Ecology

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Bastin, J. F. et al. The global tree restoration potential. Science 364, 76–79 (2019).Article 
CAS 

Google Scholar 
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Restoring natural forests is the best way to remove atmospheric carbon. Nature 568, 25–28 (2019).CAS 
Article 

Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
Article 

Google Scholar 
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
Article 

Google Scholar 
Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen-phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Camenzind, T., Httenschwiler, S., Treseder, K. K., Lehmann, A. & Rillig, M. C. Nutrient limitation of soil microbial processes in tropical forests. Ecol. Monogr. 88, 4–21 (2018).Article 

Google Scholar 
Hou, E., Luo, Y., Kuang, Y., Chen, C. & Wen, D. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. & Follstad Shah, J. J. Ecoenzymatic stoichiometry and ecological theory. Annu. Rev. Ecol. Evol. Syst. 43, 313–343 (2012).Article 

Google Scholar 
Houghton, R. A. Balancing the global carbon budget. Annu. Rev. Earth Planet. Sci. 35, 313–347 (2007).CAS 
Article 

Google Scholar 
Chen, J. et al. Differential responses of carbon-degrading enzyme activities to warming: implications for soil respiration. Global Change Biol. 24, 4816–4826 (2018).Article 

Google Scholar 
Waring, B. G., Weintraub, S. R. & Sinsabaugh, R. L. Ecoenzymatic stoichiometry of microbial nutrient acquisition in tropical soils. Biogeochemistry 117, 101–113 (2014).CAS 
Article 

Google Scholar 
Mori, T., Lu, X., Aoyagi, R. & Mo, J. Reconsidering the phosphorus limitation of soil microbial activity in tropical forests. Funct. Ecol. 32, 1145–1154 (2018).Article 

Google Scholar 
Gallardo, A. & Schlesinger, W. H. Factors limiting microbial biomass in the mineral soil and forest floor of a warm-temperate forest. Soil Biol. Biochem. 26, 1409–1415 (1994).Article 

Google Scholar 
Feng, J. et al. Coupling and decoupling of soil carbon and nutrient cycles across an aridity gradient in the drylands of northern China: Evidence from ecoenzymatic stoichiometry. Global Biogeochem. Cycles. 33, 559–569 (2019).CAS 

Google Scholar 
Cui, Y. et al. Patterns of soil microbial nutrient limitations and their roles in the variation of soil organic carbon across a precipitation gradient in an arid and semi-arid region. Sci. Total Environ. 658, 1440–1451 (2019).CAS 
Article 

Google Scholar 
Jing, X. et al. Soil microbial carbon and nutrient constraints are driven more by climate and soil physicochemical properties than by nutrient addition in forest ecosystems. Soil Biol. Biochem. 141, 107657 (2020).CAS 
Article 

Google Scholar 
Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).Article 
CAS 

Google Scholar 
Zhou, L. et al. Soil extracellular enzyme activity and stoichiometry in China’s forests. Funct. Ecol. 34, 1461–1471 (2020).Article 

Google Scholar 
Fang, J., Chen, A., Peng, C., Zhao, S. & Ci, L. Changes in forest biomass carbon storage in China between 1949 and 1998. Science 292, 2320–2322 (2001).CAS 
Article 

Google Scholar 
Zhu, J. et al. Carbon stocks and changes of dead organic matter in China’s forests. Nat. Commun. 8, 1–10 (2017).Article 
CAS 

Google Scholar 
Fang, J., Yu, G., Liu, L., Hu, S. & Chapin, F. S. Climate change, human impacts, and carbon sequestration in China. Proc. Natl. Acad. Sci. USA 115, 4015–4020 (2018).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of soil enzyme activity at global scale. Ecol. Lett. 11, 1252–1264 (2008).Article 

Google Scholar 
Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).CAS 
Article 

Google Scholar 
Moorhead, D. L., Sinsabaugh, R. L., Hill, B. H. & Weintraub, M. N. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. Soil Biol. Biochem. 93, 1–7 (2016).CAS 
Article 

Google Scholar 
Cui, Y. et al. Stoichiometric models of microbial metabolic limitation in soil systems. Global Ecol. Biogeogr. 30, 2297–2311 (2021).Article 

Google Scholar 
Elser, J. J. et al. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine, and terrestrial ecosystems. Ecol. Lett. 10, 1135–1142 (2007).Article 

Google Scholar 
Schulte-Uebbing, L. & Vries, W. D. Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: a meta-analysis. Global Change Biol. 24, 416–431 (2017).Article 

Google Scholar 
Richardson, S. J., Peltzer, D. A., Allen, R. B. & Parfitt, M. G. L. Rapid development of phosphorus limitation in temperate rainforest along the Franz josef soil chronosequence. Oecologia 139, 267–276 (2004).Article 

Google Scholar 
Augusto, L., Achat, D. L., Jonard, M., Vidal, D. & Ringeval, B. Soil parent material-a major driver of plant nutrient limitations in terrestrial ecosystems. Global Change Biol. 23, 3808–3824 (2017).Article 

Google Scholar 
Yao, Q. et al. Community proteogenomics reveals the systemic impact of phosphorus availability on microbial functions in tropical soil. Nat. Ecol. Evol. 2, 499–509 (2018).Article 

Google Scholar 
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799 (2013).CAS 
Article 

Google Scholar 
Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. New Phytol. 198, 656–669 (2013).CAS 
Article 

Google Scholar 
Cui, Y. et al. Ecoenzymatic stoichiometry and microbial nutrient limitation in rhizosphere soil in the arid area of the northern Loess Plateau, China. Soil Biol. Biochem. 116, 11–21 (2018).CAS 
Article 

Google Scholar 
Cui, Y. et al. Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region. Soil Biol. Biochem. 147, 107814 (2020).CAS 
Article 

Google Scholar 
Johnson, J. et al. The response of soil solution chemistry in european forests to decreasing acid deposition. Global Change Biol. 24, 3603–3619 (2018).Article 

Google Scholar 
Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).CAS 
Article 

Google Scholar 
Penuelas, J. et al. Human-induced nitrogen-phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 1–10 (2013).
Google Scholar 
Yu, G. et al. Stabilization of atmospheric nitrogen deposition in china over the past decade. Nat. Geosci. 12, 424–429 (2019).CAS 
Article 

Google Scholar 
Cui, Y. et al. Decreasing microbial phosphorus limitation increases soil carbon release. Geoderma 419, 115868 (2022).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L., Moorhead, D. L., Xu, X. & Litvak, M. E. Plant, microbial and ecosystem carbon use efficiencies interact to stabilize microbial growth as a fraction of gross primary production. New Phytol. 214, 1518–1526 (2017).CAS 
Article 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Global Change Biol. 27, 2633–2644 (2021).CAS 
Article 

Google Scholar 
Friggens, N. L., Hester, A. J., Mitchell, R. J., Parker, T. C. & Wookey, P. A. Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. Global Change Biol. 26, 5178–5188 (2020).Article 

Google Scholar 
Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
Article 

Google Scholar 
Rosinger, C., Rousk, J. & Sandén, H. Can enzymatic stoichiometry be used to determine growth-limiting nutrients for microorganisms?-A critical assessment in two subtropical soils. Soil Biol. Biochem. 128, 115–126 (2019).CAS 
Article 

Google Scholar 
Mori, T. Does ecoenzymatic stoichiometry really determine microbial nutrient limitations? Soil Biol. Biochem. 146, 107816 (2020).CAS 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term nitrogen deposition on extracellular enzyme activity in an acer saccharum, forest soil. Soil Biol. Biochem. 34, 1309–1315 (2002).CAS 
Article 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).CAS 
Article 

Google Scholar 
Lindstrom, M. J. & Bates, D. M. Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data. J. Am. Stat. Assoc. 83, 1014–1022 (1988).
Google Scholar 
Legendre, P. & Legendre, L. Numerical ecology, 2nd English edition. Elsevier Science BV, Amsterdam (1998).Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R News 8/1, 20–25 (2008).
Google Scholar 
Toms, J. D. & Lesperance, M. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84, 2034–2041 (2003).Article 

Google Scholar 
Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

Google Scholar 
Breiman, L. Random forests. Machine Learning 45, 5–32 (2001).Article 

Google Scholar 
Sanchez, G., Trinchera, L. & Russolillo, G. plspm: Tools for Partial Least Squares Path Modeling (PLS-PM). R package version 0.4.7 edn (2016).Development Core Team R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). More

