures require spatially and temporally explicit information on whale density and fishing effort which can be costly to attain. The use of ropeless gear has also been suggested as an alternative whale entanglement mitigation measure that requires further research and development before being initiated as an alternative regulatory tool60. The costs of monitoring or technical advancements however may outweigh the financial and societal cost of fishery closures. Revenue losses for Dungeness crab estimated here for the 2019 and 2020 seasons are on par with losses experienced during the HAB period. During the delays to the 2016 fishing season an estimated $26.1 million was lost from ex-vessel revenues from all species that crab fishers target, including $13.6 million from Dungeness crab alone38, requiring $25 million in government aid. Whale mitigation under the RAMP regulation will potentially delay or close the fishery year after year with uncertain economic impact that cannot be sustainably resolved with government aid. Development of tools to mitigate against economic loss while achieving whale protection will be necessary to come to a sustainable solution. This can only be achieved by first including economic loss in risk assessments. Doing so may also provide balance to partnerships between fishery managers and fishers.Regulators are obligated to protect Humpback whales, blue whales and Leatherback turtles using the best available science33. In this fishery, current triggers to open and close are based on a range of factors, but thus ultimately depend on the number of whales present within a management region33. Regulators have a number of alternative regulatory options available to them, which include depth restrictions, gear restrictions or modifications and fleet advisories, if they can offer the same level of whale protection33. Yet, the RAMP process lacks the socio-economic information needed to consider the socio-economic risk of regulatory actions, and that of the alternatives, to the fishing community. Results presented here highlight that the economic effects and that risk to fishing communities should be considered when designing whale entanglement mitigation programs33. Having this economic information will facilitate the ability of managers, as set out in the RAMP regulation (subsection d4)33, to consider the socio-economic impact if deciding between management measures that equivalently reduce entanglement risk.We have used two fishing seasons as an example of the economic impacts of these new whale entanglement regulations which will be implemented each year going forward. Synthesis of ex-vessel revenues is not a complete picture of the socio-economic impacts of regulations, but it provides a starting point for protecting both whales and fishing communities. While reported whale entanglements remain higher than pre-2014 totals, reported whale entanglements in California have declined markedly in the years following the 2014–2016 large marine heatwave (Fig. 1b). This is a success for this fishery and attributed to increased awareness, development of best practices for fishing gear and the mitigation program to protect whales. We now need to be successful at protecting and mitigating the socio-economic impacts to fishery participants and the fishing communities they support. More

Jackson, R. B. et al. The Ecology of soil carbon: Pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445. https://doi.org/10.1146/annurev-ecolsys-112414-054234 (2017).Article 

Google Scholar 
Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. PNAS 114, 9575–9580 (2017).Article 
ADS 
CAS 

Google Scholar 
Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427. https://doi.org/10.1038/s41467-020-18887-7 (2020).Article 
ADS 
CAS 

Google Scholar 
Hopkins, A. & Holz, B. Grassland for agriculture and nature conservation: Production, quality and multi-functionality. Agron 4, 3–20 (2006).
Google Scholar 
van Veen, J. A., Liljeroth, E., Lekkerkerk, L. J. A. & van de Geijn, S. C. Carbon fluxes in plant-soil systems at elevated atmospheric CO2 levels. Ecol. Appl. 1, 175–181. https://doi.org/10.2307/1941810 (1991).Article 

Google Scholar 
Jones, M. B. & Donnelly, A. Carbon sequestration in temperate grassland ecosystems and the influence of management, climate and elevated CO2. New Phytol. 164, 423–439. https://doi.org/10.1111/j.1469-8137.2004.01201.x (2004).Article 

Google Scholar 
Ward, S. E. et al. Legacy effects of grassland management on soil carbon to depth. Glob. Change Biol. 22, 2929–2938. https://doi.org/10.1111/gcb.13246 (2016).Article 
ADS 

Google Scholar 
Hungate, B. A. et al. The fate of carbon in grasslands under carbon dioxide enrichment. Nature 388, 576–579. https://doi.org/10.1038/41550 (1997).Article 
ADS 
CAS 

Google Scholar 
Six, J., Conant, R. T., Paul, E. A. & Paustian, K. Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils. Plant Soil 241, 155–176. https://doi.org/10.1023/A:1016125726789 (2002).Article 
CAS 

Google Scholar 
Chang, J. et al. Climate warming from managed grasslands cancels the cooling effect of carbon sinks in sparsely grazed and natural grasslands. Nat. Commun. 12, 118. https://doi.org/10.1038/s41467-020-20406-7 (2021).Article 
ADS 
CAS 

Google Scholar 
IPCC. 2001. Climate change 2001: The scientific basis contribution of working group 1 to the third assessment report of the intergovernmental panel on climate change In (eds Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., Van Der Linden, P. J., Dai, X., Maskell, K. & Johnson, C. A.) (Cambridge University Press).Gwin, L. Scaling-up sustainable livestock production: Innovation and challenges for grass-fed beef in the U.S. J. Sustain. Agric. 33, 189–209. https://doi.org/10.1080/10440040802660095 (2009).Article 

Google Scholar 
Iqbal, J., Siegrist, J. A., Nelson, J. A. & McCulley, R. L. Fungal endophyte infection increases carbon sequestration potential of southeastern USA tall fescue stands. Soil Biol. Biochem. 44, 81–92. https://doi.org/10.1016/j.soilbio.2011.09.010 (2012).Article 
CAS 

Google Scholar 
Robinson, R. A. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. J. Appl. Ecol. 39, 157–176. https://doi.org/10.1046/j.1365-2664.2002.00695.x (2002).Article 

Google Scholar 
Law, Q. D., Bigelow, C. A. & Patton, A. J. Selecting turfgrasses and mowing practices that reduce mowing requirements. Crop Sci. 56, 3318–3327. https://doi.org/10.2135/cropsci2015.09.0595 (2016).Article 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. Rangel Ecol. Manag. 26(1), 13–18 (1973).Article 
CAS 

Google Scholar 
Virkajarvi, P. Effects of defoliation height on regrowth of timothy and meadow fescue in the generative and vegetative phases of growth. Agric. Food Sci. 12, 177–193 (2003).Article 

Google Scholar 
Reicher, Z., Patton, A. J., Bigelow, C. A. & Voigt, T. Mowing, Thatching, Aerifying, and Rolling Turf (Turf Grass Sci. Purdue Univ, 2006).
Google Scholar 
Kaatz, P. Cutting management for cool-season forage grasses. Michigan State University Extension, https://www.canr.msu.edu/news/cutting_management_for_cool_season_forage_grasses (2011).Briske, D. D. Strategies of plant survival in grazed systems: A functional interpretation. Ecol. Manag. Graz. Syst. 37–67 (1996).Crider, F. J. Root-growth stoppage resulting from defoliation of grass (No. 156759). United States Department of Agriculture, Economic Research Service (1995).Lal, R., Negassa, W. & Lorenz, K. Carbon sequestration in soil. Curr. Opin. Environ. Sustain. 15, 79–86. https://doi.org/10.1016/j.cosust.2015.09.002 (2015).Article 

Google Scholar 
Coughenour, M. B., McNaughton, S. J. & Wallace, L. L. Modelling primary production of perennial graminoids – uniting physiological processes and morphometric traits. Ecol. Modell. 23, 101–134. https://doi.org/10.1016/0304-3800(84)90121-2 (1984).Article 
CAS 

Google Scholar 
Whipps, J. M. & Lynch, J. M. Energy losses by the plant in rhizodeposition. Plant products and the new technology / edited by K.W. Fuller and J.R. Gallon (1985).Johansson, G. Release of organic C from growing roots of meadow fescue (Festuca pratensis L.). Soil Biol. Biochem. 24, 427–433. https://doi.org/10.1016/0038-0717(92)90205-C (1992).Article 

Google Scholar 
Woodburn, A. T. Glyphosate: Production, pricing and use worldwide. Pest Manag. Sci. 56, 309–312. https://doi.org/10.1002/(SICI)1526-4998(200004)56:4%3c309::AID-PS143%3e3.0.CO;2-C (2000).Article 
CAS 

Google Scholar 
Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325. https://doi.org/10.1002/ps.1518 (2008).Article 
CAS 

