Ecology

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

All statistical analyses were performed through R version 4.1.050. Besides the explicitly mentioned packages, the R packages cowplot51, data.table52, dplyr53, ggplot254, itsadug55, purrr56, raster57, sf58, sfheaders59, tibble60 and tidyr61 were key for data handling, data analysis, and plotting. Posterior distributions were summarised through means and credible intervals (CIs). CIs are the highest density intervals, calculated through the package bayestestR62. To summarise multiple posterior distributions, 5000 Monte Carlo simulations were used.Study regionThe study included data from the whole of Switzerland. As an observation unit for records, we chose 1 × 1 km squares (henceforth squares). Switzerland covers 41,285 km2, spanning a large gradient in elevation, climate and land use. It can be divided into five coarse biogeographic regions based on floristic and faunistic distributions and on institutional borders of municipalities63 (Fig. 1b). The Jura is a mountainous but agricultural landscape in the northwest (~4200 km2, 300–1600 m asl; annual mean temperature: ~9.4 °C, annual precipitation: ~1100 mm (gridded climate data here and in the following from MeteoSwiss (https://www.meteoswiss.admin.ch), average 1980–2020, at sites ~500 m asl.)). The Jura is separated from the Alps by the Plateau, which is the lowland region spanning from the southwest to the northeast (~11,300 km2, 250–1400 m asl, mostly below 1000 m asl; ~9.5 °C, ~1100 mm). It is the most densely populated region with most intensive agricultural use. For the Alps, three regions can be distinguished. The Northern Alps (~10,700 km2, 350–4000 m asl; ~9.2 °C, ~1400 mm), which entail the area from the lower Prealps, which are rather densely populated and largely used agriculturally, up to the northern alpine mountain range. The Central Alps (~11,300 km2, 450–4600 m asl; ~9.5 °C, ~800 mm) comprise of the highest mountain ranges in Switzerland and the inner alpine valleys characterised by more continental climate (i.e., lower precipitation). Intensive agricultural land use is concentrated in the lower elevations and agriculture in higher elevations is mostly restricted to grassland areas used for summer grazing. The Southern Alps (~3800 km2, 200–3800 m asl; ~10.4 °C, 1700 mm) range from the southern alpine mountain range down to the lowest elevations of Switzerland and are clearly distinguished from the other regions climatically, as they are influenced by Mediterranean climate, resulting in, e.g., milder winters. Besides differences between biogeographic regions, climate, land use and changes therein vary greatly between different elevations (Supplementary Fig. S9). To account for these differences, we split the regions in two elevation classes at the level of squares. All squares with a mean elevation of less than 1000 m asl were assigned to the low elevation, whereas squares above 1000 m asl were assigned to the high elevation (no squares in the Plateau fell in the high elevation). This resulted in nine bioclimatic zones (Fig. 1b), for which separate species trends were estimated in the subsequent analyses. The threshold of 1000 m asl enabled a meaningful distinction based on the studied drivers (climate and land-use change) and was also determined by the availability of records data (high coverage in all nine bioclimatic zones).Species detection dataWe extracted records of butterflies (refers here to Papilionoidea as well as Zygaenidae moths), grasshoppers (refers here to all Orthoptera) and dragonflies (refers here to all Odonata) from the database curated by info fauna (The Swiss Faunistic Records Centre; metadata available from the GBIF database at https://doi.org/10.15468/atyl1j, https://doi.org/10.15468/bcthst, https://doi.org/10.15468/fcxtjg). This database unites faunistic records made in Switzerland from various sources including both records by private persons and from projects such as research projects, Red-List inventories or checks of revitalisation measures. Only records with a sufficient precision, both temporally (day of recording) and spatially (place of recording known to the precision of 1 km2 or less), were used for analyses. Besides temporal and spatial information, information on the observer and the project (if any) was obtained for each record. All records made by a person/project on a day in a square were attributed to one visit, which was later used as replication unit to model the observation process (see below).We included records from the focal time range 1980–2020. Additionally, we included records from 1970–1979 for butterflies in occupancy-detection models to increase the robustness of mean occupancy estimates. We excluded the mean occupancy estimates for these additional years from further analyses to cover the same period for all groups. Prior to analyses, following the approach in ref. 26, we excluded observations of non-adult stages and observations from squares that only were visited in 1 year of the studied period, because these would not contain any information on change between years64. This resulted in 18,018 squares (15,248 for butterflies, 9870 for grasshoppers, 5188 for dragonflies) and 1,448,134 records (879,207 butterflies, 272,863 grasshoppers, 296,064 dragonflies) that we included in the analyses (Supplementary Fig. S2). The three datasets for the different groups were treated separately for occupancy-detection modelling, following the same procedures for all three groups. To determine detections and non-detections for each species and visit, which could then be used for occupancy-detection modelling, we only included visits that (a) did not originate from a project, which had a restricted taxonomic focus not including the focal species, (b) were not below the 5% quantile or above the 95% quantile of the day of the year at which the focal species has been recorded26 and (c) were from a bioclimatic zone, from which the focal species was recorded