Philippa Kaur

Asner, G. P., Elmore, A. J., Olander, L. P., Martin, R. E. & Harris, A. T. Grazing systems, ecosystem responses, and global change. Annu. Rev. Environ. Resour. 29, 261–299 (2004).Article 

Google Scholar 
Millenium Ecosystem Assessment Board. Ecosystems and Human Well-Being: Wetlands and Water: Synthesis (Island Press, Washington, DC, 2005).Lind, J., Sabates-Wheeler, R., Caravani, M., Kuol, L. B. D. & Nightingale, D. M. Newly evolving pastoral and post-pastoral rangelands of Eastern Africa. Pastoralism 10, 24 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffman, T. & Vogel, C. Climate change impacts on African rangelands. Rangelands 30, 12–17 (2008).Article 

Google Scholar 
Joyce, L. A. et al. Climate change and North American rangelands: Assessment of mitigation and adaptation strategies. Rangeland Ecol. Manage. 66, 512–528 (2013).Article 

Google Scholar 
Stringer, L. C., Reed, M. S., Dougill, A. J., Seely, M. K. & Rokitzki, M. Implementing the UNCCD: Participatory challenges. Nat. Resour. Forum 31, 198–211 (2007).Article 

Google Scholar 
Vågen, T.-G., Winowiecki, L. A., Tondoh, J. E., Desta, L. T. & Gumbricht, T. Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263, 216–225 (2016).Article 
ADS 

Google Scholar 
Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Chang. Biol. 23, 235–244 (2017).Article 
ADS 
PubMed 

Google Scholar 
Muñoz, P. et al. Land degradation, poverty and inequality (2019).Bond, W. & Keeley, J. Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394 (2005).Article 
PubMed 

Google Scholar 
Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. New Phytol. 191, 197–209 (2011).Article 
PubMed 

Google Scholar 
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).Article 
ADS 
CAS 
MATH 
PubMed 

Google Scholar 
Fuhlendorf, S. D., Fynn, R. W. S., McGranahan, D. A. & Twidwell, D. Heterogeneity as the basis for rangeland management in Rangeland Systems: Processes, Management and Challenges, Springer Series on Environmental Management (ed. Briske, D. D.), 169–196 (Springer International Publishing, 2017).Liao, C., Agrawal, A., Clark, P. E., Levin, S. A. & Rubenstein, D. I. Landscape sustainability science in the drylands: mobility, rangelands and livelihoods. Landsc. Ecol. 35, 2433–2447 (2020).Article 

Google Scholar 
Galvin, K. A. Transitions: pastoralists living with change. Annu. Rev. Anthropol. 38, 185–198 (2009).Article 

Google Scholar 
López-i Gelats, F., Fraser, E. D. G., Morton, J. F. & Rivera-Ferre, M. G. What drives the vulnerability of pastoralists to global environmental change? A qualitative meta-analysis. Glob. Environ. Change 39, 258–274 (2016).Obiri, J. F. Invasive plant species and their disaster-effects in dry tropical forests and rangelands of Kenya and Tanzania. Jàmbá: Journal of Disaster Risk Studies 3, 417–428 (2011).Kioko, J., Kiringe, J. W. & Seno, S. O. Impacts of livestock grazing on a savanna grassland in Kenya. J. Arid Land 4, 29–35 (2012).Article 

Google Scholar 
Kotiaho, J. S. et al. The IPBES assessment report on land degradation and restoration. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem (2018).Western, D., Mose, V. N., Worden, J. & Maitumo, D. Predicting extreme droughts in savannah Africa: A comparison of proxy and direct measures in detecting biomass fluctuations, trends and their causes. PLoS One 10, e0136516 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Dai, A. Drought under global warming: a review. WIREs Climate Change 2, 45–65 (2011).Article 

Google Scholar 
Holechek, J. L., Cibils, A. F., Bengaly, K. & Kinyamario, J. I. Human population growth, African pastoralism, and rangelands: A perspective. Rangeland Ecol. Manage. 70, 273–280 (2017).Article 

Google Scholar 
Midgley, G. F. & Bond, W. J. Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change. Nat. Clim. Chang. 5, 823–829 (2015).Article 
ADS 

Google Scholar 
Hill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: Characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sensing 12, 406 (2020).Article 
ADS 