Google Scholar 
Helander, M., Saloniemi, I. & Saikkonen, K. Glyphosate in northern ecosystems. Trends Plant Sci. 17, 569–574. https://doi.org/10.1016/j.tplants.2012.05.008 (2012).Article 
CAS 

Google Scholar 
Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3. https://doi.org/10.1186/s12302-016-0070-0 (2016).Article 
CAS 

Google Scholar 
Helander, M. et al. Glyphosate decreases mycorrhizal colonization and affects plant-soil feedback. Sci. Total Environ. 642, 285–291. https://doi.org/10.1016/j.scitotenv.2018.05.377 (2018).Article 
ADS 
CAS 

Google Scholar 
Helander, M., Pauna, A., Saikkonen, K. & Saloniemi, I. Glyphosate residues in soil affect crop plant germination and growth. Sci. Rep. 9, 19653. https://doi.org/10.1038/s41598-019-56195-3 (2019).Article 
ADS 
CAS 

Google Scholar 
Zaller, J. G. & Brühl, C. A. Editorial: Non-target effects of pesticides on organisms inhabiting agroecosystems. Front Environ. Sci. 7, 75. https://doi.org/10.3389/fenvs.2019.00075 (2019).Article 

Google Scholar 
Muola, A. et al. Risk in the circular food economy: Glyphosate-based herbicide residues in manure fertilizers decrease crop yield. Sci. Total Environ. 750, 141422. https://doi.org/10.1016/j.scitotenv.2020.141422 (2021).Article 
ADS 
CAS 

Google Scholar 
Fuchs, B., Saikkonen, K. & Helander, M. Glyphosate-modulated biosynthesis driving plant defense and species interactions. Trends Plant Sci. 26, 312–323. https://doi.org/10.1016/j.tplants.2020.11.004 (2021).Article 
CAS 

Google Scholar 
Fuchs, B. et al. A Glyphosate-based herbicide in soil differentially affects hormonal homeostasis and performance of non-target crop plants. Front Plant Sci. 12, 787958 (2022).Article 

Google Scholar 
Borggaard, O. K. & Gimsing, A. L. Fate of glyphosate in soil and the possibility of leaching to ground and surface waters: A review. Pest Manag. Sci. 64, 441–456. https://doi.org/10.1002/ps.1512 (2008).Article 
CAS 

Google Scholar 
Rueppel, M. L., Brightwell, B. B., Schaefer, J. & Marvel, J. T. Metabolism and degradation of glyphosate in soil and water. J. Agric. Food Chem. 25, 517–528. https://doi.org/10.1021/jf60211a018 (1977).Article 
CAS 

Google Scholar 
Carlisle, S. M. & Trevors, J. T. Glyphosate in the environment. Wat Air Soil Poll 39, 409–420 (1988).Article 
ADS 
CAS 

Google Scholar 
Torstensson, N. T. L., Lundgren, L. N. & Stenström, J. Influence of climatic and edaphic factors on persistence of glyphosate and 2,4-D in forest soils. Ecotoxicol. Environ. Saf. 18, 230–239. https://doi.org/10.1016/0147-6513(89)90084-5 (1989).Article 
CAS 

Google Scholar 
Stenrød, M., Eklo, O. M., Charnay, M.-P. & Benoit, P. Effect of freezing and thawing on microbial activity and glyphosate degradation in two Norwegian soils. Pest Manag. Sci. 61, 887–898. https://doi.org/10.1002/ps.1107 (2005).Article 
CAS 

Google Scholar 
Antier, C. et al. Glyphosate use in the European agricultural sector and a framework for its further monitoring. Sustainability 12, 5682. https://doi.org/10.3390/su12145682 (2020).Article 
CAS 

Google Scholar 
Jones, R. J. Effect of an associate grass, cutting interval, and cutting height on yield and botanical composition of Siratro pastures in a sub-tropical environment. Aust. J. Exp. Agric. 14, 334–342. https://doi.org/10.1071/ea9740334 (1974).Article 

Google Scholar 
Volenec, J. J. & Nelson, C. J. Responses of Tall Fescue leaf meristems to N fertilization and harvest frequency. Crop Sci. 23(4), 720–724. https://doi.org/10.2135/cropsci1983.0011183X002300040028x (1983).Article 

Google Scholar 
Saikkonen, K. et al. Fungal endophytes help prevent weed invasions. Agric. Ecosyst. Environ. 165, 1–5. https://doi.org/10.1016/j.agee.2012.12.002 (2013).Article 

Google Scholar 
Scavo, A. & Mauromicale, G. Integrated weed management in herbaceous field crops. Agronomy 10, 466. https://doi.org/10.3390/agronomy10040466 (2020).Article 

Google Scholar 
Clay, K. & Holah, J. Fungal endophyte symbiosis and plant diversity in successional fields. Science 285, 1742–1744. https://doi.org/10.1126/science.285.5434.1742 (1999).Article 
CAS 

Google Scholar 
Gundel, P. E., Pérez, L. I., Helander, M. & Saikkonen, K. Symbiotically modified organisms: Nontoxic fungal endophytes in grasses. Trends Plant Sci. 18, 420–427. https://doi.org/10.1016/j.tplants.2013.03.003 (2013).Article 
CAS 

Google Scholar 
Kauppinen, M., Saikkonen, K., Helander, M., Pirttilä, A. M. & Wäli, P. R. Epichloë grass endophytes in sustainable agriculture. Nat. Plants 2, 15224 (2016).Article 

Google Scholar 
Clay, K. Fungal endophytes of grasses. Annu. Rev. Ecol. Syst. 21, 275–297 (1990).Article 

Google Scholar 
Saikkonen, K., Young, C. A., Helander, M. & Schardl, C. L. Endophytic Epichloë species and their grass hosts: From evolution to applications. Plant Mol. Biol. 90, 665–675. https://doi.org/10.1007/s11103-015-0399-6 (2016).Article 
CAS 

Google Scholar 
Ahlholm, J. U., Helander, M., Lehtimäki, S., Wäli, P. & Saikkonen, K. Vertically transmitted fungal endophytes: Different responses of host-parasite systems to environmental conditions. Oikos 99, 173–183. https://doi.org/10.1034/j.1600-0706.2002.990118.x (2002).Article 

Google Scholar 
Easton, H. S. & Fletcher, L. R. in Proc. 6th International Symposium Fungal Endophytes of Grasses (eds Popay, A. J. & Thom, E. R.) 11–18 (New Zealand Grassland Association, 2007).Saari, S., Lehtonen, P., Helander, M. & Saikkonen, K. High variation in frequency of infection by endophytes in cultivars of meadow fescue in Finland. Grass Forage Sci. 64, 169–176. https://doi.org/10.1111/j.1365-2494.2009.00680.x (2009).Article 

Google Scholar 
König, J., Fuchs, B., Krischke, M., Mueller, M. J. & Krauss, J. Hide and seek: Infection rates and alkaloid concentrations of Epichloë festucae var. lolii in Lolium perenne along a land-use gradient in Germany. Grass Forage Sci. 73, 510–516. https://doi.org/10.1111/gfs.12330 (2018).Article 
CAS 

Google Scholar 
Krauss, J. et al. Epichloë endophyte infection rates and alkaloid content in commercially available grass seed mixtures in Europe. Microorganisms 8, 498. https://doi.org/10.3390/microorganisms8040498 (2020).Article 
CAS 

Google Scholar 
Brink, G. E., Casler, M. D. & Martin, N. P. Meadow Fescue, Tall Fescue, and Orchardgrass response to defoliation management. Agronomy J 102, 667–674. https://doi.org/10.2134/agronj2009.0376 (2010).Article 

Google Scholar 
Conant, R. T., Cerri, C. E. P., Osborne, B. B. & Paustian, K. Grassland management impacts on soil carbon stocks: A new synthesis. Ecol. Appl. 27, 662–668. https://doi.org/10.1002/eap.1473 (2017).Article 

Google Scholar 
Trlica, M. J. Distribution and utilization of carbohydrate reserves in range plants. In (ed Sosebee, R. E.) 73–96 (Rangeland Plant Physiology, 1977).Faeth, S. H. & Sullivan, T. J. Mutualistic asexual endophytes in a native grass are usually parasitic. Am. Nat. 161, 310–325. https://doi.org/10.1086/345937 (2003).Article 

Google Scholar 
Saikkonen, K., Saari, S. & Helander, M. Defensive mutualism between plants and endophytic fungi?. Fungal Divers. 41, 101–113. https://doi.org/10.1007/s13225-010-0023-7 (2010).Article 