This study presents a comprehensive quantification of carbon sequestration as well as CO2/CH4/N2O emissions reductions from terrestrial ecosystems based on multiple sources of data from literature, inventories, public databases and documents. The pathways considered ecosystem restoration and protection from being converted into cropland or built-up areas, reforestation, management with improved nitrogen use in cropland, restricted deforestation, grassland recovery, reducing risk from forest wildfire and others. Here we describe the cross-cutting methods that apply across all 16 NCS pathways. The definitions, detailed methods and data sources for evaluating individual pathways can be found in the Supplementary Information.Cross-cutting methodsBaseline settingWe set 2000 as the base year because the large-scale national ecological projects, such as the Grain for Green Project, were started since then. We first evaluate the historical mitigation capacity during 2000–2020, which is the first 20 years of implementing the projects. From this procedure we can determine how much mitigation capacity has been realized through the previous projects in the past two decades and to what extent additional actions can be made after 2020. Relative to the baseline 2000–2020, we then evaluate the maximum potentials of the NCS mitigation in the future 10 (2020–2030) and 40 (2020–2060) years, corresponding to the timetable of China’s NDCs: carbon peak before 2030 and carbon neutrality by 2060.The settings of baseline in this study are different from the existing assessments (2000s–2010s as a baseline and 2010–2025/2030/2050 as scenarios)1,22,23,27,28. Baseline sets the temporal and spatial reference for NCS pathway scenarios, which may have a great impact on the NCS estimates. Notably, NCS actions during 2000–2020 will have a great impact in the future periods, which we refer to as the ‘legacy effect’. The legacy effect itself, mainly reforestation, is independent of being assessed, but it is conceptually attributed to natural flux and excluded from future NCS potential estimates.Maximum potentialThe MAMP refers to the additional CO2 sequestration or avoided GHG emissions measured in CO2 equivalents (CO2e) at given flux rates in a period on the maximum extent to which the stewardship options are applied (numbers are expressed as TgCO2e yr−1 for individual pathways and PgCO2e yr−1 for national total) (Extended Data Fig. 1 and Supplementary Table 2). ‘Additional’ means mitigation outcomes due to human actions taken beyond business-as-usual land-use activities (since 2020) and excluding existing land fluxes not attributed to direct human activities1. The MAMP of CH4 and N2O are accounted by three cropland and wetland pathways (cropland nutrient management, improved rice cultivation and peatland restoration). We adopt 100 yr global warming potential to calculate the warming equivalent for CH4 (25) and N2O (298), respectively38,39 because these values are used in national GHG inventories, although some researchers have argued that using the fixed 100 yr global warming potential to calculate the warming equivalents may be problematic because they cannot differentiate the contrasting impacts of the long- and short-lived climate pollutants39. Because the flux rate of the GHG by ecosystems may vary with the time of recovery or growth, the MAMP may also change for different periods even given the same extent.The ‘maximum’ is constrained by varied factors across the NCS pathways. We constrain forest and grassland restoration by the rate of implementation, farmland red line and tree surviving rate (Extended Data Fig. 2). Surviving rate here is the ratio of the area with increased vegetation cover due to reforestation to the total reforestation area. The farmland red line refers to ‘the minimum area of cultivated land’ given by the Ministry of Land and Resources of China. It defines the lowest limit, and the current red line is ~120 Mha. It is a rigid constraint below which the total amount of cultivated land cannot be reduced. From this total amount, there is provincial farmland red line. This red line sets a constraint on the implementation of the NCS pathways associated with land-use change. We set the future scenario of farmland area that can be used for grassland or forest restoration on the basis of the provincial farmland red line. Basic farmland is closely related to national food security. By 2050, China’s population is predicted to decrease slightly, but with economic development, the per capita demand for food may increase40. We assume that the food production in the future can meet the food demand via increasing agricultural investment and technological advancement. The N fertilizer reduction scenario is set to be below the level 60%, under which crop yield is not significantly affected19, because N fertilizer is surplus in many Chinese croplands. For timber production, we assume that the demand for timber can be met if the production level is maintained at the level of 2010–2020 (83.31 million m3 yr−1). As deforestation of natural forests is 100% forbidden since 2020, the future timber will come mainly from tree plantations. For grazing optimization, we assume that livestock production is not affected by grassland fencing due to refined livestock management such as improving feed nutrient and fine-seed breeding41.The areas of historical NCS implementation during 2000–2020 were estimated using statistical data, published literature and public documents, with a supplement from remote-sensing data. The flux rates were obtained either by directly using the values from multiple literature sources or from estimates using the empirical formulae. For the estimates of future NCS potential, the flux rate and extent of the pathway were determined on the basis of the baseline (2000–2020). The extent is assumed to be achieved by using the same rate but limited by the multiple constraints stated in the preceding unless the implementation scopes have been reported in national planning documents. We estimate the legacy effect by multiplying the implementation area in the past by the flux rates in the future two periods.SaturationThe future mitigation potential that we estimate for 2030 and 2060 will not persist indefinitely because the finite potential for natural ecosystems to store additional carbon will saturate. For each NCS pathway, we estimate the expected duration of the potential for sequestration at the maximum rate (Supplementary Table 3). Forests can continue to sequester carbon for 70–100 years or more. Restored grasslands and fenced grasslands can continue to sequester carbon for >50 years. Forest-fire management and cover crops can continue to sequester carbon for 40–50 years or more. Sea grasses and peatlands can continue to sequester carbon for millennia. Avoided pathways do not saturate as long as the business-as-usual cases indicate that there are potential areas for avoided losses of ecosystems. In this case, sea grass and salt marsh would disappear entirely after 64 years, but it would be 100–300 years or more for forest, grassland and peatland.Estimation of uncertaintiesThe extent (area or biomass amount) and flux (sequestration or reduced emission per area or biomass amount in unit time) are considered to estimate uncertainty of the historical mitigation capacity or future potential for each NCS pathway. We use the IPCC approaches to combine uncertainty42. Where mean and standard deviation can be estimated from collected literature, 95% CIs are presented on the basis of multiple published estimates. Where a sample of estimates is not available but only a range of a factor, we report uncertainty as a range and use Monte Carlo simulations (with normal distribution and 100,000 iterations) to combine the uncertainties of extent and flux (IPCC Approach 2). The overall uncertainties of the 16 NCS pathways were combined using IPCC Approach 142. If the extent estimate is based on a policy determination, rather than an empirical estimate of biophysical potential, we do not consider it a source of uncertainty.MACsThe economic/cost constraints refer to the amount of NCS that can be achieved at a given social cost. The MAC curve is fitted according to the total publicly funded investment and total mitigation capacity or potential during a period. The MAC curves are drawn to estimate the historical mitigation or MAMP at the cost thresholds of US$10, US$50 and US$100 (MgCO2e)−1, respectively. The trading price in China’s current carbon market is ~US$10 USD (as the minimum cost43), and the cost-effective price point44,45 to achieve the Paris Agreement goal of limiting global warming to below 2 °C above pre-industrial levels is US$100 (as the maximum cost). A carbon price of US$50 is regarded as a medium value1,46. For the pathways of reforestation, avoided grassland conversion, grazing optimization and grassland restoration, we collected the statistical data of investments in China from 2000 to 2020 and estimated the affordable MAMP below the three mitigation costs. Due to data limitations, the points used for fitting the MAC curve are values for cost (invested funds) and benefit (mitigation capacity) in each of the provinces. We rank the ratio of benefit to cost in a descending order to obtain the maximum marginal benefit for MAC by assuming that NCS measures are first implemented in the region with the highest cost/benefit rate. We refer to the investment standard before 2020 as the benchmark and estimate the cost of each pathway for the future periods with discount rates of 3% and 5%, respectively. The social discount rate 4–6% is usually used as a benchmark discount value in carbon price studies in China compared with lower scenarios (for example, 3.6%)46,47. In a global study for estimating country-level social cost of carbon, 3% and 5% are used for scenario analysis48. Note that the mean value from the two discount rates was used in presenting the results. For the other pathways where investment data cannot be obtained, we refer to relevant references to estimate MAC. All the cost estimates are expressed in 2015 dollars, transformed on the basis of the Renminbi and US dollar exchange rate of the same year. The year 2015 represents a relatively stable condition of economic increase over the past decade (2011–2020) in China (the increase rate of gross domestic product (GDP) in 2015 is similar to the 10 yr mean). In the cases when the MAC curves exceed the estimated maximum potentials in the period, we identify the historical capacity or the MAMP as limited by the biophysical estimates.Additional mitigation required to meet Paris Agreement NDCsOn 28 October 2021, China officially submitted ‘China’s Achievements, New Goals and New Measures for Nationally Determined Contributions’ (‘New Measures 2021’ hereafter) and ‘China’s Mid-Century Long-Term Low Greenhouse Gas Emission Development Strategy’ to the Secretariat of the United Nations Framework Convention on Climate Change as an enhanced strategy to China’s updated NDCs (first submission in 2015). The goal of China’s updated NDCs is to strive to peak CO2 emissions before 2030 and achieve carbon neutralization by 2060. It specified the goals to include the following: before 2030, China’s carbon dioxide emissions per unit of GDP are expected be more than 65% lower than that in 2005, and the forest stock volume is expected to be increased by around 6.0 (previously 4.5) billion m3 over the 2005 level. In the ‘New Measures 2021’9 and ‘Master Plan of Major Projects of National Important Ecosystem Protection and Restoration (2021–2035)’5, many NCS-related opportunities are proposed to consolidate the carbon sequestration of ecosystems and increase the future NCS potential, including protecting the existing ecosystems, implementing engineering to precisely improve forest quality, continuously increasing forest area and stock volume, strengthening grassland protection and recovery and wetland protection and improving the quality of cultivated land and the agricultural carbon sinks.Industrial CO2 emissionsThe historical CO2 emissions data from 2000 to 201749,50 are used as the benchmark of industrial CO2 emissions during 2000–2020. For future projections, we use the peak value of the A1B2C2 scenario (in the range of 10,000 to 12,000 Mt) in 2030 from ref. 11. We assume that CO2 emission increases linearly from 2017 to 2030.Characterizing co-benefitsNCS activities proposed in the future measures or plans may enhance co-benefits. Four generalized types of ecosystem services are identified: improving biodiversity, water-related, soil-related and air-related ecosystem services (Fig. 1). Biodiversity benefits refer to the increase in different levels of diversity (alpha, beta and/or gamma diversity)51. Water, soil and air benefits refer to flood regulation and water purification, improved fertility and erosion prevention, and improvements in air quality, respectively, as defined in the Millennium Ecosystem Assessment52. The evidence that each pathway produces co-benefits from one or more peer-reviewed publications was collected through reviewing the literature (see the details for co-benefits of each pathway in Supplementary Information).Mapping province-level mitigationThe data for extent of implementing forest pathways are obtained from the statistical yearbook and reported at the province level. To be consistent with the forest pathways, the other pathways were also aggregated to the provincial-level estimate from the spatial data. If the flux data were available in different climate regions, the provinces are first assigned to climate regions. When a province spans multiple climate zones, the weight value is set according to the proportion of area, and finally an estimated value of rate was calculated (for fire management, some grassland and wetland pathways). For the forest pathways, we first collected the flux-rate data from reviewing literature and then averaged these flux rates to region/province. The flux rates for reforestation and natural forest management were calculated separately by province and age group. Similarly, specified flux rates are applied for different times after ecosystem restoration or conversion for other pathways.Classification of NCS typesThree types of NCS pathways were classified: protection (of intact natural ecosystems), improved management (on managed lands) and restoration (of native cover)35. In our study, four (AVFC, AVGC, AVCI, AVPI), eight (IMP, NFM, FM, BIOC, CVCR, CRNM, IMRC, GROP) and four (RF, GRR, CWR, PTR) NCS pathways were identified as protection, management and restoration types, respectively (Supplementary Table 1). These pathways can be further divided into groups of ‘single’ type or ‘mixed’ type according to their contribution to individual pathways. Specifically, in a certain area, when the mitigation capacity of a certain pathway accounts for more than 50% of the total, it is regarded as a single or dominant NCS type; if no single pathway accounts for more than 50%, it is a mixed type, named by the top pathways whose NCS sum exceeds 50% of the total mitigation capacity. More