at least once.Occupancy-detection modelsWe used occupancy-detection models65,66 to estimate annual mean occupancy of squares for the whole of Switzerland and for the nine bioclimatic zones for each species (i.e., mean number of squares occupied by a species), mostly following the approach in ref. 26. We fitted a separate model for each species, based on different datasets for the three groups. We included only species that were recorded in any square in at least 25% of all analysed years. Occupancy-detection models are hierarchical models in which two interconnected processes are modelled jointly, one of which describes occurrence probability (ecological process; used to infer mean occupancy), whereas the other describes detection probability (observation process)65. The two processes are modelled through logistic regression models. The occupancy model estimates occurrence probability for all square and year combinations, whereas the observation model estimates the probability that a species has been detected by an observer during a visit. More formally, each square i in the year t has the latent occupancy status zi,t, which may be either 1 (present) or 0 (absent). zi,t depends on the occurrence probability ψi,t as follows$${z}_{i,t}sim {{{mbox{Bern}}}}left({psi }_{i,t}right)$$
(1)
The occupancy status is linked to the detection/non-detection data yi,t,j at square i in year t at visit j as$${y}_{i,t,, j}{{|}}{z}_{i,t}sim {mathrm {Bern}}({z}_{i,t}{p}_{i,t,j})$$
(2)
where pi,t,j is the detection probability.The regression model for occurrence probability (occupancy model) looked as follows$${{mbox{logit}}}({psi }_{i,t})={mu }_{o}+{beta }_{o1}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}+{beta }_{o2}{{{{{rm{elevatio}}}}}}{{{{{{rm{n}}}}}}}_{i}^{2}+{alpha }_{o1,i}+{alpha }_{o2,i}+{gamma }_{r(i),t}$$
(3)
with μo being the global intercept, elevationi being the scaled elevation above sea level and αo1,i, αo2,i and γr(i),t being the random effects for fine biogeographic region (12 levels, Supplementary Fig. S10; these were again defined based on floristic and faunistic distributions and followed institutional borders63), square and year. The random effects for fine biogeographic region and square were modelled as follows:$${alpha }_{o1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o1}right)$$
(4)
and$${alpha }_{o2}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{o2}right)$$
(5)
The random effect of the year was implemented with separate random walks per zone following ref. 67, which allowed the effect to vary between the nine bioclimatic zones, while accounting for dependencies among consecutive years. Conceptually, in random walks, the effect of 1 year is dependent on the previous year’s effect, resulting in trajectories with less sudden changes between consecutive years. This was implemented as follows:$${gamma }_{r,t}sim left{begin{array}{c}{{{{{rm{Normal}}}}}}left(0,{1.5}^{2}right){{{{rm{for}}}}},t=1\ {{{{{rm{Normal}}}}}}left({gamma }_{r,t-1},{sigma }_{gamma r}^{2}right){{{{rm{for}}}}},t , > ,1end{array}right.$$
(6)
with$${sigma }_{gamma r}sim {{mbox{Cauchy}}}left(0,1right)$$
(7)
The regression model for detection probability (observation model) looked as follows$${{{{rm{logit}}}}}({p}_{i,t,j}) =, {mu }_{d}+{beta }_{d1}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}+{beta }_{d2}{{{{{rm{yda}}}}}}{{{{{{rm{y}}}}}}}_{j}^{2}+{beta }_{d3}{{{{{rm{shortlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d4}{{{{{rm{longlis}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d5}{{{{{rm{exper}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d6}{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j}+{beta }_{d7}{{{{{rm{targeted}}}}}}_{{{{{rm{projec}}}}}}{{{{{{rm{t}}}}}}}_{j} \ quad+ {beta }_{d8}{{{{{rm{redlis}}}}}}{{{{{{rm{t}}}}}}}_{j}+{alpha }_{d1,t}$$
(8)
where μd is the global intercept, ydayj is the scaled day of the year of visit j, shortlistj and longlistj are dummies of a three-level factor denoting the number of species recorded during the visit (1; 2–3; >3), and expertj, projectj, targeted_projectj and redlistj are dummies of a five-level factor denoting the source of the data. The source might either be a common naturalist observation (reference level), an observation by an expert naturalist, an observation made during a not further specified project, an observation made in a project targeted at the focal species or an observation made during a Red-List inventory. An expert naturalist was defined as an observer that contributed a significant number of records, which was defined as the upper 2.5% quantile of all observers arranged by their total number of records, and that made at least one visit with an exceptionally long species list, which was defined as a visit in the upper 2.5% quantile of all visits arranged by the number of records. The proportions of records originating from these different sources changed across years, but change was not unidirectional and differed among the investigated groups (Supplementary Fig. S11), such that accounting for data source in the model should suffice to yield reliable estimates of occupancy trends. αd1,t is a random effect for year, which was modelled as$${alpha }_{d1}sim {{{{{rm{Normal}}}}}}left(0,{sigma }_{d1}right)$$
(9)
The occupancy and observation models were fitted jointly in Stan through the interface CmdStanR68. Four Markov chain Monte Carlo chains with 2000 iterations each, including 1000 warm-up iterations, were used. Priors of the model parameters are specified in Supplementary Table S5. For the prior distribution of global intercepts, a standard deviation of 1.5 was chosen to not overweight the extreme values on the probability scale. To ensure that chains mixed well, Rhat statistics for annual mean occupancy estimates were calculated through the package rstan69. For Switzerland-wide annual estimates (n = 18,140), 98.0% of values met the standard threshold of 1.1 (99.9% of values 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