Google Scholar 
Clay, K. & Schardl, C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am. Nat. 160, 99–127. https://doi.org/10.1086/342161 (2002).Article 

Google Scholar 
Rozpądek, P. et al. The fungal endophyte Epichloë typhina improves photosynthesis efficiency of its host orchard grass (Dactylis glomerata). Planta 242, 1025–1035. https://doi.org/10.1007/s00425-015-2337-x (2015).Article 
CAS 

Google Scholar 
Xia, C. et al. An Epichloë endophyte improves photosynthetic ability and dry matter production of its host Achnatherum inebrians infected by Blumeria graminis under various soil water conditions. Fungal Ecol. 22, 26–34. https://doi.org/10.1016/j.funeco.2016.04.002 (2016).Article 

Google Scholar 
Malinowski, D., Leuchtmann, A., Schmidt, D. & Nosberger, J. Symbiosis with Neotyphodium uncinatum endophyte may increase the competitive ability of meadow fescue. Agron. J. 89, 833–839 (1997).Article 

Google Scholar 
Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Ann. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).Article 
CAS 

Google Scholar 
Chen, Z. et al. Fungal endophyte improves survival of Lolium perenne in low fertility soils by increasing root growth, metabolic activity and absorption of nutrients. Plant Soil 452, 185–206. https://doi.org/10.1007/s11104-020-04556-7 (2020).Article 
CAS 

Google Scholar 
Franz, J. E., Mao, M.K. and Sikorski, J.A. (1997). Uptake, transport and metabolism of glyphosate in plants, in Glyphosate: A unique global herbicide, ed by Franz JE, ACS Monograph No 189, American Chemical Society, Washington, DC, pp 143–181.Pline, W. A., Wilcut, J. W., Edmisten, K. L. & Wells, R. Physiological and morphological response of glyphosate-resistant and non-glyphosate-resistant cotton seedlings to root-absorbed glyphosate. Pestic. Biochem. Phys. 73, 48–58. https://doi.org/10.1016/S0048-3575(02)00014-7 (2002).Article 
CAS 

Google Scholar 
Johansson, G. Carbon distribution in grass (Festuca pratensis L.) during regrowth after cutting—utilization of stored and newly assimilated carbon. Plant Soil 151, 11–20. https://doi.org/10.1007/BF00010781 (1993).Article 
ADS 
CAS 

Google Scholar 
Ergon, Å. et al. How can forage production in Nordic and Mediterranean Europe adapt to the challenges and opportunities arising from climate change?. Euro J. Agron. 92, 97–106. https://doi.org/10.1016/j.eja.2017.09.016 (2018).Article 

Google Scholar 
Niemelainen, O. et al. Increase in perennial forage yields driven by climate change, at Apukka Research Station, Rovaniemi, 1980–2017. Agric. Food Sci. 29, 139–153 (2020).Article 

Google Scholar 
Anwar, M. R., Liu, D. L., Macadam, I. & Kelly, G. Adapting agriculture to climate change: A review. Theor. Appl. Climatol. 113, 225–245. https://doi.org/10.1007/s00704-012-0780-1 (2013).Article 
ADS 

Google Scholar 
Farmit. Nurmea yli kymppitonni hehtaarilta. Farmit.net. (accessed 28 June 2022); https://www.farmit.net/nurmikasvit-lypsylehma/2016/05/24/nurmea-yli-kymppitonni-hehtaarilta (2016).Peltonen, S., Aalto, K., Hennola, I. & Anttila, S. (Eds.). Peltojen kunnostus. (Tieto Tuottamaan; No. 145), (ProAgria Keskusten Liiton julkaisuja; No. 1163). ProAgria maaseutukeskusten liitto (2019).Laihonen, M., Saikkonen, K., Helander, M. & Tammaru, T. Insect oviposition preference between Epichloë-symbiotic and Epichloë-free grasses does not necessarily reflect larval performance. Ecol. Evol. 10, 7242–7249. https://doi.org/10.1002/ece3.6450 (2020).Article 

Google Scholar  More

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

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

Google Scholar 
Magurran, A. E. et al. Divergent biodiversity change within ecosystems. Proc. Natl. Acad. Sci. 115, 1843–1847 (2018).Article 
ADS 
CAS 

Google Scholar 
Blowes, S. A. et al. Local biodiversity change reflects interactions among changing abundance, evenness, and richness. Ecology online, e3820 (2022).Crowder, D. W., Northfield, T. D., Gomulkiewicz, R. & Snyder, W. E. Conserving and promoting evenness: Organic farming and fire-based wildland management as case studies. Ecology 93, 2001–2007 (2012).Article 

Google Scholar 
Hillebrand, H., Bennett, D. M. & Cadotte, M. W. Consequences of dominance: A review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).Article 

Google Scholar 
Masuda, R. et al. Fish assemblages associated with three types of artificial reefs: density of assemblages and possible impacts on adjacent fish abundance. Fishery Bulletin, National Oceanic and Atmospheric Administration. 108, 162–173 (2010).
Google Scholar 
Miyazono, S., Patiño, R. & Taylor, C. M. Desertification, salinization, and biotic homogenization in a dryland river ecosystem. Sci. Total Environ. 511, 444–453 (2015).Article 
ADS 
CAS 

Google Scholar 
Yonekura, R., Kita, M. & Yuma, M. Species diversity in native fish community in Japan: Comparison between non-invaded and invaded ponds by exotic fish. Ichthyol. Res. 51, 176–179 (2004).Article 

Google Scholar 
Evans, N. T., Shirey, P. D., Wieringa, J. G., Mahon, A. R. & Lamberti, G. A. Comparative cost and effort of fish distribution detection via environmental DNA analysis and electrofishing. Fisheries 42, 90–99 (2017).Article 

Google Scholar 
Miya, M., Gotoh, R. O. & Sado, T. MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fish. Sci. 86, 939–970 (2020).Article 
CAS 

Google Scholar 
Oka, S. et al. Environmental DNA metabarcoding for biodiversity monitoring of a highly diverse tropical fish community in a coral reef lagoon: Estimation of species richness and detection of habitat segregation. Environ. DNA 3, 55–69 (2021).Article 
CAS 

Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).Article 
CAS 

Google Scholar 
Pimm, S. L. et al. Emerging technologies to conserve biodiversity. Trends Ecol. Evol. 30, 685–696 (2015).Article 

Google Scholar 
Rourke, M. L. et al. Environmental DNA (eDNA) as a tool for assessing fish biomass: A review of approaches and future considerations for resource surveys. Environ. DNA 4, 9–33 (2022).Article 
CAS 

Google Scholar 
Tsuji, S. et al. Real-time multiplex PCR for simultaneous detection of multiple species from environmental DNA: An application on two Japanese medaka species. Sci. Rep. 8, 1–8 (2018).Article 
CAS 

Google Scholar 
Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).Article 

Google Scholar 
Rodríguez-Ezpeleta, N. et al. Biodiversity monitoring using environmental DNA. Mol. Ecol. Resour. 21, 1405–1409 (2021).Article 

Google Scholar 
Boivin-Delisle, D. et al. Using environmental DNA for biomonitoring of freshwater fish communities: Comparison with established gillnet surveys in a boreal hydroelectric impoundment. Environ. DNA 3, 105–120 (2021).Article 
CAS 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).Article 

Google Scholar 
Doi, H. et al. Compilation of real-time PCR conditions toward the standardization of environmental DNA methods. Ecol. Res. 36, 379–388 (2021).Article 
CAS 

Google Scholar 
Kelly, R. P. Making environmental DNA count. Mol. Ecol. Resour. 16, 10–12 (2016).Article 
CAS 

Google Scholar 
Kumar, G., Eble, J. E. & Gaither, M. R. A practical guide to sample preservation and pre-PCR processing of aquatic environmental DNA. Mol. Ecol. Resour. 20, 29–39 (2020).Article 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Let. 4, 423–425 (2008).Article 

Google Scholar 
Kuwae, M. et al. Sedimentary DNA tracks decadal-centennial changes in fish abundance. Commun. Biol. 3, 1–12 (2020).Article 

Google Scholar 
Lynggaard, C. et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr. Biol. 32, 701–707.e5 (2022).Article 
CAS 