Uchida, H. & Baba, O. Fishery management and the pooling arrangement in the Sakura ebi fishery in Japan, 175–189. https://www.fao.org/3/a1497e/a1497e16.pdf (2008).Omori, M. The biology of a sergestid shrimp Sergestes lucens Hansen. Bull. Ocean Res. Inst. Univ. Tokyo 4, 1–83 (1969).
Google Scholar 
Gurney, R. & Lebour, M. V. Larvae of decapod crustacea. Part VI. The genus Sergestes. Discov. Rep. 20, 1–68 (1940).
Google Scholar 
Holthuis, L. B. FAO species catalogue. Vol. 1. Shrimps and prawns of the world. An annotated catalogue of species of interest to fisheries. FAO Fish. Synop. Vol. 125, 1–271 (1980).Omori, M., Ukishima, Y. & Muranaka, F. New record of occurrence of Sergia lucens (Hansen) (Crustacea, Sergestidae) off Tung-kang, Taiwan, with special reference to phylogeny and distribution of the species. J. Oceanogr. Soc. Jpn. 44, 261–267 (1988) (in Japanese with English abstract).Article 

Google Scholar 
Isshiki, T. & Tajima, Y. The research of a sergestid shrimp, Sergia lucens (Hansen) in the mouth of Tokyo Bay I. The seasonal distribution of adult and the distribution of eggs. Bull. Kanagawa Pref. Fish. Exp. Stn. 13, 73–78 (1992) (in Japanese with English abstract).
Google Scholar 
Lee, D. A., Wu, S. H., Liao, I. C. & Yu, H. P. On three species of commercially important sergestid shrimps (Decapoda: Sergestidae) in the coastal waters of Taiwan. J. Taiwan Fish. Res. Inst. 4, 1–19 (1996) (in Chinese with English abstract).CAS 

Google Scholar 
Yinji, L. & Ratana, C. Governing in an uncertain time: The case of Sakura shrimp fishery, Japan. Marit. Stud. 20, 115–126 (2021).Article 

Google Scholar 
Isono, R. S., Kita, J. & Setoguma, T. Acute effects of kaolinite suspension on eggs and larvae of some marine teleosts. Comp. Biochem. Physiol. Part C 120, 449–455 (1998).CAS 
Article 

Google Scholar 
Aoki, S. & Oinuma, K. Distribution of clay minerals in surface sediments of Suruga Bay, central Japan. J. Geol. Soc. Jpn. 87(7), 429–438 (1981) (in Japanese with English abstract).Article 

Google Scholar 
Nasnodkar, M. R. & Ganapati, N. N. Clay mineralogy and chemistry of mudflat core sediments from Sharavathi and Gurupur estuaries: Source and processes. Indian J. Geo-Mar. Sci. 48(3), 379–388 (2019).
Google Scholar 
Capper, N. The effects of suspended sediment on the aquatic organisms Daphnia magna and Pimephales promelas. All Theses. 2. https://tigerprints.clemson.edu/all_theses/2 (2006).Boyd, M. B. et al. Disposal of dredge spoil, problem identification and assessment and research program development. Technical report H-72–8, U.S. army engineer waterways experiment station, CE, Vicksburg, Miss. (1972).McFarland, V. A. & Peddicord, R. K. Lethality of a suspended clay to a diverse selection of marine and estuarine macrofauna. Arch. Environ. Contam. Toxicol. 9, 733–741 (1980).CAS 
Article 

Google Scholar 
Arakawa, H. et al. The influence of suspended particles on larval development in the Manila clam Ruditapes philippinarum. Sci. Postp. 1, e00028. https://doi.org/10.14340/spp.2014.08A0002 (2014).Article 

Google Scholar 
Davis, H. C. Effects of turbidity-producing materials in sea water on eggs and larvae of the clam (Venus (Mercenaria) mercenaria). Biol. Bull. 118, 48–54 (1960).Article 

Google Scholar 
Tabata, A., Morinaga, T. & Arakawa, H. Influences of concentration, particle-size and kind of inorganic suspended matter on feed caught by Manila clam, Ruditapes philippinarum. La Mer 37, 163–171 (2000).CAS 

Google Scholar 
Annisa, Dwiatmoko, M. U., Saismana, U. & Maulanai, R. Characteristics of kaolin clay on Alluvial formation subdistrict mataraman based on physical properties and chemical properties. In MATEC Web of Conferences Vol. 280, 03009. https://doi.org/10.1051/matecconf/201928003009 (2019).Murray, H. H. Structure and composition of clay minerals and their physical and chemical properties. Dev. Clay Sci. 2, 7–31. https://doi.org/10.1016/S1572-4352(06)02002-2 (2006).Article 

Google Scholar 
Kumari, N. & Mohan, C. Basics of clay minerals and their characteristic properties. Clay Clay Miner. 1–29 (2021).Lively, J. S., Kaufman, Z. & Carpenter, E. J. Phytoplankton ecology of a barrier island estuary: Great South Bay, New York. Estuar. Coast. Shelf Sci. 16(1), 51–68 (1983).ADS 
Article 

Google Scholar 
Lloyd, D. S. Turbidity as a water quality standard for salmonid habitats in Alaska. N. Am. J. Fish. Manag. 7, 34–45 (1987).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshw. Biol. 28, 103–109 (1992).Article 

Google Scholar 
McCabe, G. D. & O’Brien, W. J. The effects of suspended silt on feeding and reproduction of Daphnia pulex. Am. Midl. Nat. 110, 324–337 (1983).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic Rotifers and Cladocerans. Ecology 71, 1741–1755 (1990).Article 

Google Scholar 
Loosanoff, V. L. Effects of turbidity on some larval and adult bivalves. Proc. Gulf. Carib. Fish. Inst. 14, 80–95 (1961).
Google Scholar 
Arruda, J. A., Marzolf, G. R. & Faulk, R. T. The role of suspended sediments in the nutrition of zooplankton in turbid reservoirs. Ecology 64, 1225–1235 (1983).Article 

Google Scholar 
Kathyayani, S. A., Muralidhar, M., Kumar, T. S. & Alavandi, S. V. Stress quantification in Penaeus vannamei exposed to varying levels of turbidity. J. Coast. Res. 86, 177–183 (2019).CAS 
Article 

Google Scholar 
Wilber, D. H. & Clarke, D. G. Biological effects of suspended sediments: A review of suspended sediment impacts on fish and shellfish with relation to dredging activities in estuaries. N. Am. J. Fish. Manag. 21, 855–875 (2001).Article 

Google Scholar 
Lin, H., Charmantier, G., Thuet, P. & Trilles, J. Effects of turbidity on survival, osmoregulation, and gill Na+-K+ ATPase in juvenile shrimp Penaeus japonicus. Mar. Ecol. Prog. Ser. 90, 31–37 (1992).ADS 
CAS 
Article 

Google Scholar 
Davis, H. C. & Hidu, H. Effects of turbidity-producing substances in sea water on eggs and larvae of three genera of bivalve mollusks. Veliger 11, 316–323 (1969).
Google Scholar 
Nimmo, D. R., Hamaker, T. L., Matthews, E. & Young, W. T. The long-term effects of suspended particulates on survival and reproduction of the mysid shrimp, Mysidopsis bahia, in the laboratory. In Proceedings of a Symposium on the Ecological Effects of Environmental Stress, New York, 413–422 (1979).Peddicord, R. & McFarland, V. Effects of suspended dredged material on the commercial crab, Cancer magister. In Proceedings of the Specialty Conference on Dredging and Its Environmental Effects, Mobile, Alabama, 633–644 (1976).Peddicord, R. K. Direct Effects of Suspended Sediments on Aquatic Organisms. Contaminants and Sediments. Volume 1. Fate and Transport, Case Studies, Modeling, Toxicity 501–536 (Ann Arbor Science Publishers, 1980).
Google Scholar 
Wakeman, T., Peddicord, R. & Sustar, J. Effects of suspended solids associated with dredging operations on estuarine organisms. In Ocean 75 conference, 431–436 (1975).Gebauer, P., Walter, I. & Anger, K. Effects of substratum and conspecific adults on the metamorphosis of Chasmagnathus granulata (Dana) (Decapoda: Grapsidae) megalopae. J. Exp. Mar. Biol. Ecol. 223, 185–198 (1998).Article 

Google Scholar 
Carvalho, L. & Calado, R. Trade-offs between timing of metamorphosis and grow out performance of a marine caridean shrimp juveniles and its relevance for aquaculture. Aquaculture 492, 97–102 (2018).Article 

Google Scholar 
Calado, R. et al. The physiological consequences of delaying metamorphosis in the marine ornamental shrimp Lysmata seticaudata and its implications for aquaculture. Aquaculture 546, 737391. https://doi.org/10.1016/j.aquaculture.2021.737391 (2022).Article 

Google Scholar 
Murphy, R. C. Factors affecting the distribution of the introduced bivalve, Mercenaria mercenaria, in a California lagoon—The importance of bioturbation. J. Mar. Res. 43, 673–692 (1985).Article 

Google Scholar 
Bricelj, V. M. & Malouf, R. E. Influence of algal and suspended sediment concentration on the feeding physiology of the hard clam Mercenaria mercenaria. Mar. Biol. 84, 155–165 (1984).Article 

Google Scholar 
Wenger, A. S., Jacob, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth, and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48 (2012).Article 

Google Scholar 
Robinson, W. E., Wehling, W. E. & Morse, M. P. The effect of suspended clay on feeding and digestive efficiency of the surf clam Spisula solidissima (Dillwyn). J. Exp. Mar. Biol. Ecol. 74, 1–12 (1984).CAS 
Article 

Google Scholar 
Turner, E. J. & Miller, D. C. Behavior and growth of Mercenaria mercenaria during simulated storm events. Mar. Biol. 111, 55–64 (1991).Article 

Google Scholar 
Grant, J. & Thorpe, B. Effects of suspended sediment on growth, respiration, and excretion of the soft-shelled clam (Mya arenaria). Can. J. Fish. Aquat. Sci. 48, 1285–1292 (1991).Article 

Google Scholar 
Gleason, R. A., Euliss, N. H., Hubbard, D. E. & Duffy, W. G. Effects of sediment load on emergence of aquatic invertebrates and plants from wetland soil egg and seed banks. Wetlands 23, 26–34 (2003).Article 

Google Scholar 
Jacek, R., Anna, S. & Miroslaw, S. The effect of lake sediment on the hatching success of Daphnia ephippial eggs. J. Limnol. 75, 597–605 (2016).
Google Scholar 
Newcombe, C. P. & McDonald, D. D. Effects of suspended sediment on aquatic ecosystems. N. Am. J. Fish. Manag. 11, 77–82 (1991).Article 