Google Scholar 
Lake, P. S. Resistance, resilience and restoration. Ecol. Manage. Restor. 14, 20–24 (2013).Article 

Google Scholar 
Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’?. Trends Ecol. Evol. 30, 503–506 (2015).Article 
PubMed 

Google Scholar 
Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 
ADS 

Google Scholar 
Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. 26, 677–685 (2018).Article 

Google Scholar 
Ruppert, J. C. et al. Quantifying drylands’ drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime. Glob. Chang. Biol. 21, 1258–1270 (2015).Article 
ADS 
PubMed 

Google Scholar 
Homewood, K. M. Policy, environment and development in African rangelands. Environ. Sci. Policy 7, 125–143 (2004).Article 

Google Scholar 
Caro, T. & Davenport, T. R. B. Wildlife and wildlife management in Tanzania. Conserv. Biol. 30, 716–723 (2016).Article 
PubMed 

Google Scholar 
Bollig, M. & Schulte, A. Environmental change and pastoral perceptions: degradation and indigenous knowledge in two African pastoral communities. Hum. Ecol. 27, 493–514 (1999).Article 

Google Scholar 
Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Nicholson, S. E. Climate and climatic variability of rainfall over Eastern Africa. Rev. Geophys. 55, 590–635 (2017).Article 
ADS 

Google Scholar 
2012 Population and Housing Census (National Bureau of Statistics, Ministry of Finance, 2013).Kiffner, C., Nagar, S., Kollmar, C. & Kioko, J. Wildlife species richness and densities in wildlife corridors of Northern Tanzania. J. Nat. Conserv. 31, 29–37 (2016).Article 

Google Scholar 
Foley, C. A. H. & Faust, L. J. Rapid population growth in an elephant Loxodonta africana population recovering from poaching in Tarangire National Park, Tanzania. Oryx 44, 205–212 (2010).Article 

Google Scholar 
Kebacho, L. L. Large-scale circulations associated with recent interannual variability of the short rains over East Africa. Meteorol. Atmos. Phys. 134, 10 (2021).Article 
ADS 

Google Scholar 
Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E. & Marsham, J. H. Extreme rainfall in East Africa, October 2019-January 2020 and context under future climate change. Weather 76, 26–31 (2021).Article 
ADS 

Google Scholar 
Abukari, H. & Mwalyosi, R. B. Comparing pressures on national parks in Ghana and Tanzania: The case of mole and Tarangire National Parks. Global Ecol. Conserv. 15, e00405 (2018).Article 

Google Scholar 
Kaswamila, A. An analysis of the contribution of community wildlife management areas on livelihood in Tanzania. Sustain. Natl. Res. Manag. 139–54 (2012).NTRI. Maps | NTRI – Northern Tanzania Rangelands Initiative. https://www.ntri.co.tz/maps/ (2016). Accessed: 2021-3-29.Mworia, J., Kinyamario, J. & John, E. Impact of the invader Ipomoea hildebrandtii on grass biomass, nitrogen mineralisation and determinants of its seedling establishment in Kajiado, Kenya. Afr. J. Range Forage Sci. 25, 11–16 (2008).Article 

Google Scholar 
Manyanza, N. M. & Ojija, F. Invasion, impact and control techniques for invasive Ipomoea hildebrandtii on Maasai steppe rangelands. NATO Adv. Sci. Inst. Ser. E Appl. Sci. 17, 12 (2021).Thaiyah, A. G. et al. Acute, sub-chronic and chronic toxicity of Solanum incanum L in sheep in Kenya. Kenya Veterinarian 35, 1–8 (2011).
Google Scholar 
Roques, K. G., O’Connor, T. G. & Watkinson, A. R. Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence. J. Appl. Ecol. 38, 268–280 (2001).Article 

Google Scholar 
Riginos, C. & Herrick, J. E. Monitoring rangeland health: a guide for pastoralists and other land managers in Eastern Africa. Version II (2010).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).QGIS Development Team. QGIS Geographic Information System. QGIS Association (2022).Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015).Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC 10 (2015).Funk, C. et al. The climate hazards infrared precipitation with stations–a new environmental record for monitoring extremes. Scientific Data 2, 1–21 (2015).Article 