Google Scholar 
Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis—A review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).Article 

Google Scholar 
Bylemans, J., Gleeson, D. M., Duncan, R. P., Hardy, C. M. & Furlan, E. M. A performance evaluation of targeted eDNA and eDNA metabarcoding analyses for freshwater fishes. Environ. DNA 1, 402–414 (2019).Article 

Google Scholar 
Wozney, K. M. & Wilson, C. C. Quantitative PCR multiplexes for simultaneous multispecies detection of Asian carp eDNA. J. Great Lakes Res. 43, 771–776 (2017).Article 
CAS 

Google Scholar 
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2016).Article 
CAS 

Google Scholar 
Fraija-Fernández, N. et al. Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecol. Evol. 10, 7560–7584 (2020).Article 

Google Scholar 
Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).Article 
ADS 

Google Scholar 
Thomsen, P. F. et al. Environmental DNA from seawater samples correlate with trawl catches of subarctic, deepwater fishes. PLoS ONE 11, e0165252 (2016).Article 

Google Scholar 
Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).Article 

Google Scholar 
Lim, N. K. M. et al. Next-generation freshwater bioassessment: eDNA metabarcoding with a conserved metazoan primer reveals species-rich and reservoir-specific communities. R. Soc. Open Sci. 3, 160635 (2016).Article 
ADS 

Google Scholar 
Hoshino, T., Nakao, R., Doi, H. & Minamoto, T. Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique. Sci. Rep. 11, 4372 (2021).Article 
ADS 
CAS 

Google Scholar 
Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).Article 
CAS 

Google Scholar 
Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcod. Metagenom. 2, e23297 (2018).
Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).Article 
ADS 
CAS 

Google Scholar 
Sato, M. et al. Quantitative assessment of multiple fish species around artificial reefs combining environmental DNA metabarcoding and acoustic survey. Sci. Rep. 11, 1–14 (2021).Article 
ADS 
CAS 

Google Scholar 
Ushio, M. Interaction capacity as a potential driver of community diversity. Proc. R. Soc. B Biol. Sci. 289, 20212690 (2022).Article 

Google Scholar 
Andruszkiewicz, E. A., Sassoubre, L. M. & Boehm, A. B. Persistence of marine fish environmental DNA and the influence of sunlight. PLoS ONE 12, e0185043 (2017).Article 

Google Scholar 
Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8, 8697–8712 (2018).Article 

Google Scholar 
Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).Article 

Google Scholar 
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).Article 
ADS 
CAS 

Google Scholar 
Hänfling, B. et al. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol. 25, 3101–3119 (2016).Article 

Google Scholar 
Nakagawa, H. et al. Comparing local-and regional-scale estimations of the diversity of stream fish using eDNA metabarcoding and conventional observation methods. Freshw. Biol. 63, 569–580 (2018).Article 
CAS 

Google Scholar 
Sato, H., Sogo, Y., Doi, H. & Yamanaka, H. Usefulness and limitations of sample pooling for environmental DNA metabarcoding of freshwater fish communities. Sci. Rep. 7, 14860 (2017).Article 
ADS 

Google Scholar 
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).Article 
CAS 

Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).Article 
ADS 
CAS 

Google Scholar 
Jane, S. F. et al. Distance, flow and PCR inhibition: eDNA dynamics in two headwater streams. Mol. Ecol. Resour. 15, 216–227 (2015).Article 
CAS 

Google Scholar 
Harper, L. R. et al. Needle in a haystack? A comparison of eDNA metabarcoding and targeted qPCR for detection of the great crested newt (Triturus cristatus). Ecol. Evol. 8, 6330–6341 (2018).Article 

Google Scholar 
Nichols, R. V. et al. Minimizing polymerase biases in metabarcoding. Mol. Ecol. Resour. 18, 927–939 (2018).Article 
CAS 

Google Scholar 
Hosoya, K. Yamakei Handy Illustrated Book 15: Freshwater fishes of Japan (Yama-Kei Publishers, 2019).
Google Scholar 
Nakabo, T. Fishes of Japan with Pictorial Keys to the Species (3-Volume Set). (Tokai University Press, 2013).Goutte, A., Molbert, N., Guérin, S., Richoux, R. & Rocher, V. Monitoring freshwater fish communities in large rivers using environmental DNA metabarcoding and a long-term electrofishing survey. J. Fish Biol. 97, 444–452 (2020).Article 
CAS 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).Article 
CAS 

Google Scholar 
Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).Article 

Google Scholar 
Tsuji, S., Ushio, M., Sakurai, S., Minamoto, T. & Yamanaka, H. Water temperature-dependent degradation of environmental DNA and its relation to bacterial abundance. PLoS ONE 12, e0176608 (2017).Article 

Google Scholar 
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—sequence relationships with an innovative metabarcoding protocol. PLoS ONE 10, e0130324 (2015).Article 

Google Scholar 
Nester, G. M. et al. Development and evaluation of fish eDNA metabarcoding assays facilitate the detection of cryptic seahorse taxa (family: Syngnathidae). Environ. DNA 2, 614–626 (2020).Article 

Google Scholar 
Piñol, J., Mir, G., Gomez-Polo, P. & Agustí, N. Universal and blocking primer mismatches limit the use of high-throughput DNA sequencing for the quantitative metabarcoding of arthropods. Mol. Ecol. Resour. 15, 819–830 (2015).Article 

Google Scholar 
Zhang, S., Zhao, J. & Yao, M. A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol. Evol. 11, 1609–1625 (2020).Article 
ADS 

Google Scholar 
Yamanaka, H. et al. A simple method for preserving environmental DNA in water samples at ambient temperature by addition of cationic surfactant. Limnology 18, 233–241 (2017).Article 
CAS 

Google Scholar 
Minamoto, T. et al. An illustrated manual for environmental DNA research: Water sampling guidelines and experimental protocols. Environ. DNA 3, 8–13 (2021).Article 
CAS 

Google Scholar 
Tsuji, S., Nakao, R., Saito, M., Minamoto, T. & Akamatsu, Y. Pre-centrifugation before DNA extraction mitigates extraction efficiency reduction of environmental DNA caused by the preservative solution (benzalkonium chloride) remaining in the filters. Limnology 23, 9–16 (2022).Article 
CAS 

Google Scholar 
R Core Team. R. A Language and Environment for Statistical Computing. (2021).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).Coulter, D. P. et al. Nonlinear relationship between Silver Carp density and their eDNA concentration in a large river. PLoS ONE 14, e0218823 (2019).Article 
CAS 

Google Scholar 
Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).Article 
CAS 

Google Scholar 
Kanno, K., Onikura, N., Kurita, Y., Koyama, A. & Nakajima, J. Morphological, distributional, and genetic characteristics of Cottus pollux in the Kyushu Island, Japan: indication of fluvial and amphidromous life histories within a single lineage. Ichthyol. Res. 65, 462–470 (2018).Article 

Google Scholar  More

Gao, A. L. & Zhou, K. Y. Growth and reproduction of three populations of finless porpoise, Neophocaena phocaenoides, in Chinese waters. Aquat Mamm 19, 3–12 (1993).
Google Scholar 
Jefferson, T. A. Preliminary analysis of geographic variation in cranial morphometrics of the finless porpoise (Neophocaena phocaenoides). Raffles Bull Zool 10, 3–14 (2002).
Google Scholar 
Pilleri, G. & Gihr, M. Contribution to the knowledge of the cetaceans of Pakistan with particular reference to the genera Neomeris, Sousa, Delphinus and Tursiops and description of a new Chinese porpoise (Neomeris asiaeorientalis). Investig Cetacea 4, 107–162 (1972).
Google Scholar 
Pilleri, G. & Gihr, M. On the taxonomy and ecology of the finless black porpoise, Neophocaena (Cetacea, Delphinidae). Mammalia 39, 657–673 (1975).Article 

Google Scholar 
Wang, P. L. The morphological characters and the problem of subspecies identifications of the finless porpoise. Fish Sci 11, 4–8 (1992).
Google Scholar 
Wang, P. L. On the taxonomy of the finless porpoise in China. Fish Sci 6, 10–14 (1992).
Google Scholar 
Gao, A. L. & Zhou, K. Y. Geographical variation of external measurements and three subspecies of Neophocaena phocaenoides in Chinese waters. Acta Theriol Sin 15, 81–92 (1995).
Google Scholar 
Wang, J. Y., Frasier, T. R., Yang, S. C. & White, B. N. Detecting recent speciation events: the case of the finless porpoise (genus Neophocaena). Heredity 101, 145–155 (2008).Article 