Google Scholar 
Chutter, F. M. The effects of silt and sand on the invertebrate fauna of streams and rivers. Hydrobiologia 34, 57–76 (1968).Article 

Google Scholar 
Hellawell, J. M. Biological indicators of freshwater pollution and environmental management. In Pollution Monitoring Series (ed. Melanby, K.) https://doi.org/10.1007/978-94-009-4315-5 (1986).Makita, M. & Kondo, M. Rearing of the larvae of Seigia Lucens (Hansen). Bull. Shizuoka Pref. Fish. Exp. Stn. 16, 97–105 (1982) (in Japanese).
Google Scholar  More

Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 

Google Scholar 
Leibold MA. Chase JM Metacommunity Ecology. Levin SA, Horn HS, editors: Princeton University Press, Princeton; 2018.Logue JB, Mouquet N, Peter H, Hillebrand H, Declerck P, Flohre A, et al. Empirical approaches to metacommunities: A review and comparison with theory. Trends Ecol Evol. 2011;26:482–91.PubMed 

Google Scholar 
Hanson CA, Fuhrman JA, Horner-Devine MC, Martiny JB. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat Rev Microbiol. 2012;10:497–506.CAS 
PubMed 

Google Scholar 
Lindström ES, Langenheder S. Local and regional factors influencing bacterial community assembly. Environ Microbiol Rep. 2012;4:1–9.PubMed 

Google Scholar 
Langenheder S, Lindström ES. Factors influencing aquatic and terrestrial bacterial community assembly. Environ Microbiol Rep. 2019;11:306–15.PubMed 

Google Scholar 
Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol Lett. 2004;7:601–13.
Google Scholar 
Vass M, Langenheder S. The legacy of the past: Effects of historical processes on microbial metacommunities. Aquat Micro Ecol. 2017;79:13–9.
Google Scholar 
Fukami T. Historical contingency in community assembly: Integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.
Google Scholar 
Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Natl Acad Sci. 2015;112:E1326–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang FG, Zhang QG. Patterns in species persistence and biomass production in soil microcosms recovering from a disturbance reject a neutral hypothesis for bacterial community assembly. PLoS One. 2015;10:e0126962.PubMed 
PubMed Central 

Google Scholar 
Zhou J, Deng Y, Zhang P, Xue K, Liang Y, Van Nostrand JD, et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc Natl Acad Sci. 2014;111:E836–45.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrenberg S, O’Neill SP, Knelman JE, Todd B, Duggan S, Bradley D, et al. Changes in assembly processes in soil bacterial communities following a wildfire disturbance. ISME J. 2013;7:1102–11.PubMed 
PubMed Central 

Google Scholar 
Jiang L, Morin PJ. Temperature fluctuation facilitates coexistence of competing species in experimental microbial communities. J Anim Ecol. 2007;76:660–8.PubMed 

Google Scholar 
Tucker CM, Fukami T. Environmental variability counteracts priority effects to facilitate species coexistence: evidence from nectar microbes. Proc Biol Sci. 2014;281:20132637.PubMed 
PubMed Central 

Google Scholar 
Grainger TN, Letten AD, Gilbert B, Fukami T. Applying modern coexistence theory to priority effects. Proc Natl Acad Sci. 2019;116:6205–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiang L, Patel SN. Community assembly in the presence of disturbance: A microcosm experiment. Ecology 2008;89:1931–40.PubMed 

Google Scholar 
Loeuille N, Leibold MA. Evolution in metacommunities: On the relative importance of species sorting and monopolization in structuring communities. Am Nat. 2008;171:788–99.PubMed 

Google Scholar 
Shade A, Jones SE, McMahon KD. The influence of habitat heterogeneity on freshwater bacterial community composition and dynamics. Environ Microbiol. 2008;10:1057–67.CAS 
PubMed 

Google Scholar 
Pereira CL, Araújo MB, Matias MG. Interplay between productivity and regional species pool determines community assembly in aquatic microcosms. Aquat Sci. 2018;80:45.
Google Scholar 
Herlemann DP, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Neubauer SC, Piehler MF, Smyth AR, Franklin RB. Saltwater intrusion modifies microbial community structure and decreases denitrification in tidal freshwater marshes. Ecosystems. 2018;22:912–28.
Google Scholar 
Rath KM, Fierer N, Murphy DV, Rousk J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 2019;13:836–46.CAS 
PubMed 

Google Scholar 
Tang X, Xie G, Shao K, Tian W, Gao G, Qin B. Aquatic bacterial diversity, community composition and assembly in the semi-arid Inner Mongolia Plateau: combined effects of salinity and nutrient levels. Microorganisms. 2021;9:208.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xia LC, Steele JA, Cram JA, Cardon ZG, Simmons SL, Vallino JJ, et al. Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Syst Biol. 2011;5:S15.PubMed 
PubMed Central 

Google Scholar 
Langenheder S, Comte J, Zha Y, Samad MS, Sinclair L, Eiler A, et al. Remnants of marine bacterial communities can be retrieved from deep sediments in lakes of marine origin. Environ Microbiol Rep. 2016;8:479–85.CAS 
PubMed 

Google Scholar 
Comte J, Lindström ES, Eiler A, Langenheder S. Can marine bacteria be recruited from freshwater sources and the air? ISME J. 2014;8:2423–30.PubMed 
PubMed Central 

Google Scholar 
Comte J, Langenheder S, Berga M, Lindström ES. Contribution of different dispersal sources to the metabolic response of lake bacterioplankton following a salinity change. Environ Microbiol. 2017;19:251–60.CAS 
PubMed 

Google Scholar 
Langenheder S, Ragnarsson H. The role of environmental and spatial factors for the composition of aquatic bacterial communities. Ecology 2007;88:2154–61.PubMed 

Google Scholar 
del Giorgio PA, Bird DF, Prairie YT, Planas D. Flow cytometric determination of bacterial abundance in lakeplankton with the green nucleid acid stain SYTO 13. Limnol Oceanogr. 1996;41:783–9.
Google Scholar 
Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: Limitations and uses. ISME J. 2013;7:2061–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Székely AJ, Berga M, Langenheder S. Mechanisms determining the fate of dispersed bacterial communities in new environments. ISME J. 2013;7:61–71.PubMed 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.
Google Scholar 
Hugerth LW, Wefer HA, Lundin S, Jakobsson HE, Lindberg M, Rodin S, et al. DegePrime, a program for degenerate primer design for broad- taxonomic-range PCR in microbial ecology studies. Appl Environ Microbiol. 2014;80:5116–23.PubMed 
PubMed Central 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high- throughput sequencing reads. EMBnet J. 2011;17:10–2.
Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Chao A, Jost L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 2012;93:2533–47.PubMed 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
R-Core-Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. R package version 2.5-7. ed 2020.Bier RL Field and chemistry data from 2016 Fluctuations Project Data sets. In: DiVA, editor. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3517382016.Noguchi K, Gel YR, Brunner E, Konietschke F. nparLD: An R software package for the nonparametric analysis of longitudinal data in factorial experiments. J Stat Softw. 2012;50:1–23.
Google Scholar 
Willis A, Martin BD, Trinh P, Teichman S, Barger K, Bunge J. Breakaway: Species Richness Estimation and Modeling. R package version 4.7.3. ed. 2020.Baselga A, Orme D, Villeger S, De Bortoli J, Leprieur F, Logez M. Betapart: Partitioning beta diversity into turnover and nestedness components. R package version 1.5.2 ed. 2020.Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). In: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL, editors. Wiley StatsRef: Statistics Reference Online: John Wiley & Sons, Inc; 2017. p. 1–15.Jabot F, Laroche F, Massol F, Arthaud F, Crabot J, Dubart M, et al. Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition. Ecol Lett. 2020;23:1330–9.PubMed 

Google Scholar 
Rosseel Y. Lavaan: An R Package for Structural Equation Modeling. J Stat Softw. 2012;48:1–36.
Google Scholar 
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
Assenov Y, Ramirez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics 2008;24:282–4.CAS 
PubMed 

Google Scholar 
Drake JA. Community-assembly mechanics and the structure of an experimental species ensemble. Am Nat. 1991;137:1–26.
Google Scholar 
Orrock JL, Fletcher RL Jr. Changes in community size affect the outcome of competition. Am Nat. 2005;166:107–11.PubMed 

Google Scholar 
Fukami T. Community assembly along a species pool gradient: implications for multiple‐scale patterns of species diversity. Popul Ecol. 2004;46:137–47.
Google Scholar 
Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73:1576–85.CAS 
PubMed 
PubMed Central 

Google Scholar 
Werba JA, Stucy AL, Peralta AL, McCoy MW. Effects of diversity and coalescence of species assemblages on ecosystem function at the margins of an environmental shift. PeerJ. 2020;8:e8608.PubMed 
PubMed Central 

Google Scholar 
Logares R, Brate J, Bertilsson S, Clasen JL, Shalchian-Tabrizi K, Rengefors K. Infrequent marine-freshwater transitions in the microbial world. Trends Microbiol. 2009;17:414–22.CAS 
PubMed 

Google Scholar 
Logares R, Lindström ES, Langenheder S, Logue JB, Paterson H, Laybourn-Parry J, et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 2013;7:937–48.CAS 
PubMed 

Google Scholar 
Muylaert K, Van Der Gucht K, Vloemans N, Meester LD, Gillis M, Vyverman W. Relationship between bacterial community composition and bottom-up versus top-down variables in four eutrophic shallow lakes. Appl Environ Microbiol. 2002;68:4740–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee AM, Sæther B-E, Engen S. Spatial covariation of competing species in a fluctuating environment. Ecology 2020;101:e02901.PubMed 

Google Scholar 
Liu J, Fu B, Yang H, Zhao M, He B, Zhang XH. Phylogenetic shifts of bacterioplankton community composition along the Pearl Estuary: the potential impact of hypoxia and nutrients. Front Microbiol. 2015;6:64.PubMed 
PubMed Central 

Google Scholar 
Guiry MD, Guiry GM. AlgaeBase. World-wide electronic publication: National University of Ireland, Galway; 2022.Shade A, Jones SE, Caporaso JG, Handelsman J, Knight R, Fierer N, et al. Conditionally rare taxa disproportionately contribute to temporal changes in microbial diversity. mBio 2014;5:e01371–14.PubMed 
PubMed Central 