Google Scholar 
Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. arXiv:math/0505527 (2005).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2016).Scaramuzza, P. & Barsi, J. Landsat 7 scan line corrector-off gap-filled product development in Proceeding of Pecora 16, 23–27 (2005).
Google Scholar 
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).Article 
ADS 

Google Scholar 
Rikimaru, A., Roy, P. S. & Miyatake, S. Tropical forest cover density mapping. Trop. Ecol. 39–47 (2002).Diek, S., Fornallaz, F., Schaepman, M. E. & De Jong, R. Barest pixel composite for agricultural areas using landsat time series. Remote Sensing 9, 1245 (2017).Article 
ADS 

Google Scholar 
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).Article 
ADS 

Google Scholar 
Adams, B. et al. Mapping forest composition with Landsat time series: An evaluation of seasonal composites and harmonic regression. Remote Sensing 12, 610 (2020).Article 
ADS 

Google Scholar 
Nwanganga, F. & Chapple, M. Practical machine learning in R (John Wiley and Sons, Indianapolis, 2020).Adam, E., Mutanga, O., Odindi, J. & Abdel-Rahman, E. M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 35, 3440–3458 (2014).Article 

Google Scholar 
Mansour, K., Mutanga, O., Adam, E. & Abdel-Rahman, E. M. Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors. Geocarto Int. 31, 477–491 (2016).Article 

Google Scholar 
Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-Seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sensing 12, 198 (2020).Article 
ADS 

Google Scholar 
Yang, L. et al. Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method. Remote Sensing 10, 185 (2018).Article 
ADS 

Google Scholar 
Pham, T. D. et al. Estimating mangrove Above-Ground biomass using extreme gradient boosting decision trees algorithm with fused Sentinel-2 and ALOS-2 PALSAR-2 data in Can Gio biosphere reserve, Vietnam. Remote Sensing 12, 777 (2020).Article 
ADS 

Google Scholar 
Adobe Inc. Adobe illustrator.Lenth, R. V. emmeans: Estimated marginal means, aka Least-Squares means. R package version 1.5.4 (2021).Royall, R. M. The effect of sample size on the meaning of significance tests. Am. Stat. 40, 313–315 (1986).MATH 

Google Scholar 
Rue, H., Martino, S. & Chopin, N. Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol. 71, 319–392 (2009).Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 1–25 (2015).Article 

Google Scholar 
Bakka, H. et al. Spatial modelling with R-INLA: A review. arXiv:1802.06350 [stat] (2018).Lobora, A. L. et al. Modelling habitat conversion in Miombo woodlands: Insights from Tanzania. J. Land Use Sci. 1747423X.2017.1331271 (2017).Bright, E. A., Rose, A. N., Urban, M. L. & McKee, J. LandScan 2017 High-Resolution global population data set. Tech. Rep., Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States) (2018).Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci Data 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, Y., Fang, J., Ma, W. & Wang, W. Relationship between variability in aboveground net primary production and precipitation in global grasslands. Geophys. Res. Lett. 35 (2008).Guo, Q. et al. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Glob. Chang. Biol. 18, 3624–3631 (2012).Article 
ADS 

Google Scholar 
Wang, X., Yue, Y. & Faraway, J. J. Bayesian Regression Modeling with INLA (Chapman and Hall/CRC, 2018).Côté, I. M. & Darling, E. S. Rethinking ecosystem resilience in the face of climate change. PLoS Biol. 8, e1000438 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Loughlin, J. et al. Climate variability and conflict risk in East Africa, 1990–2009. Proc. Natl. Acad. Sci. 109, 18344–18349 (2012).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Ongoma, V., Chen, H., Gao, C., Nyongesa, A. M. & Polong, F. Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble. Nat. Hazards 90, 901–920 (2018).Article 

Google Scholar 
Homewood, K. & Rodgers, W. A. Pastoralism, conservation and the overgrazing controversy. Conservation in Africa: People, policies and practice 111–128 (1987).Scoones, I. Exploiting heterogeneity: habitat use by cattle in dryland Zimbabwe. J. Arid Environ. 29, 221–237 (1995).Article 
ADS 