Google Scholar 
Jefferson, T. A. & Wang, J. Y. Revision of the taxonomy of finless porpoises (genus Neophocaena): the existence of two species. J Mar Anim Ecol 4, 3–16 (2011).
Google Scholar 
Zhou, X. M. et al. Population genomics of finless porpoises reveal an incipient cetacean species adapted to freshwater. Nat Commun 9, 1276 (2018).Article 
ADS 

Google Scholar 
Wang, D., Turvey, S.T., Zhao, X. & Mei, Z. Neophocaena asiaeorientalis ssp. asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/43205774/45893487 (2013).Wang, J. Y. & Reeves, R. Neophocaena Asiaeorientalis. The IUCN Red List of Threatened Species https://www.iucnredlist.org/species/41754/50381766 (2017).Kasuya, T. Japanese whaling and other cetacean fisheries. Environ Sci Pollut Res Int 14, 39–48 (2007).Article 

Google Scholar 
Yoshida, H., Shirakihara, K., Kishino, H. & Shirakihara, M. A population size estimate of the finless porpoise, Neophocaena phocaenoides, from aerial sighting surveys in Ariake Sound and Tachibana Bay, Japan. Popul Ecol 39, 239–247 (1997).Article 

Google Scholar 
Amano, M., Nakahara, F., Hayano, A. & Shirakihara, K. Abundance estimate of finless porpoises off the Pacific coast of eastern Japan based on aerial surveys. Mamm Study 28, 103–110 (2003).Article 

Google Scholar 
Shirakihara, K., Shirakihara, M. & Yamamoto, Y. Distribution and abundance of finless porpoise in the Inland Sea of Japan. Mar Biol 150, 1025–1032 (2007).Article 

Google Scholar 
Zuo, T., Sun, J. Q., Shi, Y. Q. & Wang, J. Primary survey of finless porpoise population in the Bohai Sea. Acta Theriol Sin 38, 551–561 (2018).
Google Scholar 
Ruan, R., Guo, A. H., Hao, Y. J., Zheng, J. S. & Wang, D. De novo assembly and characterization of narrow-ridged finless porpoise renal transcriptome and identification of candidate genes involved in osmoregulation. Int J Mol Sci 16, 2220–2238 (2015).Article 

Google Scholar 
Li, S. H. et al. Echolocation click sounds from wild inshore finless porpoise (Neophocaena phocaenoides sunameri) with comparisons to the sonar of riverine N. p. asiaeorientalis. J Acoust Soc Am 121, 3938–3946 (2007).Article 
ADS 

Google Scholar 
Dong, J. H., Wang, G. J. & Xiao, Z. Z. Migration and population difference of the finless porpoise in China. Mar Sci 5, 42–45 (1993).
Google Scholar 
Lu, Z. C. et al. Analysis of the diet of finless porpoise (Neophocaena asiaeorientalis sunameri) based on prey morphological characters and DNA barcoding. Conserv Genet Resour 8, 523–531 (2016).Article 

Google Scholar 
Chen, B. et al. Finless porpoises (Neophocaena asiaeorientalis) in the East China Sea: insights into feeding habits using morphological, molecular, and stable isotopic techniques. Can J Fish Aquat Sci 74, 1628–1645 (2017).Article 

Google Scholar 
Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022).Article 
ADS 

Google Scholar 
Chen, Y. X. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6 (2018).Article 
ADS 

Google Scholar 
Chikhi, R. & Medvedev, P. Informed and automated k-mer size selection for genome assembly. Bioinformatics 30, 31–37 (2014).Article 

Google Scholar 
Chin, C. S. et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods 10, 563–569 (2013).Article 

Google Scholar 
Cheng, H. Y., Concepcion, G. T., Feng, X. W., Zhang, H. W. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021).Article 

Google Scholar 
Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinformatics 19, 1–10 (2018).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article 

Google Scholar 
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3, 95–98 (2016).Article 

Google Scholar 
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).Article 
ADS 

Google Scholar 
Xiong, Y., Brandley, M. C., Xu, S. X., Zhou, K. Y. & Yang, G. Seven new dolphin mitochondrial genomes and a time-calibrated phylogeny of whales. BMC Evol Biol 9, 1–13 (2009).Article 

Google Scholar 
Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol 20, 1–17 (2019).Article 

Google Scholar 
Mayer, A., Lahr, G., Swaab, D. F., Pilgrim, C. & Reisert, I. The Y-chromosomal genes SRY and ZFY are transcribed in adult human brain. Neurogenetics 1, 281–288 (1998).Article 

Google Scholar 
Sinclair, A. H. et al. A gene from the human sex-determining region encodes a protein with homology to a conserved DNA-binding motif. Nature 346, 240–244 (1990).Article 
ADS 

Google Scholar 
Koopman, P., Gubbay, J., Vivian, N., Goodfellow, P. & Lovell-Badge, R. Male development of chromosomally female mice transgenic for Sry. Nature 351, 117–121 (1991).Article 
ADS 

Google Scholar 
Salo, P. et al. Molecular mapping of the putative gonadoblastoma locus on the Y chromosome. Genes Chromosomes Cancer 14, 210–214 (1995).Article 

Google Scholar 
Tsuchiya, K., Reijo, R., Page, D. C. & Disteche, C. M. Gonadoblastoma: molecular definition of the susceptibility region on the Y chromosome. Am J Hum Genet 57, 1400–1407 (1995).
Google Scholar 
Gegenschatz-Schmid, K., Verkauskas, G., Stadler, M. B. & Hadziselimovic, F. Genes located in Y-chromosomal regions important for male fertility show altered transcript levels in cryptorchidism and respond to curative hormone treatment. Basic Clin Androl 29, 1–8 (2019).Article 

Google Scholar 
Chen, N. Using Repeat Masker to identify repetitive elements in genomic sequences. Curr protoc Bioinf 5, 4–10 (2004).Article 

Google Scholar 
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res 35, W265–W268 (2007).Article 

Google Scholar 
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).Article 

Google Scholar 
Bao, W. D., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob DNA 6, 1–6 (2015).Article 

Google Scholar 
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27, 573–580 (1999).Article 

Google Scholar 
Liu, W. et al. Blood Transcriptome Analysis Reveals Gene Expression Differences between Yangtze Finless Porpoises from Two Habitats: Natural and Ex Situ Protected Waters. Fishes 7, 96 (2022).Article 

Google Scholar 
Yin, D. H. et al. Integrated analysis of blood mRNAs and microRNAs reveals immune changes with age in the Yangtze finless porpoise (Neophocaena asiaeorientalis). Comp Biochem Physiol B Biochem Mol Biol 256, 110635 (2021).Article 

Google Scholar 
Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019).Article 

Google Scholar 
Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol 20, 1–13 (2019).Article 

Google Scholar 
Stanke, M., Diekhans, M., Baertsch, R. & Haussler, D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics 24, 637–644 (2008).Article 

Google Scholar 
Keane, M. et al. Insights into the evolution of longevity from the bowhead whale genome. Cell Rep 10, 112–122 (2015).Article 

Google Scholar 
Yim, H. S. et al. Minke whale genome and aquatic adaptation in cetaceans. Nat Genet 46, 88–92 (2014).Article 

Google Scholar 
Jones, S. J. et al. The genome of the beluga whale (Delphinapterus leucas). Genes 8, 378 (2017).Article 
ADS 

Google Scholar 
Zhou, X. M. et al. Baiji genomes reveal low genetic variability and new insights into secondary aquatic adaptations. Nat Commun 4, 1–6 (2013).Article 
ADS 

Google Scholar 
Foote, A. D. et al. Convergent evolution of the genomes of marine mammals. Nat Genet 47, 272–275 (2015).Article 

Google Scholar 
Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol Biol 1962, 161–177 (2019).Article 

Google Scholar 
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44, D457–D462 (2016).Article 

Google Scholar 
Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28, 45–48 (2000).Article 

Google Scholar 
Korf, I. Gene finding in novel genomes. BMC bioinformatics 5, 1–9 (2004).Article 