Google Scholar 
Andersson MGI, Berga M, Lindström ES, Langenheder S. The spatial structure of bacterial communities is influenced by historical environmental conditions. Ecology 2014;95:1134–40.PubMed 

Google Scholar 
Ai D, Gravel D, Chu C, Wang G. Spatial structures of the environment and of dispersal impact species distribution in competitive metacommunities. PLoS One. 2013;8:e68927.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maloufi S, Catherine A, Mouillot D, Louvard C, Couté A, Bernard C, et al. Environmental heterogeneity among lakes promotes hyper β-diversity across phytoplankton communities. Freshw Biol. 2016;61:633–45.
Google Scholar 
Firkowski CR, Thompson PL, Gonzalez A, Cadotte MW, Fortin M-J. Multi-trophic metacommunity interactions mediate asynchrony and stability in fluctuating environments. Ecol Monogr. n/a:e1484.Lennon JT, Jones SE. Microbial seed banks: The ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
PubMed 

Google Scholar 
Knope ML, Forde SE, Fukami T. Evolutionary history, immigration history, and the extent of diversification in community assembly. Front Microbiol. 2011;2:273.PubMed 

Google Scholar 
Fukami T. Assembly history interacts with ecosystem size to influence species diversity. Ecology 2004;85:3234–42.
Google Scholar 
Orrock JL, Watling JI. Local community size mediates ecological drift and competition in metacommunities. Proc Biol Sci. 2010;277:2185–91.PubMed 
PubMed Central 

Google Scholar 
Chase JM. Community assembly: When should history matter? Oecologia 2003;136:489–98.PubMed 

Google Scholar 
Ron R, Fragman-Sapir O, Kadmon R. Dispersal increases ecological selection by increasing effective community size. Proc Natl Acad Sci. 2018;115:11280–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Siqueira T, Saito VS, Bini LM, Melo AS, Petsch DK, Landeiro VL, et al. Community size can affect the signals of ecological drift and niche selection on biodiversity. Ecology 2020;101:e03014.PubMed 

Google Scholar 
Vass M, Székely AJ, Lindström ES, Langenheder S. Using null models to compare bacterial and microeukaryotic metacommunity assembly under shifting environmental conditions. Sci Rep. 2020;10:2455.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shen D, Langenheder S, Jürgens K. Dispersal modifies the diversity and composition of active bacterial communities in response to a salinity disturbance. Front Microbiol. 2018;9:2188.PubMed 
PubMed Central 

Google Scholar 
Cunze S, Heydel F, Tackenberg O. Are plant species able to keep pace with the rapidly changing climate? PLoS One. 2013;8:e67909.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Floyd, T. J., Mech, L. D. & Jordan, P. A. Relating wolf scat content to prey consumed. J. Wildl. Manag. 42, 528 (1978).Article 

Google Scholar 
Ackerman, B. B., Lindzey, F. G. & Hemker, T. P. Cougar food habits in Southern Utah. J. Wildl. Manag. 48, 147 (1984).Article 

Google Scholar 
Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Klare, U., Kamler, J. F. & Macdonald, D. W. A comparison and critique of different scat-analysis methods for determining carnivore diet: Comparison of scat-analysis methods. Mammal Rev. 41, 294–312 (2011).Article 

Google Scholar 
Hatton, I. A. et al. The predator-prey power law: Biomass scaling across terrestrial and aquatic biomes. Science 349, aac6284 (2015).PubMed 
Article 
CAS 

Google Scholar 
Monterroso, P. et al. Feeding ecological knowledge: The underutilised power of faecal DNA approaches for carnivore diet analysis. Mammal Rev. 49, 97–112 (2019).Article 

Google Scholar 
Hayward, M. W., O’Brien, J., Hofmeyr, M. & Kerley, G. I. H. Prey preferences of the African wild dog Lycaon Pictus (Canidae: Carnivora): Ecological requirements for conservation. J. Mammal. 87, 1122–1131 (2006).Article 

Google Scholar 
Crawford, K., Mcdonald, R. A. & Bearhop, S. Applications of stable isotope techniques to the ecology of mammals. Mammal Rev. 38, 87–107 (2008).Article 

Google Scholar 
Crossey, B., Chimimba, C., du Plessis, C., Ganswindt, A. & Hall, G. African wild dogs ( Lycaon pictus ) show differences in diet composition across landscape types in Kruger National Park, South Africa. J. Mammal. 102, 1211–1221 (2021).Article 

Google Scholar 
Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Treves, A. & Karanth, K. U. Human-carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).Article 

Google Scholar 
Swihart, R. K., Gehring, T. M., Kolozsvary, M. B. & Nupp, T. E. Responses of ‘resistant’ vertebrates to habitat loss and fragmentation: The importance of niche breadth and range boundaries. Divers. Distrib. 9, 1–18 (2003).Article 

Google Scholar 
Kamler, J. F. et al. Cuon alpinus. IUCN Red List Threat. Spec. https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T5953A72477893.en (2015).Article 

Google Scholar 
Johnsingh, A. J. T. Distribution and status of dhole Cuon alpinus Pallas, 1811 in South Asia. Mammalia 49, (1985).Acharya, B. B. Dissertation submitted to Saurashtra University, Rajkot, Gujarat, for the award of the Degree of Doctor of Philosophy in Wildlife Science. 133.Sillero-Zubiri, E. C., Hoffmann, M. & Macdonald, D. W. Canids: Foxes, Wolves, Jackals and Dogs. 443.Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Karanth, K. K., Nichols, J. D., Karanth, K. U., Hines, J. E. & Christensen, N. L. The shrinking ark: Patterns of large mammal extinctions in India. Proc. R. Soc. B Biol. Sci. 277, 1971–1979 (2010).Article 

Google Scholar 
Srivathsa, A., Karanth, K. K., Jathanna, D., Kumar, N. S. & Karanth, K. U. On a dhole trail: Examining ecological and anthropogenic correlates of dhole habitat occupancy in the Western Ghats of India. PLoS ONE 9, e98803 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Newsome, T. M. & Ripple, W. J. A continental scale trophic cascade from wolves through coyotes to foxes. J. Anim. Ecol. 84, 49–59 (2015).PubMed 
Article 

Google Scholar 
Fleming, P. J. S. et al. Roles for the Canidae in food webs reviewed: Where do they fit?. Food Webs 12, 14–34 (2017).Article 

Google Scholar 
Van Valkenburgh, B. Iterative evolution of hypercarnivory in canids (Mammalia: Carnivora): Evolutionary interactions among sympatric predators. Paleobiology 17, 340–362 (1991).Article 

Google Scholar 
Clements, H. S., Tambling, C. J., Hayward, M. W. & Kerley, G. I. H. An objective approach to determining the weight ranges of prey preferred by and accessible to the five large african carnivores. PLoS ONE 9, e101054 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hayward, M. W., Lyngdoh, S. & Habib, B. Diet and prey preferences of dholes ( C uon alpinus ): Dietary competition within A sia’s apex predator guild. J. Zool. 294, 255–266 (2014).Article 

Google Scholar 
Srivathsa, A., Sharma, S. & Oli, M. K. Every dog has its prey: Range-wide assessment of links between diet patterns, livestock depredation and human interactions for an endangered carnivore. Sci. Total Environ. 714, 136798 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Cohen, J. A. Cuon alpinus. Mamm. Spec. https://doi.org/10.2307/3503800 (1978).Article 

Google Scholar 
Srivathsa, A., Sharma, S., Singh, P., Punjabi, G. A. & Oli, M. K. A strategic road map for conserving the Endangered dhole Cuon alpinus in India. Mammal Rev. 50, 399–412 (2020).Article 

Google Scholar 
Ghaskadbi, P., Nigam, P. & Habib, B. Stranger Danger: Differential response to strangers and neighbors by a social carnivore, the Asiatic wild dog (Cuon alpinus). Behav. Ecol. Sociobiol. 76, 86. https://doi.org/10.1007/s00265-022-03188-4 (2022). Article 

Google Scholar 
Ghaskadbi, P., Das, J., Mahadev, V. & Habib, B. First record of mixed species association between dholes and a wolf from Satpura Tiger Reserve, India. Canid Biol. Conserv. 23(4): 15–17. http://www.canids.org/CBC/23/Dhole_wolf_association.pdf (2021).Wachter, B. et al. An advanced method to assess the diet of free-ranging large carnivores based on scats. PLoS ONE 7, e38066 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Edgaonkar, A. Satpura National Park, India. 135.Borah, J., Deka, K., Dookia, S. & Gupta, R. P. Food habits of dholes (Cuon alpinus) in Satpura Tiger Reserve. Madhya Pradesh, India. 73, 85–88 (2009).
Google Scholar 
Karanth, K. U. & Sunquist, M. E. Behavioural correlates of predation by tiger ( Panthera tigris ), leopard ( Panthera pardus ) and dhole ( Cuon alpinus ) in Nagarahole, India. J. Zool. 250, 255–265 (2000).Article 

Google Scholar 
Krishna, Y. C., Clyne, P. J., Krishnaswamy, J. & Kumar, N. S. Distributional and ecological review of the four horned antelope. Tetracerus quadricornis. 73, 1–6 (2009).
Google Scholar 
Sharma, K., Chundawat, R. S., Van Gruisen, J. & Rahmani, A. R. Understanding the patchy distribution of four-horned antelope Tetracerus quadricornis in a tropical dry deciduous forest in Central India. J. Trop. Ecol. 30, 45–54 (2014).Article 

Google Scholar 
Rahman, D. A., Syamsudin, M., Firdaus, A. Y. & Afriandi, H. T. Photographic record of Dholes predating on a young Banteng in southwestern Java, Indonesia. J. Threat. Taxa 13, 20278–20283 (2021).Article 

Google Scholar 
Durbin, L. S., Venkataraman, A., Hedges, S. & Dukworth, W. South Asia—south of th e Himalaya (oriental). In Canids: Foxes, Wolves, Jackals and Dogs . Status Survey and Conserva- tion Action Plan. (IUCN Canid Specialist Group, 2004).Bashir, T., Bhattacharya, T., Poudyal, K., Roy, M. & Sathyakumar, S. Precarious status of the Endangered dhole Cuon alpinus in the high elevation Eastern Himalayan habitats of Khangchendzonga Biosphere Reserve, Sikkim, India. Oryx 48, 125–132 (2014).Article 