Google Scholar 
Goldman, M. J. & Riosmena, F. Adaptive capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes. Glob. Environ. Change 23, 588–597 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Selemani, I. S. & Others. Communal rangelands management and challenges underpinning pastoral mobility in Tanzania: a review. Livestock Res. Rural Dev. 26, 1–12 (2014).Middleton, N. Rangeland management and climate hazards in drylands: dust storms, desertification and the overgrazing debate. Nat. Hazards 92, 57–70 (2018).Article 

Google Scholar 
Sallu, S. M., Twyman, C. & Stringer, L. C. Resilient or vulnerable livelihoods? Assessing livelihood dynamics and trajectories in rural Botswana. Ecology and Society 15 (2010).Oba, G. & Lusigi, W. J. An overview of drought strategies and land use in African pastoral systems (Agricultural Administration Unit, Overseas Development Institute, 1987).Russell, S., Tyrrell, P. & Western, D. Seasonal interactions of pastoralists and wildlife in relation to pasture in an African savanna ecosystem. J. Arid Environ. 154, 70–81 (2018).Article 
ADS 

Google Scholar 
Girvetz, E. et al. Future climate projections in Africa: Where are we headed? In The Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future 15–27 (Springer International Publishing, 2019).Lyon, B. & DeWitt, D. G. A recent and abrupt decline in the East African long rains. Geophys. Res. Lett. 39 (2012).Liebmann, B. et al. Climatology and interannual variability of boreal spring wet season precipitation in the Eastern Horn of Africa and implications for its recent decline. J. Clim. 30, 3867–3886 (2017).Article 
ADS 

Google Scholar 
Shongwe, M. E., van Oldenborgh, G. J., van den Hurk, B. & van Aalst, M. Projected changes in mean and extreme precipitation in Africa under global warming. part II: East Africa. J. Clim. 24, 3718–3733 (2011).Dunning, C. M., Black, E. & Allan, R. P. Later wet seasons with more intense rainfall over Africa under future climate change. J. Clim. 31, 9719–9738 (2018).Article 
ADS 

Google Scholar 
Rowell, D. P., Booth, B. B. B., Nicholson, S. E. & Good, P. Reconciling past and future rainfall trends over East Africa. J. Clim. 28, 9768–9788 (2015).Article 
ADS 

Google Scholar 
Vizy, E. K. & Cook, K. H. Mid-Twenty-First-Century changes in extreme events over Northern and Tropical Africa. J. Clim. 25, 5748–5767 (2012).Article 
ADS 

Google Scholar 
Gebremeskel Haile, G. et al. Droughts in East Africa: Causes, impacts and resilience. Earth-Sci. Rev. 193, 146–161 (2019).Kendon, E. J. et al. Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale. Nat. Commun. 10, 1794 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Finney, D. L. et al. Effects of explicit convection on future projections of mesoscale circulations, rainfall, and rainfall extremes over Eastern Africa. J. Clim. 33, 2701–2718 (2020).Article 
ADS 

Google Scholar 
Prins, H. H. T. & Loth, P. E. Rainfall patterns as background to plant phenology in Northern Tanzania. J. Biogeogr. 15, 451–463 (1988).Article 

Google Scholar 
Ngondya, I. B., Treydte, A. C., Ndakidemi, P. A. & Munishi, L. K. Invasive plants: ecological effects, status, management challenges in Tanzania and the way forward. J. Biodivers. Environ. Sci. (JBES) 10, 204–217 (2017).
Google Scholar 
Drusch, M. et al. Sentinel-2: ESA’s optical High-Resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36 (2012).Article 
ADS 

Google Scholar 
Rapinel, S. et al. Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sens. Environ. 223, 115–129 (2019).Article 
ADS 

Google Scholar 
Li, W. et al. Accelerating savanna degradation threatens the Maasai Mara socio-ecological system. Glob. Environ. Change 60, 102030 (2020).Article 

Google Scholar 
Wonkka, C. L., Twidwell, D., Franz, T. E., Taylor, C. A. & Rogers, W. E. Persistence of a severe drought increases desertification but not woody dieback in semiarid savanna. Rangeland Ecol. Manage. 69, 491–498 (2016).Article 

Google Scholar 
Vierich, H. I. D. & Stoop, W. A. Changes in West African savanna agriculture in response to growing population and continuing low rainfall. Agric. Ecosyst. Environ. 31, 115–132 (1990).Article 