Google Scholar 
Finn, R. D. et al. InterPro in 2017-beyond protein family and domain annotations. Nucleic Acids Res 45, D190–D199 (2017).Article 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J Mol Biol 215, 403–410 (1990).Article 

Google Scholar 
Mulder, N. J. & Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol 396, 59–70 (2007).Article 

Google Scholar 
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat Genet 25, 25–29 (2000).Article 

Google Scholar 
NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR21047154 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20760936 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997931 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997932 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997933 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997934 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRR20997935 (2022).NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP389529 (2022).Yin, D. H. et al. Neophocaena asiaeorientalis sunameri isolate NAS202207, whole genome shotgun sequencing project. GenBank https://identifiers.org/insdc.gca:GCA_026225855.1 (2022).Yin, D. H. et al. Gapless genome assembly of East Asian finless porpoise, Neophocaena asiaeorientalis sunameri. figshare https://doi.org/10.6084/m9.figshare.20381274.v2 (2022).Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 

Google Scholar 
Marçais, G. et al. MUMmer4: A fast and versatile genome alignment system. PLoS Comput Biol 14, e1005944 (2018).Article 

Google Scholar  More

Pan, H. et al. Archaea and bacteria respectively dominate nitrification in lightly and heavily grazed soil in a grassland system. Biol. Fert. Soils. 54(1), 41–54 (2018).Article 

Google Scholar 
Pan, H. et al. Understanding the relationships between grazing intensity and the distribution of nitrifying communities in grassland soils. Sci. Total Environ. 634, 1157–1164 (2018).Article 
ADS 

Google Scholar 
Dong, L., Li, J. J., Sun, J. & Yang, C. Soil degradation influences soil bacterial and fungal community diversity in overgrazed alpine meadows of the Qinghai-Tibet plateau. Sci. Rep. 11, 11538 (2021).Article 
ADS 

Google Scholar 
Oduor, C. O. et al. Enhancing soil organic carbon, particulate organic carbon and microbial biomass in semi-arid rangeland using pasture enclosures. BMC Ecol. 18, 45 (2018).Article 

Google Scholar 
Wang, S. Z., Fan, J. W., Li, Y. Z. & Huang, L. Effects of grazing exclusion on biomass growth and species diversity among various grassland types of the Tibetan Plateau. Sustainability 11(6), 1705 (2019).Article 

Google Scholar 
Simpson, A. C., Zabowski, D., Rochefort, R. M. & Edmonds, R. L. Increased microbial uptake and plant nitrogen availability in response to simulated nitrogen deposition in alpine meadows. Geoderma 336, 68–80 (2019).Article 
ADS 

Google Scholar 
Qasim, S. et al. Influence of grazing enclosure on vegetation biomass and soil quality. Int. Soil Water Conserv. 5(1), 62–68 (2017).Article 

Google Scholar 
Hirobe, M. et al. Effects of livestock grazing on the spatial heterogeneity of net soil nitrogen mineralization in three types of Mongolian grasslands. J. Soils Sediment. 13, 1123–1132 (2013).Article 

Google Scholar 
Luo, Y. K., Wang, C. H., Shen, Y., Sun, W. & Dong, K. H. The interactive effects of mowing and N addition did not weaken soil net N mineralization rates in semiarid grassland of Northern China. Sci. Rep. 9, 13457 (2019).Article 
ADS 

Google Scholar 
Wu, H. et al. Feedback of grazing on gross rates of N mineralization and inorganic N partitioning in steppe soils of Inner Mongolia. Plant Soil. 340(1–2), 127–139 (2011).Article 

Google Scholar 
Xu, Y. Q., Li, L. H., Wang, Q. B., Chen, Q. S. & Cheng, W. X. The patterns between nitrogen mineralization and grazing intensities in an Inner Mongolian typical steppe. Plant Soil. 300, 289–300 (2007).Article 

Google Scholar 
Wang, X. et al. Grazing improves C and N cycling in the Northern Great Plains: A meta-analysis. Sci. Rep. 6, 33190 (2016).Article 
ADS 

Google Scholar 
Pang, R., Sun, Y., Xu, X. L., Song, M. H. & Ouyang, H. Effects of clipping and shading on 15NO3− and 15NH4+ recovery by plants in grazed and ungrazed temperate grasslands. Plant Soil. 433(1–2), 339–352 (2018).Article 

Google Scholar 
Sun, Y., Schleuss, P. M., Pausch, J., Xu, X. L. & Kuzyakov, Y. Nitrogen pools and cycles in Tibetan Kobresia pastures depending on grazing. Biol. Fert. Soils. 54(5), 569–581 (2018).Article 

Google Scholar 
Andrioli, R. J., Distel, R. A. & Didone, N. G. Influence of cattle grazing on nitrogen cycling in soils beneath Stipa tenuis, native to central Argentina. J. Arid. Environ. 74(3), 419–422 (2010).Article 
ADS 

Google Scholar 
Norman, J. S., Lin, L. & Barrett, J. E. Paired carbon and nitrogen metabolism by ammonia-oxidizing bacteria and archaea in temperate forest soils. Ecosphere 6(10), 1–11 (2016).
Google Scholar 
Mukhtar, H., Lin, Y. P., Lin, C. M. & Petway, J. R. Assessing thermodynamic parameter sensitivity for simulating temperature responses of soil nitrification. Environ. Sci.-Proc. Imp. 21(9), 1596–1608 (2019).
Google Scholar 
Rütting, T., Schleusner, P., Hink, L. & Prosser, J. I. The contribution of ammonia-oxidizing archaea and bacteria to gross nitrification under different substrate availability. Soil Biol. Biochem 160, 108353 (2021).Article 

Google Scholar 
Pan, H. et al. Management practices have a major impact on nitrifier and denitrifier communities in a semiarid grassland ecosystem. J. Soils Sediment. 16, 896–908 (2016).Article 

Google Scholar 
Szukics, U. et al. Management versus site effects on the abundance of nitrifiers and denitrifiers in European mountain grasslands. Sci. Total Environ. 648, 745–753 (2019).Article 
ADS 

Google Scholar 
Chen, Q., Hooper, D. U. & Lin, S. Shifts in species composition constrain restoration of overgrazed grassland using nitrogen fertilization in Inner Mongolian steppe, China. PLoS ONE 6(3), e16909 (2011).Article 
ADS 

Google Scholar 
Raison, R. J., Connell, M. J. & Khanna, P. K. Methodology for studying fluxes of soil mineral-N in situ. Soil Biol. Biochem. 19, 521–530 (1987).Article 

Google Scholar 
Kurola, J., Salkinoja-Salonen, M., Aarnio, T., Hultman, J. & Romantschuk, M. Activity, diversity and population size of ammonia-oxidizing bacteria in oil-contaminated land farming soil. FEMS Microbiol. Lett. 250, 33–38 (2005).Article 

Google Scholar 
Tran, H. T. et al. Bacterial community progression during food waste composting containing high dioctyl terephthalate (DOTP) concentration. Chemosphere 265, 129064 (2021).Article 
ADS 

Google Scholar 
Hook, P. B. & Burke, I. C. Evaluation of a method for estimating net nitrogen mineralization in a semiarid grassland. Soil Sci. Soc. Am. J. 59, 831–837 (1995).Article 
ADS 

Google Scholar 
Liu, T. Z., Nan, Z. B. & Hou, F. J. Grazing intensity effects on soil nitrogen mineralization in semi-arid grassland on the Loess Plateau of northern China. Nutr. Cyc. Agroecosyst. 91(1), 67–75 (2011).Article 

Google Scholar 
Li, J. P., Ma, H. B., Xie, Y. Z., Wang, K. B. & Qiu, K. Y. Deep soil C and N pools in long-term fenced and overgrazed temperate grasslands in northwest China. Sci. Rep. 9, 16088 (2019).Article 
ADS 

Google Scholar 
Di, H. J. et al. Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nat. Geosci. 2(9), 621–624 (2009).Article 
ADS 

Google Scholar 
Li, J. P., Zheng, Z. R., Xie, H. T., Zhao, N. X. & Gao, Y. B. Increased soil nutrition and decreased light intensity drive species loss after eight years grassland enclosures. Sci. Rep. 7, 44525 (2017).Article 
ADS 

Google Scholar 
Luo, C. Y. et al. Effect of warming and grazing on litter mass loss and temperature sensitivity of litter and dung mass loss on the Tibetan plateau. Glob. Change Biol. 16, 1606–1617 (2010).Article 
ADS 