Google Scholar 
Yoshimura, H., Hirata, S. & Kinoshita, K. Plant-eating carnivores: Multispecies analysis on factors influencing the frequency of plant occurrence in obligate carnivores. Ecol. Evol. 11, 10968–10983 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Snake-in-the-diet-of-Cuon-alpinus-Pallas-1811-in-Kalakad-Mundanthurai-Tiger-Reserve-Tamil-Nadu.pdf.Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR)— Phase IV Monitoring Report and Report on Collaring of Leopards. (2014). 26 (2015).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2015). 62 (2016).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2016). 27 (2017).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2017). 44 (2018).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2018). 41 (2019).Habib, B. et al. Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) (2019). 47 https://ntca.gov.in/assets/uploads/Reports/WII/TATR%20Phase%20IV%202019.pdf (2020).Jhala, Y. V., Qureshi, Q. & Nayak, A. K. Status of tigers, co-predators and prey in India 2018. 656 https://ntca.gov.in/assets/uploads/Reports/AITM/Tiger_Status_Report_2018.pdf (2019).Bagchi, S., Goyal, S. P. & Sankar, K. Prey abundance and prey selection by tigers (Panthera tigris) in a semi-arid, dry deciduous forest in western India. J. Zool. 260, 285–290 (2003).Article 

Google Scholar 
Woodroffe, R., Lindsey, P. A., Romañach, S. S. & Ranah, S. M. K. African Wild Dogs ( Lycaon pictus ) Can Subsist on Small Prey: Implications for Conservation. J. Mammal. 88, 181–193 (2007).Article 

Google Scholar 
Merrill, E. et al. Building a mechanistic understanding of predation with GPS-based movement data. Philos. Trans. R. Soc. B Biol. Sci. 365, 2279–2288 (2010).Article 

Google Scholar 
Pitman, R. T., Mulvaney, J., Ramsay, P. M., Jooste, E. & Swanepoel, L. H. Global Positioning System-located kills and faecal samples: A comparison of leopard dietary estimates. J. Zool. 292, 18–24 (2014).Article 

Google Scholar 
Jansen, C., Leslie, A. J., Cristescu, B., Teichman, K. J. & Martins, Q. Determining the diet of an African mesocarnivore, the caracal: Scat or GPS cluster analysis?. Wildl. Biol. 2019, wlb.00579 (2019).Article 

Google Scholar 
Leighton, G. R. M. et al. An integrated dietary assessment increases feeding event detection in an urban carnivore. Urban Ecosyst. 23, 569–583 (2020).Article 

Google Scholar 
Studd, E. K. et al. The Purr-fect Catch: Using accelerometers and audio recorders to document kill rates and hunting behaviour of a small prey specialist. Methods Ecol. Evol. 12, 1277–1287 (2021).Article 

Google Scholar 
Bhandari, A., Ghaskadbi, P., Nigam, P. & Habib, B. Dhole pack size variation: Assessing the effect of Prey availability and Apex predator. Ecol. Evol. 11, 4774–4785 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Hubel, T. Y. et al. Additive opportunistic capture explains group hunting benefits in African wild dogs. Nat. Commun. 7, 11033 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parker, D. M., Vyver, D. B. & Bissett, C. The influence of an apex predator introduction on an already established subordinate predator. J. Zool. 313, 224–235 (2021).Article 

Google Scholar 
Johnsingh, A. J. T. Prey selection in three large sympatric carnivores in Bandipur. Mammalia 56, (1992).Marucco, F., Pletscher, D. H. & Boitani, L. Accuracy of scat sampling for carnivore diet analysis: Wolves in the Alps as a case study. J. Mammal. 89, 665–673 (2008).Article 

Google Scholar 
Martins, Q., Horsnell, W. G. C., Titus, W., Rautenbach, T. & Harris, S. Diet determination of the Cape Mountain leopards using global positioning system location clusters and scat analysis. J. Zool. 283, 81–87 (2011).Article 

Google Scholar 
Champion, S. H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Manager of Publications, 1968).
Google Scholar 
Thinley, P. et al. Seasonal diet of dholes (Cuon alpinus) in northwestern Bhutan. Mamm. Biol. 76, 518–520 (2011).Article 

Google Scholar 
Modi, S., Habib, B., Ghaskadbi, P., Nigam, P. & Mondol, S. Standardization and validation of a panel of cross-species microsatellites to individually identify the Asiatic wild dog (Cuon alpinus). PeerJ 7, e7453 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Modi, S., Mondol, S., Nigam, P. & Habib, B. Genetic analyses reveal demographic decline and population differentiation in an endangered social carnivore, Asiatic wild dog. Sci. Rep. 11, 16371 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Putman, R. J. Facts from faeces. Mammal Rev. 14, 79–97 (1984).Article 

Google Scholar 
Kohn, M. H. & Wayne, R. K. Facts from feces revisited. Trends Ecol. Evol. 12, 223–227 (1997).CAS 
PubMed 
Article 

Google Scholar 
Mukherjee, S., Goyal, S. P. & Chellam, R. Standardisation of scat analysis techniques for leopard (Panthera pardus) in Gir National Park, Western India. Mammalia 58, (1994).Bahuguna, A., Sahajpal, V., Goyal, S. P., Mukherjee, S. & Thakur, V. Species Identification from Guard Hair of Selected Indian Mammals: A Reference Guide. Wildlife Institute of India (Wildlife Institute of India, 2010).
Google Scholar 
Leopold, B. D. & Krausman, P. R. Diets of 3 Predators in Big Bend National Park, Texas. J. Wildl. Manag. 50, 290 (1986).Article 

Google Scholar 
Van Ballenberghe, V., Erickson, A. W. & Byman, D. Ecology of the Timber Wolf in Northeastern Minnesota. Wildl. Monogr. 3–43 (1975).Ciucci, P., Boitani, L., Pelliccioni, E. R., Rocco, M. & Guy, I. A comparison of scat-analysis methods to assess the diet of the wolf Canis lupus. Wildl. Biol. 2, 37–48 (1996).Article 

Google Scholar 
Weaver, J. L. Refining the equation for interpreting prey occurrence in Gray wolf scats. J. Wildl. Manag. 57, 534–538 (1993).Article 

Google Scholar 
Chakrabarti, S. et al. Adding constraints to predation through allometric relation of scats to consumption. J. Anim. Ecol. 85, 660–670 (2016).PubMed 
Article 

Google Scholar 
Lumetsberger, T. et al. Re-evaluating models for estimating prey consumption by leopards. J. Zool. 302, 201–210 (2017).Article 

Google Scholar 
Jacobs, J. Quantitative measurement of food selection: A modification of the forage ratio and Ivlev’s electivity index. Oecologia 14, 413–417 (1974).ADS 
PubMed 
Article 

Google Scholar 
Karanth, K. U. & Nichols, J. D. Distribution and Dynamics of Tiger and Prey Populations in Maharashtra, India Final Technical Report (October 2001 to August 2005). (2005).19 LIVESTOCK CENSUS-2012 ALL INDIA REPORT. https://d1wqtxts1xzle7.cloudfront.net/56129012/6ESSJan-6098P-with-cover-page-v2.pdf?Expires=1644491741&Signature=Apc1rT2raxYnUyrRJ64NqOd6oUEpnF2AiRQVPB-9gS2W2TIrOcInF3KnBJVA2dPxzfbIz8ap9IPe-l24mpYs9i8xEZAvsxRVnDhSHT8H9C9fd0voDxyUwl3gUyJgDDzLO-204J95UuopJQw5Df6xTNmTOs5Oiadk0Fkf9Fk-QRVajisuRjzyX2eLmrBH4LyTJFu5irffnKwnluqHl53KoMAQ6nTKi7dlqI4pdFIVCtisXpkSsI44xV1mYX6KC67zmKCZlvjpTxTuHCFV4nmfpgZpPXh4sIOE-0utbwcf5g~UdmRtVVhaXfjZ2iw0gOm7-bIuQILDldPr3OnNUqXbSw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA (2012).The Measurement of Niche Overlap and Some Relatives – Hurlbert – 1978 – Ecology – Wiley Online Library. https://esajournals.onlinelibrary.wiley.com/doi/abs/https://doi.org/10.2307/1936632.Habib, B., Ghaskadbi, P., Khan, S., Hussain, Z. & Nigam, P. Not a cakewalk: Insights into movement of large carnivores in human-dominated landscapes in India. Ecol. Evol. 11, 1653–1666 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Neu, C. W., Byers, C. R. & Peek, J. M. A technique for analysis of utilization-availability data. J. Wildl. Manag. 38, 541–545 (1974).Article 

Google Scholar  More

Hughes, J. & Macdonald, D. W. A review of the interactions between free-roaming domestic dogs and wildlife. Biol. Cons. 157, 341–351 (2013).Article 

Google Scholar 
Doherty, T. S. et al. The global impacts of domestic dogs on threatened vertebrates. Biol. Cons. 210, 56–59 (2017).Article 

Google Scholar 
Young, J. K., Olson, K. A., Reading, R. P., Amgalanbaatar, S. & Berger, J. Is wildlife going to the dogs? Impacts of feral and free-roaming dogs on wildlife populations. Bioscience 61, 125–132 (2011).Article 

Google Scholar 
Ritchie, E. G., Dickman, C. R., Letnic, M., Vanak, A. T. & Gommper, M. Dogs as predators and trophic regulators. Free-ranging dogs and wildlife conservation, 55–68 (2014).Gompper, M. E. In Free-ranging dogs and wildlife conservation, Oxford University Press (2014).Somaweera, R., Webb, J. K. & Shine, R. It’sa dog-eat-croc world: Dingo predation on the nests of freshwater crocodiles in tropical Australia. Ecol. Res. 26, 957–967 (2011).Article 

Google Scholar 
Weston, M. A. & Stankowich, T. In Free-Ranging Dogs and Wildlife Conservation. ME Gompper (ed.) (ed Matthew E Gompper) Ch. 4, 94–113 (Oxford University Press, 2013).Zapata-Ríos, G. & Branch, L. C. Altered activity patterns and reduced abundance of native mammals in sites with feral dogs in the high Andes. Biol. Cons. 193, 9–16 (2016).Article 

Google Scholar 
Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in Carnivora. Am. Nat. 167, 524–536 (2006).PubMed 
Article 

Google Scholar 
Gingold, G., Yom-Tov, Y., Kronfeld-Schor, N. & Geffen, E. Effect of guard dogs on the behavior and reproduction of gazelles in cattle enclosures on the Golan Heights. Anim. Conserv. 12, 155–162 (2009).Article 