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

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

Hoegh-Guldberg O, Smith JG. The effect of sudden changes in temperature, light, and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper) and Seriatopora hysterix (Dana). J Exp Mar Biol Ecol. 1989;129:279–303.Article 

Google Scholar 
Glynn PW. Coral reef bleaching: ecological perspectives. Coral Reefs. 1993;12:1–17.Article 

Google Scholar 
Berkelmans R, van Oppen MJH. The role of zooxanthellae in the thermal tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc R Soc B: Biol Sci. 2006;273:2305–12.Article 

Google Scholar 
Cunning R, Gillette P, Capo T, Galvez K, Baker AC. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans. Coral Reefs. 2015;34:155–60.Article 

Google Scholar 
Scharfenstein HJ, Chan WY, Buerger P, Humphrey C, van Oppen MJH. Evidence for de novo acquisition of microalgal symbionts by bleached adult corals. ISME J. 2022;16:1676–9.Article 

Google Scholar 
Goulet TL. Most corals may not change their symbionts. Mar Ecol Prog Ser. 2006;321:1–7.Article 

Google Scholar 
Jones A, Berkelmans R. Potential costs of acclimatization to a warmer climate: growth of a reef coral with heat tolerant vs. sensitive symbiont types. PLoS ONE. 2010;5:e10437.Article 

Google Scholar 
van Oppen MJH, Oliver JK, Putnam HM, Gates RD. Building coral reef resilience through assisted evolution. Proc R Soc B: Biol Sci. 2015;112:2307–13.
Google Scholar 
Buerger P, Alvarez C, Coppin CW, Pearce SL, Chakravarti LJ, Oakeshott JG, et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci Adv. 2020;6:eaba2498.Kuffner IB, Toth LT. A geological perspective on the degradation and conservation of western Atlantic coral reefs. Conserv Biol: J Soc Conserv Biol. 2016;30:706–15.Article 

Google Scholar 
Young CN, Schopmeyer SA, Lirman D. A review of reef restoration and Coral propagation using the threatened genus Acropora in the Caribbean and western Atlantic. Bull Mar Sci. 2012;88:1075–98.Article 

Google Scholar 
Reich HG, Kitchen SA, Stankiewicz KH, Devlin-Durante M, Fogarty ND, Baums IB. Genomic variation of an endosymbiotic dinoflagellate (Symbiodinium fitti) among closely related coral hosts. Mol Ecol. 2021;30:3500–14.Article 

Google Scholar 
Baums IB, Devlin-Durante MK, Lajeunesse TC. New insights into the dynamics between reef corals and their associated dinoflagellate endosymbionts from population genetic studies. Mol Ecol. 2014;23:4203–15.Article 