Google Scholar 
Shahzad, T. et al. Contribution of exudates, arbuscular mycorrhizal fungi and litter depositions to the rhizosphere priming effect induced by grassland species. Soil Biol. Biochem. 80, 146–155 (2015).Article 

Google Scholar 
Xie, Z. et al. Identifying response groups of soil nitrifiers and denitrifiers to grazing and associated soil environmental drivers in Tibetan alpine meadows. Soil Biol. Biochem. 77, 89–99 (2014).Article 

Google Scholar 
Clark, I. M., Hughes, D. J., Fu, Q. L., Abadie, M. & Hirsch, P. R. Metagenomic approaches reveal differences in genetic diversity and relative abundance of nitrifying bacteria and archaea in contrasting soils. Sci. Rep. 11, 15905 (2021).Article 
ADS 

Google Scholar 
He, J. Z. et al. Quantitative analyses of the abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing archaea of a Chinese upland red soil under long-term fertilization practices. Environ. Microbiol. 9, 2364–2374 (2007).Article 

Google Scholar 
Meyer, A. et al. Influence of land use intensity on the diversity of ammonia oxidizing bacteria and archaea in soils from grassland ecosystems. Microb. Ecol. 67(1), 161–166 (2014).Article 

Google Scholar 
Zhu, X. X. et al. Effects of warming, grazing/cutting and nitrogen fertilization on greenhouse gas fluxes during growing seasons in an alpine meadow on the Tibetan Plateau. J. Agric. Meteorol. 214–215, 506–514 (2015).Article 

Google Scholar 
Jia, Z. J. & Cornrad, R. Bacteria rather than archaea dominate microbial ammonia oxidation in an agricultural soil. Environ. Microbiol. 11(7), 1658–1671 (2009).Article 

Google Scholar 
Verhamme, D. T., Prosser, J. I. & Nicol, G. W. Ammonia concentration determines differential growth of ammonia-oxidising archaea and bacteria in soil microcosms. ISME J. 5, 1067–1071 (2011).Article 

Google Scholar 
Zhou, X. H. et al. Diversity, abundance and community structure of ammonia-oxidizing archaea and bacteria in riparian sediment of Zhenjiang ancient canal. Ecol. Eng. 90, 447–458 (2016).Article 

Google Scholar 
Martens-Habbena, W., Berube, P. M., Urakawa, H., de la Torre, J. R. & Stahl, D. A. Ammonia oxidation kinetics determine niche separation of nitrifying archaea and bacteria. Nature 461, 976–979 (2009).Article 
ADS 

Google Scholar 
Clark, D. R. et al. Mineralization and nitrification: Archaea dominate ammonia-oxidising communities in grassland soils. Soil Biol. Biochem. 143, 107725 (2020).Article 

Google Scholar 
Long, X. N., Chen, C. R., Xu, Z. H., Linder, S. & He, J. Z. Abundance and community structure of ammonia oxidizing bacteria and archaea in a Sweden boreal forest soil under 19-year fertilization and 12-year warming. J. Soils Sediment. 12, 1124–1133 (2012).Article 

Google Scholar 
Wessén, E. & Hallin, S. Abundance of archaeal and bacterial ammonia oxidizers-possible bioindicator for soil monitoring. Ecol. Indic. 11, 1696–1698 (2011).Article 

Google Scholar 
Yang, Y. et al. Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland. Glob. Change Biol. 19(2), 637–648 (2013).Article 
ADS 

Google Scholar 
Zhang, C. J. et al. Impacts of long-term nitrogen addition, watering and mowing on ammonia oxidizers, denitrifiers and plant communities in a temperate steppe. Appl. Soil Ecol. 130, 241–250 (2018).Article 

Google Scholar 
Alves, R. J. E., Minh, B. Q., Urich, T., Haeseler, A. V. & Schleper, C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat. Commun. 9, 1517 (2018).Article 
ADS 

Google Scholar 
DeLong, E. F. Everything in moderation archaea as ‘non extremophiles’. Curr. Opin. Genet. Dev. 8(6), 649–654 (1998).Article 

Google Scholar 
Jia, Z. J. et al. Evidence for niche differentiation of nitrifying communities in grassland soils after 44 years of different field fertilization scenarios. Pedoshpere 30(1), 87–97 (2019).
Google Scholar 
Wang, X. L. et al. Long-term fertilization effects on active ammonia oxidizers in an acidic upland soil in China. Soil Biol. Biochem. 84, 28–37 (2015).Article 

Google Scholar 
Li, Y. Y., Chapman, S. J., Nicol, G. W. & Yao, H. Y. Nitrification and nitrifiers in acidic soils. Soil Biol. Biochem. 116, 290–301 (2018).Article 

Google Scholar 
Olivera, N. L., Prieto, L., Bertiller, M. B. & Ferrero, M. A. Sheep grazing and soil bacterial diversity in shrub lands of the Patagonian Monte, Argentina. J. Arid. Environ. 125, 16–20 (2016).Article 
ADS 

Google Scholar  More

Further details about radiocarbon and thermal analysis, isotopic partitioning procedures and quantification of their uncertainty, and statistical analyses can be found in Supplementary Methods.Study soils, experimental design and soil samplingWe selected three soil types: eutric cambisol, chromic vertisol and silandic andosol70. The three soil profiles studied were found in long-term semi-natural grasslands located relatively close to each other ( More