Google Scholar 
Fernández-Juricic, E. & Tellería, J. L. Effects of human disturbance on spatial and temporal feeding patterns of Blackbird Turdus merula in urban parks in Madrid, Spain. Bird Study 47, 13–21 (2000).Article 

Google Scholar 
Vanak, A. T. & Gompper, M. E. Dogs Canis familiaris as carnivores: Their role and function in intraguild competition. Mammal Rev. 39, 265–283 (2009).Article 

Google Scholar 
Silva-Rodríguez, E. A. & Sieving, K. E. Domestic dogs shape the landscape-scale distribution of a threatened forest ungulate. Biol. Cons. 150, 103–110 (2012).Article 

Google Scholar 
Banks, P. B. & Bryant, J. V. Four-legged friend or foe? Dog walking displaces native birds from natural areas. Biol. Let. 3, 611–613 (2007).Article 

Google Scholar 
Langston, R., Liley, D., Murison, G., Woodfield, E. & Clarke, R. What effects do walkers and dogs have on the distribution and productivity of breeding European Nightjar Caprimulgus europaeus?. Ibis 149, 27–36 (2007).Article 

Google Scholar 
Lenth, B. E., Knight, R. L. & Brennan, M. E. The effects of dogs on wildlife communities. Nat. Areas J. 28, 218–227 (2008).Article 

Google Scholar 
Weston, M. A. & Stankowich, T. Dogs as agents of disturbance. Free-Ranging Dogs and Wildlife Conservation. ME Gompper (ed.), 94–113 (2013).Letnic, M., Ritchie, E. G. & Dickman, C. R. Top predators as biodiversity regulators: The dingo Canis lupus dingo as a case study. Biol. Rev. 87, 390–413 (2012).PubMed 
Article 

Google Scholar 
Maguire, G. S., Miller, K. K. & Weston, M. A. In Impacts of Invasive Species on Coastal Environments 397–412 (Springer, 2019).Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Rodriguez, L. F. Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biol. Invasions 8, 927–939 (2006).Article 

Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).Article 

Google Scholar 
Díaz, S., Fargione, J., Chapin, F. S. III. & Tilman, D. Biodiversity loss threatens human well-being. PLoS Biol 4, e277 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193. https://doi.org/10.1890/10-1510.1 (2011).Article 

Google Scholar 
Nel, R. et al. The status of sandy beach science: Past trends, progress, and possible futures. Estuar. Coast. Shelf Sci. 150, 1–10 (2014).ADS 
Article 

Google Scholar 
Schlacher, T. A. et al. Golden opportunities: A horizon scan to expand sandy beach ecology. Estuar. Coast. Shelf Sci. 157, 1–6 (2015).ADS 
Article 

Google Scholar 
Schlacher, T. A. et al. Key ecological function peaks at the land–ocean transition zone when vertebrate scavengers concentrate on ocean beaches. Ecosystems 23, 1–11 (2019).MathSciNet 

Google Scholar 
Lockwood, J. L. & Maslo, B. In Coastal Convervation (eds Brooke Maslo & JL Lockwood) 1–10 (Cambridge University Press, 2014).Morin, D. J., Lesmeister, D. B., Nielsen, C. K. & Schauber, E. M. The truth about cats and dogs: Landscape composition and human occupation mediate the distribution and potential impact of non-native carnivores. Glob. Ecol. Conserv. 15, e00413 (2018).Article 

Google Scholar 
Cortés, E. I., Navedo, J. G. & Silva-Rodríguez, E. A. Widespread presence of domestic dogs on sandy beaches of Southern Chile. Animals 11, 161 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Burger, J., Jeitner, C., Clark, K. & Niles, L. J. The effect of human activities on migrant shorebirds: Successful adaptive management. Environ. Conserv. 31, 283–288 (2004).Article 

Google Scholar 
Dowling, B. & Weston, M. A. Managing a breeding population of the Hooded Plover Thinornis rubricollis in a high-use recreational environment. Bird Conserv. Int. 9, 255–270 (1999).Article 

Google Scholar 
Vanak, A. T. & Gompper, M. E. Interference competition at the landscape level: The effect of free-ranging dogs on a native mesocarnivore. J. Appl. Ecol. 47, 1225–1232 (2010).Article 

Google Scholar 
Marzluff, J. M., McGowan, K. J., Donnelly, R. & Knight, R. L. In Avian ecology and conservation in an urbanizing world 331–363 (Springer, 2001).Handler, A., Lonsdorf, E. V. & Ardia, D. R. Evidence for red fox (Vulpes vulpes) exploitation of anthropogenic food sources along an urbanization gradient using stable isotope analysis. Can. J. Zool. 98, 79–87 (2020).Article 

Google Scholar 
Prange, S., Gehrt, S. D. & Wiggers, E. P. Demographic factors contributing to high raccoon densities in urban landscapes. The J. Wildlife Manag. 67, 324–333 (2003).Article 

Google Scholar 
Méndez, A. et al. Adapting to urban ecosystems: unravelling the foraging ecology of an opportunistic predator living in cities. Urban Ecosyst. 23, 1117–1126 (2020).Article 

Google Scholar 
Rees, J., Webb, J., Crowther, M. & Letnic, M. Carrion subsidies provided by fishermen increase predation of beach-nesting bird nests by facultative scavengers. Anim. Conserv. 18, 44–49 (2015).Article 

Google Scholar 
Kimber, O. et al. The fox and the beach: Coastal landscape topography and urbanisation predict the distribution of carnivores at the edge of the sea. Glob. Ecol. Conserv. 23, e01071 (2020).Article 

Google Scholar 
Ruxton, G. D. & Houston, D. C. Obligate vertebrate scavengers must be large soaring fliers. J. Theor. Biol. 228, 431–436 (2004).ADS 
MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
Cortés-Avizanda, A., Jovani, R., Donázar, J. A. & Grimm, V. Bird sky networks: How do avian scavengers use social information to find carrion?. Ecology 95, 1799–1808 (2014).PubMed 
Article 

Google Scholar 
Harel, R., Spiegel, O., Getz, W. M. & Nathan, R. Social foraging and individual consistency in following behaviour: Testing the information centre hypothesis in free-ranging vultures. Proc. Royal Soc. B: Biol. Sci. 284, 20162654 (2017).Article 

Google Scholar 
Soulsbury, C. D., Iossa, G., Baker, P. J., White, P. C. & Harris, S. Behavioral and spatial analysis of extraterritorial movements in red foxes (Vulpes vulpes). J. Mammal. 92, 190–199 (2011).Article 

Google Scholar 
Johnson, C. N. & VanDerWal, J. Evidence that dingoes limit abundance of a mesopredator in eastern Australian forests. J. Appl. Ecol. 46, 641–646 (2009).Article 

Google Scholar 
Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: The dynamics of spatially subsidized food webs. Ann. Rev. Ecol. Syst. 28, 289–316 (1997).Article 

Google Scholar 
Barton, P. S., Cunningham, S. A., Lindenmayer, D. B. & Manning, A. D. The role of carrion in maintaining biodiversity and ecological processes in terrestrial ecosystems. Oecologia 171, 761–772 (2013).ADS 
PubMed 
Article 

Google Scholar 
Schlacher, T. A., Strydom, S. & Connolly, R. M. Multiple scavengers respond rapidly to pulsed carrion resources at the land–ocean interface. Acta Oecologica 48, 7–12 (2013).ADS 
Article 

Google Scholar 
Dunbrack, T. R. & Dunbrack, R. L. Why take your dog on a picnic: presence of a potential predator (Canis lupus familiaris) reverses the outcome of food competition between northwestern crows (Corvus caurinus) and glaucous-winged gulls (Larus glaucescens). Northwest. Nat. 91, 94–98 (2010).Article 

Google Scholar 
Jiménez, J. et al. Restoring apex predators can reduce mesopredator abundances. Biol. Cons. 238, 108234 (2019).Article 

Google Scholar 
Bhadra, A. et al. The meat of the matter: A rule of thumb for scavenging dogs?. Ethol. Ecol. Evol. 28, 427–440 (2016).Article 

Google Scholar 
Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. Jr. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).PubMed 
Article 

Google Scholar 
Ogada, D., Torchin, M., Kinnaird, M. & Ezenwa, V. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).CAS 
PubMed 
Article 

Google Scholar 
O’Bryan, C. J. et al. The contribution of predators and scavengers to human well-being. Nat. Ecol. & Evol. 2, 229–236 (2018).Article 

Google Scholar 
Gómez-Serrano, M. Á. Four-legged foes: Dogs disturb nesting plovers more than people do on tourist beaches. Ibis 163, 338–352 (2021).Article 

Google Scholar 
Stantial, M., Cohen, J., Darrah, A., Farrell, S. & Maslo, B. The effect of top predator removal on the distribution of a mesocarnivore and nest survival of an endangered shorebird. Avian Conserv. Ecol. https://doi.org/10.5751/ACE-01806-160108 (2021).Article 

Google Scholar 
Mahon, P. S. Targeted control of widespread exotic species for biodiversity conservation: The red fox (Vulpes vulpes) in New South Wales, Australia. Ecol. Manag. Restor. 10, S59–S69 (2009).ADS 
Article 

Google Scholar 
Colwell, M. A. In The Population Ecology and Conservation of Charadrius Plovers 127–147 (CRC Press, 2019).Huijbers, C. M. et al. Limited functional redundancy in vertebrate scavenger guilds fails to compensate for the loss of raptors from urbanized sandy beaches. Divers. Distrib. 21, 55–63 (2015).Article 

Google Scholar 
Huijbers, C. M., Schlacher, T. A., Schoeman, D. S., Weston, M. A. & Connolly, R. M. Urbanisation alters processing of marine carrion on sandy beaches. Landsc. Urban Plan. 119, 1–8 (2013).Article 

Google Scholar 
Meek, P. et al. Recommended guiding principles for reporting on camera trapping research. Biodivers. Conserv. 23, 2321–2343 (2014).Article 

Google Scholar 
Kolowski, J. M. & Forrester, T. D. Camera trap placement and the potential for bias due to trails and other features. PLoS ONE 12, e0186679 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article 

Google Scholar 
Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. Royal Soc. B: Biol. Sci. 274, 1101–1108 (2007).Article 

Google Scholar 
Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Jr. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Anderson, M. J. Permutation tests for univariate or multivariate analysis of variance and regression. Can. J. Fish. Aquat. Sci. 58, 626–639 (2001).Article 