Google Scholar 
Gantt SE, Keister E, Manfroy A, Merck D, Fitt W, Muller E, et al. Wild and nursery-raised corals: comparative physiology of two framework coral species. Coral Reefs. (In Press).Hume BCC, Smith EG, Ziegler M, Hugh J, Warrington M, Burt J, et al. SymPortal: a novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol Ecol Resour. 2019;19:1063–80.Article 

Google Scholar 
Randall CJ, Negri AP, Quigley KM, Foster T, Ricardo GF, Webster NS, et al. Sexual production of corals for reef restoration in the Anthropocene. Mar Ecol Prog Ser. 2020;635:203–32.Article 

Google Scholar 
Bay LK, Cumbo VR, Abrego D, Kool JT, Ainsworth TD, Willis BL. Infection dynamics vary between Symbiodinium types and cell surface treatments during establishment of endosymbiosis with coral larvae. Diversity. 2011;3:356–74.Article 

Google Scholar 
Abrego D, van Oppen MJH, Willis BL. Highly infectious symbiont dominates initial uptake in coral juveniles. Mol Ecol. 2009;18:3518–31.Article 

Google Scholar 
Cunning R, Silverstein RN, Baker AC. Investigating the causes and consequences of symbiont shuffling in a multi-partner reef coral symbiosis under environmental change. Proc R Soc B: Biol Sci. 2015;282:20141725.Chamberland VF, Petersen D, Latijnhouwers KRW, Snowden S, Mueller B, Vermeij MJA. Four-year-old Caribbean Acropora colonies reared from field-collected gametes are sexually mature. Bull Mar Sci. 2016;92:263–4.Silverstein RN, Correa AMS, Baker AC. Specificity is rarely absolute in coral–algal symbiosis: implications for coral response to climate change. Proc R Soc B: Biol Sci. 2012;279:2609–18.Article 

Google Scholar  More

Laboratory cultureOchrosphaera neapolitana (RCC1357) was precultured in K/2 medium without Tris buffer8 using artificial seawater (ASW) supplemented with NaHCO3 and HCl to yield an initial DIC of 2050 µM. In triplicate, 1-L bottles were filled with 150 mL of seawater medium with air in the bottle headspace and inoculated with a mid-log phase preculture at an initial cell concentration of 104 cells mL−1. Cultures were grown at 18 °C under a warm white LED light at 100 ± 20 µE on a 16h-light/8h-dark cycle. Bottles were orbitally shaken at 60 rpm to keep cells in suspension. Cell growth was monitored with a Multisizer 4e particle counter and sizer (Beckman Coulter). At ~1.4 × 105 cells mL−1, cells were diluted up to 300 mL to 2–3 × 104 cells mL−1 and harvested after 2 days of more exponential growth up to 7.9 ± 0.6 × 104 cells mL−1. More detailed culture results are listed in the Supplementary Note 1.Immediately after harvesting, pH was measured using a pH probe calibrated with Mettler Toledo NBS standards (it should be noted here that high ionic strength calibration standards would be optimal for pH measurement of liquids like seawater). There was a carbonate system shift during the batch culture and more details are shown in Supplementary Fig. S1. Cells in 50 mL were pelleted by centrifuging at ~1650 × g for 5 min. Seawater supernatant was analyzed for DIC and δ13CDIC by injecting 3.5 mL into an Apollo analyzer and injecting 1 mL into He-flushed glass vials containing H3PO4 for the Gas Bench.For seawater DIC, an Apollo SciTech DIC-C13 Analyzer coupled to a Picarro CO2 analyzer was calibrated with in-house NaHCO3 standards dissolved in deionized water at different known concentrations and δ13C values from −4.66 to −7.94‰. δ13CDIC in media were measured with a Gas Bench II with an autosampler (CTC Analytics AG, Switzerland) coupled to ConFlow IV Interface and a Delta V Plus mass spectrometer (Thermo Fischer Scientific). Pelleted cells were snap-frozen with N2 (l) and stored at −80 °C. For PIC analysis, pellet was resuspended in 1 mL methanol and vortexed. After centrifugation, the methanol phase with extracted organics was removed and the pellet containing the coccoliths was dried at 60 °C overnight. About 300 mg of dried coccolith powder were placed in air-tight glass vials, flushed with He and reacted with five drops of phosphoric acid at 70 °C. PIC δ13C and δ18O were measured by the same Gas Bench system. The system and abovementioned in-house standards were calibrated using international standards NBS 18 (δ13C = −5.01‰, δ18O = +23.00‰) and NBS 19 (δ13C = +1.95‰, δ18O = +2.2‰). The analytical error for DIC concentration and δ13C is More