Tularemia affects animal welfare, human health, and the environment and is thus better approached from a one-health perspective27. Several studies in the Northern hemisphere28, and more recently in Australia15,16, have provided a vital research track in the epidemiology of this disease. In contrast, studies in Africa are too limited and scarce. The aim of this study was to investigate the presence of tularemia in wild leporids collected in Northern Algeria. These animals are highly susceptible to F. tularensis infection and considered sentinel hosts for surveillance of tularemia. The strategy we used to detect F. tularensis in leporids mainly used molecular, histological and immunohistochemical analyzes of tissues taken from animals found dead or hunted. To the best of our knowledge, detection of F. tularensis by PCR or culture has not been previously reported in wild leporidae in Algeria or other African countries.Animal tissue samples were tested using three qPCR assays of variable sensitivity and specificity. The Type B-qPCR test targets a specific junction between ISFtu2 and a flanking 3′ region, which is considered specific for F. tularensis subsp. holarctica26, the only tularemia agent found in Europe and Asia. The Tul4-qPCR assay targets a simple copy gene encoding a surface protein, which can be found in the genome of all F. tularensis subspecies causing tularemia and that of the aquatic bacterium F. novicida. Because F. novicida has never been isolated from lagomorphs or other animal species, and very rarely from human29, a positive Tul4 qPCR for the studied tissue samples likely indicated the presence of F. tularensis DNA. The ISFtu2 qPCR is considered highly sensitive because multiple copies of this insertion sequence are found in the F. tularensis genome. However, it lacks specificity because ISFtu2 is also found in many other Francisella species25.Two animals were considered “probable” tularemia cases because some of their samples were positive for the three qPCR tests. Ten animals were considered “possible” tularemia cases because their samples were positive for the ISFtu2 and Tul4 qPCRs but not the Type B qPCR. Finally 19 leporids were “uncertain” cases because only samples positive for the ISFtu2 qPCR were found. For the remaining 43 animals, all the tested samples were negative for the three qPCRs. Overall, we detected F. tularensis DNA-positive samples in 12/74 (16.21%) leporids, which strongly suggest that tularemia is present in the lagomorph population of the study area. The positive Type B qPCR tests in two animals suggested that F. tularensis subsp. holarctica could be the involved subspecies. We did not confirm these data by isolating F. tularensis from the studied leporids. However, the isolation of this pathogen from human or animal samples is tedious and has a low sensitivity13. Moreover, most of our samples were not appropriate for F. tularensis culture because of their long-term preservation in ethanol 70° or 10% formalin. Further study using fresh (non-fixed) tissue samples from dead leporids collected in the same study area is needed to definitively confirm the presence of tularemia in these animals and characterize the F. tularensis subspecies and genotypes involved.Although PCR is usually more sensitive than culture for detecting F. tularensis, it also has some limitations. Firstly, the DNA extraction from organs preserved in ethanol for several months was difficult although easier for spleen than for liver samples. Some tissue samples could be lysed only after overnight incubation with proteinase K. Secondly, tissue samples contained PCR inhibitors as demonstrated by better DNA amplification from some samples after their dilution in PCR grade water. To reduce the effect of PCR inhibitors, organ samples with negative qPCR were retested using Bovine Serum Albumin (BSA) and the Real-time PCR system TaqMan (Applied Biosystems, Munich, Germany)30. Finally, DNA regions to be amplified were optimized to obtain high sensitivity and specificity of qPCR tests.IHC detection of F. tularensis in formalin-fixed tissue can be helpful for tularemia diagnosis31,32. For one possible tularemia case, F. tularensis could be detected on immunohistochemical (IHC) examination of a liver sample using a specific anti-F. tularensis antibody. The intensity and localization of positive staining were comparable to those previously recorded for other animals32,33. IHC did not provide interpretable findings for four other tested specimens. Such negative results might be explained by an inhomogeneous distribution of infectious foci in the involved organs as well as a low bacterial inoculum in infected tissues. This has been previously demonstrated in tularemia granulomatous lesions in cell types like epithelial cells of the kidney, testis, and epididymis, hepatocytes, and bronchiolar epithelial cells31. Besides, IHC is a delicate technology whose results are highly dependent on the quality and fixation time of the organ tissues34. IHC analysis of dead animal tissues remains challenging, especially in case of tissue necrosis34.In our limited case series we found a F. tularensis infection prevalence in leporids of 2.7% (2/74) for probable tularemia cases and 16.2% (12/74) when considering both probable and posible cases. We cannot make a guess about the prevalence of tularemia because our series is not representative of the general lagomorph population in the study area. In Germany, F. tularensis DNA was detected in 1.1% of European Brown hares and 2.4% of wild rabbits collected between 2009 and 201435. Higher infection rates were reported in the same country, including 11.8% (100/848 animals) in hares collcted in the North Rhine-Westphalia region36 and 30% (55/179) in brown hares collected between 2010 and 2016 in Baden-Wuerttemberg37. In Hungary, the prevalence of tularemia in hares was evaluated at 4.9–5.3%38. In Portugal, prevalences of 4.3% and 6.3% were reported in brown hares and wild rabbits, respectively39. However, the comparison of the reported tularemia prevalences in leporids is irrelevant because studies involved different animal species and geographic areas, and used different methods for F. tularensis detection.Two possibilities could explain the lack of detection of tularemia in Algeria before this study. The first hypothesis is that this disease was not searched for in previous years, while it could have been present in this country for decades. The second hypothesis is that tularemia was recently imported in Algeria. Migratory birds may have been involved in the long-distance spread of F. tularensis40. These hosts can be infested by ectoparasites such as ticks which are the primary vectors of tularemia41,42. They can also spread the bacteria in the hydro-telluric environment through their secretions and feces18,43,44. An alternative possibility is that F. tularensis-infected animals (especially game animals) have been imported in Algeria from endemic countries. Whatever the mode of introduction of tularemia in Algeria, the dissemination of this disease over time might have been facilitated by the ability of F. tularensis to infect multiple hosts and its better survival in a cool environment45, which characterizes Northern Algeria climate. The emergence or re-emergence of tularemia in other countries has been related to climate change, human-mediated movement of infected animals, and wartime resulting in a significant rise of F. tularensis infections in the rodent populations39,46.In our study, infected animals were collected throughout 4 years, although more frequently in autumn. Probable and possible tularemia cases were mainly collected during the hunting season (i.e., September, October, November, and December). Animals could not be collected in February because of heavy rains and in May and June because it corresponds to female leporids’ lactation period. In most endemic countries, tularemia cases are typically more frequent in late spring, the summer months, and early autumn37,47,48,49,50. Occasionally, fatal tularemia cases in hares have been predominantly reported during the cold season11,51. The climatic conditions can affect tularemia outbreaks in animals, depending on the reservoir involved and the predominant modes of infection52.We detected tularemia more frequently in female than in male hares, and the reverse was true for wild rabbits. The prevalence of tularemia in male or female lagomorphs varies between studies. In Sweden, Morener et al.50 reported a tularemia case series only involving male hares. In the same country, Borg et al.50 observed an overrepresentation of females in the epizootic of 1967. They suggested that, compared to males, females had a higher risk of exposure to infected mosquitoes or were more vulnerable to tularemia because they were pregnant or had just given birth to a litter50. Tularemia was found in a few juveline leporids, which might be explained by a shorter exposure time to F. tularensis, a higher death rates due to higher susceptibility to F. tularensis infection or easier predation by their natural enemies, or more frequent hunting of adults compared to the juveniles53.Tularemia is usually more frequently detected in leporids found dead than in hunted animals. As an example, a German study reported a higher prevalence of tularemia in hares found dead (2.9%) than in hunted ones (0.7%)35. In our study, most qPCR-positive animals were hunted. Our study might not be representative of the prevalence of tularemia in either population because most collected animals had been hunted.The incubation period and clinical presentation of tularemia in leporids vary according to the species considered. Tularemia is typically an acute disease in mountain hares (Lepus timidus) in Scandinavia and has a chronic pattern in European brown hares (Lepus europaeus) in Central Europe50. The incubation time and clinical presentation of tularemia can be different in Cape hares (Lepus capensis). Wild rabbits are less sensitive to F. tularensis infection than hares31,39,54. An extended incubation period and chronic evolution of tularemia would facilitate the detection of F. tularensis in infected animals. In our study, a similar tularemia prevalence was found in the Cape hares and wild rabbits, which might reflect exposure to a same biotope area and environmental reservoirs of F. tularensis.The pathological lesions of tularemiia in leporids can vary according to the F. tularensis strain involved, the mode and route of infection, and the susceptibility and immune status of the host32,50. In the European brown hares, granulomas with central necrosis have been reported in the lungs and kidneys and occasionally in the liver, spleen, bone marrow, and lymph nodes50. In contrast, only acute necrosis in the liver, spleen, bone marrow, and lymph nodes have been found in Lepus timudus hares in Sweden50. The lesions in the Japanese hare (Lepus brachyurus angustidens) are comparable to those of Lepus timidus, except for cutaneous, lung, brain, and adrenal gland lesions32. In the European rabbit, Oryctolagus cuniculus, tularemia is not associated with identifiable macroscopic tissue lesions39,55. To our knowledge, no reports describing post-mortem lesions in Cape hares with tularemia are available. In this study, similar lesions were found in hares and wild rabbits except necrotic foci only observed in some wild rabbit organs (such as liver, lungs, kidney, ovary). Most animals had pathological lesions of pneumonia, gastritis and enteritis. Kidney lesions and adrenal glands enlargment were oberved. Necrotic lesions were occasionally found in the lungs, liver, spleen and ovary and hemorrhages in the lungs, liver, and intestines.Tularemia is an arthropod-born disease in most endemic areas14,22,28. In our study, 50% of positive leporids were infested by known tularemia vectors such as ticks (Ixodes ricinus56,57, Rhipicephalus sanguineus39), fleas (Spillopsylus cuniculi58), and lice of lagomorphs (Haemodipsus lepori and Haemodipsus setoni59,60). Ticks are the most significant arthropod vectors of tularemia61. Ticks are frequently involved in the transmission of tularemia in North America, including Dermacentor andersoni, D. variabilis, and Amblyomma americanum57,62,63. In Europe, tick-borne tularemia represents 13% to 26% of human cases57,64. The involved species include D. marginatus, D. reticulatus, I. ricinus, R. sanguineus, and Haemaphysalis concinna65,66. Further research on wild leporid sucking arthropods is needed to confirm the presence and clarify the ecology of F. tularensis in Algeria.Our study reports for the first time the detection of F. tularensis DNA in leporids from Northern Algeria. The markers most in favor of tularemia in the animals studied are the positivity of qPCR tests, in particular, the “type B” qPCR test which amplifies a specific DNA sequence of F. tularensis subsp. holarctica, and a positive immunohistological examination in one animal. Further investigation is needed to confirm our results by the isolation of this pathogen from animal samples and determine the F. tularensis subspecies and genotypes involved. This would allow the characterization of the F. tularensis subspecies and genotypes present in Algeria. Furthermore, our findings push us in future studies to seek tularemia in the Algerian human population. To achieve this, interdisciplinary or trans-disciplinary collaborative efforts underpinned by the One Health concept will be necessary. More