Google Scholar 
Team, R. D. C. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria (2013).Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Schlacher, T. A. et al. Conservation gone to the dogs: When canids rule the beach in small coastal reserves. Biodivers. Conserv. 24, 493–509 (2015).Article 

Google Scholar 
Lewin, W.-C., Freyhof, J., Huckstorf, V., Mehner, T. & Wolter, C. When no catches matter: Coping with zeros in environmental assessments. Ecol. Ind. 10, 572–583 (2010).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. 488 (Springer Science & Business Media, 2002).Bolker, B. & Team, R. (R package version 0.9, 2010).Barton, K. & Barton, M. K. Package ‘mumin’. Version 1, 439 (2015).Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 0.4. 3. R Found. Stat. Comput., Vienna. https://CRAN. R-project. org/package= dplyr (2015).Wickham, H., Chang, W. & Wickham, M. H. Package ‘ggplot2’. Create Elegant Data Visualisations Using the Grammar of Graphics. Version 2, 1–189 (2016). More

Influence of independent variables on the extent of stereotyped behaviourThe overall level of stereotypy we observed was low, suggesting that the pandas in our study were not seriously stressed. The variables that we found to be correlated with stereotypy are consistent with what we know of pandas’ natural history. Our study reports that variables like logs on the ground, nest, sociality, zoo, tree density, age and tree height used by pandas are the driving force for stereotypy in captive pandas involved in the study.Making the captive environment more naturalistic by integrating enrichment into the enclosure seems to be a promising way of alleviating stress and improving both welfare and reintroduction success41. It also helps to improve reproductive rate and overall health39. Improved health reduces stress and gives greater control over the environment increasing the chances of survival and longevity both in captivity and following release into the wild5. It is generally accepted that enrichment of the captive environment increases animals’ ability to cope with challenges and positive use of the environment reduces or eliminates aberrant behaviour23. Lack of enclosure enrichments and less complex enclosures can cause stereotypy and other atypical behaviours24, while providing enrichment increases the frequency of natural behaviours25 and thereby reduces stress, which in turn decreases stereotypy27. But enrichment needs to be appropriate for the species of animal concerned. Abnormal behaviours are often associated with captive conditions that deviate greatly from the species’ natural environment. Consistent with this argument we found that though dead and fallen logs on the ground are one of the important characteristics of the panda habitats in the wild42,43,44,45, merely providing them in captivity does not ensure the species’ welfare: in fact, stereotypy increased with log density in our study subjects. This could be due to the fact that four individuals that showed more stereotypy were housed in the small barren enclosures with no trees but more logs as a part of enrichment. Without those four individuals, the linear relation between stereotypy and log density was not statistically significant. This clearly suggested that merely providing logs in the small enclosures does not maintain welfare.
When animals are housed in enclosures designed to resemble their natural habitat by considering their natural history (provision of vegetation, shelter, pool, etc.), there is a reduction or elimination of abnormal patterns of behaviour such as stereotypies, increased fitness and improved health, all of which may influence reproduction25,46,47,48. For many species, nests, shelter or burrows in enclosures will serve as retreat and hiding places, which are essential to cope with environmental stressors10. Gerbils, mice and rabbits have all shown less stereotyped behaviour when retreats are provided9,49,50,51. Such retreats can mitigate the effects of zoo visitors, who can serve as a source of stress for species that rarely interact with humans in the wild. Consistent with these previous results, we found that with provision of nests, the extent of stereotypy decreased in captive pandas. Many species prefer nests both for rearing the young as well as for resting and shelter, and pandas follow this pattern, so providing nests in adequate numbers will supports their natural behaviour as well as provide relief from environmental stressors. Zidar recommends providing one more nest than there are individuals in an enclosure52.Although pandas are an asocial species, our study showed that pandas show more stereotyped behaviour when housed alone than when with another individual or in group. Being a solitary species in the wild might encourage management to house them singly in captivity, but not every activity and habit of species in the wild can be used in captivity. For example, polar bears are also a solitary species, and it was at one time thought best to manage them alone, but it was found that managing them in a social setting reduces stereotypic pacing behaviour53, consistent with this study. Importantly, managers of zoo should note that living in group is greatly influenced by the individuals’ compatibility and hence this should be kept in mind while pairing.Similarly, we found that the presence of trees, and greater mean tree height use by pandas, reduced stereotypy. Pandas’ preferred high elevation habitat is favourable for taller trees20, and Shrestha et al. found that canopy cover was an important factor in habitats for pandas in the wild54. In European zoos, pandas spend 90% of their time off the ground37. Consistent with these previous findings, our study reveals that more and taller trees support natural behaviours in panda. The Central Zoo Authority (CZA) of India enrichment manual recommends taller tree provision in panda enclosures, and again we provide empirical support for its recommendation.We found that with increasing age stereotypy increased in pandas. The older the individuals the more time spent in captivity with its associated risks of stereotypic behaviour. The same trend has been observed in other species: for example in captive bears stereotypic behaviour increased with age55. In another study Asiatic black bear and sun bear showed more stereotypy with age56.Influence of independent variables on behavioural diversityAs noted in the “Introduction” section, in a species like the panda, high daytime behavioural diversity is not necessarily a positive indication of good welfare. However, our comparison of behavioural diversity with stereotypy showed a negative trend (though not significant), suggesting that low behavioural diversity might be associated with poorer welfare.Nonetheless, we found some results that suggested that lower diversity might in fact be associated with a more natural lifestyle. Because of the amount of time that wild pandas spend foraging57 and sleeping or inactive, they cannot show much behavioural diversity, and in our sample of captive individuals, they showed the same trend. For example, behavioural diversity was lower when pandas were provided with more trees in the enclosure. This suggests that when appropriate conditions are maintained in captivity, panda prefer to be inactive during the day, as is consistent with their natural history57. As pandas are essentially arboreal mammals, naturally they also spend most of the time inactive (e.g. sleeping) on the trees57. Indeed, providing larger trees would promote inactive behaviours and hence lower behaviour diversity in captivity, this captures their natural behaviour. This is consistent with our results where increased tree height used by pandas decreased behavioural diversity.We found behavioural diversity was greater when there are more logs in the enclosure. In the Yele Reserve in Sichuan, China, Wei et al. found 107 of 185 panda dropping sites (57.8%) on shrub branches, 49 (26.5%) on fallen logs, and only 29 (15.7%) on the forest floor44. Droppings were found mostly on elevated structures ranging from 1 to 3 m above the forest floor and occasionally on trees over 12 m. Moreover, microhabitats selected by pandas were also characterized by fallen logs and tree stumps42,45. Wei and Zhang mention that to access bamboo leaves easily, pandas usually use some elevated objects, such as shrub branches, fallen logs, or tree stumps to lift their body43. Hence, providing tree logs in the vicinity supports their natural behaviour. But at the same time management should keep in mind that merely providing logs in the enclosure would not guarantee species welfare, as discussed in previous section with respect to stereotypy.Temperature is an important element of microclimate for animals, and influences the activity level of captive animals10. When temperature rises, many species show distress in captivity10. The red panda inhabits low-temperature areas20, so it is unlikely that higher temperatures would support natural behaviours. We found that with increased temperature behavioural diversity decreased in captive pandas. Similarly, we found that pandas showed higher behavioural diversity in the winter season, where temperatures are low as compared to summer season.Studies that have tried to relate behavioural diversity and stereotypy in captive animals have varied in their interpretation; many have found significantly inverse relationships between the two19. In this study our multivariate model suggested that behavioural diversity is negatively influenced by stereotypy in captive pandas, confirming previous research.Other factors associated with variations in behavioural diversity are less easy to identify with welfare, positive or negative. Behavioural diversity also decreases with age of pandas and increases with distance to cage mate, number of visitors and quantum of bamboo provided, though these effects were not significant in the REVS model.Taken together, these results suggest that higher behavioural diversity is not a straightforward indicator of better welfare in all captive animals. The overall non-significant relationship between stereotyped behaviour and diversity we observed could well be the result of a mixture of factors operating in opposite directions. To interpret diversity correctly, it would be helpful to know what level of diversity the species shows in the wild, and such data are rarely available—a limitation of our study as of many others. Although there are dissenting voices58, arguably what matters most both in terms of welfare and in terms of potential reintroduction to the wild, is that a captive animal’s time budget approximates as closely as possible that of a wild animal. It is not diversity as such that is important, but the behaviours that the animal exhibits.Differences between zoosOur study showed that both the extent of stereotyped behaviour and behavioural diversity varied significantly among zoos. However, Zoo 2, an important breeding centre, housed only a female and her two cubs; this may lead to many factors being confounded and is thus a limitation to our study. Captive animals rely on the zoo environment, its routine and husbandry practices to limit their stress levels, and any failure to provide suitable resources will certainly disturb them and lead to distress10. Controlling such variables appropriately will help reduce stress among captive animals, and we can rely to some extent on our knowledge of the species’ natural history to guide us through this challenge. Our study was able to identify some of the factors that are associated with better welfare, but even with these factors taken into account, significant differences among the three zoos remained. These are presumably due to subtler variations in the zoos’ environment or management regimes. Since the panda is endemic to high elevations, we considered whether differences between the elevations of the zoos might be relevant, but the biggest differences were between Zoos 1 and 3, which are at essentially the same elevation.In Zoo 1 pandas showed lower stereotypy and higher behavioural diversity then the other two zoos. Again, these differences may be due to subtle differences between the management regimes in the three zoos; possibilities include keepers’ attitudes and the zoo’s experience in managing pandas. It is notable that Zoo 1 has longer and wider experience in the management of red pandas than the other two zoos, which have joined the captive breeding programme more recently and have fewer animals. Other notable differences were that in Zoo 1, pandas are fed twice a day as compared to the other two zoos where feed is given all at one time (both bamboo and supplementary diet); and that in Zoo 1 the enclosures were of a good size for a small mammal like the red panda, and were well maintained with much natural vegetation. The other two zoos had a large enclosure with poor vegetation (trees and grass), or a small enclosure with a barren floor and no trees at all. Location of the enclosure also needs to be considered: in two of the enclosures at Zoo 3 the sun shone directly on the animals with no shade as such, keeping the temperature higher than would be natural for pandas. Any of these factors could be the reason the pandas performed comparatively well in Zoo 1, and it would be necessary to study a wider (and, therefore, cross-national) sample of zoos holding pandas to identify which of them are the most important. More