Google Scholar 
Fynn, R. W. S. & O’Connor, T. G. Effect of stocking rate and rainfall on rangeland dynamics and cattle performance in a semi-arid savanna, South Africa. J. Appl. Ecol. 37, 491–507 (2000).Article 

Google Scholar 
Wang, S., Chen, W., Xie, S. M., Azzari, G. & Lobell, D. B. Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sensing 12, 207 (2020).Article 
ADS 

Google Scholar 
Alananga, S., Makupa, E. R., Moyo, K. J., Matotola, U. C. & Mrema, E. F. Land administration practices in Tanzania: A replica of past mistakes. Journal of Property, Planning and Environmental Law (2019).Huggins, C. Village land use planning and commercialization of land in Tanzania. LANDac Research Brief 1 (2016).Stein, H., Maganga, F. P., Odgaard, R., Askew, K. & Cunningham, S. The formal divide: Customary rights and the allocation of credit to agriculture in Tanzania. J. Dev. Stud. 52, 1306–1319 (2016).Article 

Google Scholar 
Hall, D. G. M., Reeve, M. J., Thomasson, A. J. & Wright, V. F. Water retention, porosity and density of field soils (No. Tech. Monograph N9, 1977).Moore, D. C. & Singer, M. J. Crust formation effects on soil erosion processes. Soil Sci. Soc. Am. J. 54, 1117–1123 (1990).Article 
ADS 

Google Scholar 
Cotler, H. & Ortega-Larrocea, M. P. Effects of land use on soil erosion in a tropical dry forest ecosystem, Chamela watershed, Mexico. Catena 65, 107–117 (2006).Article 

Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).Article 
CAS 

Google Scholar 
Butz, R. J. Traditional fire management: historical fire regimes and land use change in pastoral East Africa. Int. J. Wildland Fire 18, 442–450 (2009).Article 

Google Scholar  More

Mooney, H. et al. Biodiversity, climate change, and ecosystem services. Curr. Opin. Environ. Sustain. 1, 46–54 (2009).Article 

Google Scholar 
Perrings, C., Duraiappah, A., Larigauderie, A. & Mooney, H. The biodiversity and ecosystem services science-policy interface. Science 331, 1139–1140 (2011).Article 
ADS 
CAS 

Google Scholar 
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. USA 96, 1463–1468 (1999).Article 
ADS 
CAS 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Gonzalez, A. & Loreau, M. The causes and consequences of compensatory dynamics in ecological communities. Annu. Rev. Ecol. Evol. Syst. 40, 393–414 (2009).Article 

Google Scholar 
Blüthgen, N. & Klein, A.-M. Functional complementarity and specialisation: The role of biodiversity in plant–pollinator interactions. Basic Appl. Ecol. 12, 282–291 (2011).Article 

Google Scholar 
Brittain, C., Kremen, C. & Klein, A. M. Biodiversity buffers pollination from changes in environmental conditions. Glob. Change Biol. 19, 540–547 (2013).Article 
ADS 

Google Scholar 
Rader, R., Reilly, J., Bartomeus, I. & Winfree, R. Native bees buffer the negative impact of climate warming on honey bee pollination of watermelon crops. Glob. Chang. Biol. 19, 3103–3110 (2013).Article 
ADS 

Google Scholar 
Rogers, S. R., Tarpy, D. R. & Burrack, H. J. Bee species diversity enhances productivity and stability in a perennial crop. PLoS ONE 9, e97307 (2014).Article 
ADS 

Google Scholar 
Kühsel, S. & Blüthgen, N. High diversity stabilizes the thermal resilience of pollinator communities in intensively managed grasslands. Nat. Commun. 6, 1–10 (2015).Article 

Google Scholar 
Knop, E. et al. Rush hours in flower visitors over a day-night cycle. Insect Conserv. Divers. 11, 267–275 (2018).Article 

Google Scholar 
Goodwin, E. K., Rader, R., Encinas-Viso, F. & Saunders, M. E. Weather conditions affect the visitation frequency, richness and detectability of insect flower visitors in the Australian Alpine zone. Environ. Entomol. 50, 348–358 (2021).Article 

Google Scholar 
Feit, B. et al. Landscape complexity promotes resilience of biological pest control to climate change. Proc. Biol. Sci. 288, 20210547 (2021).
Google Scholar 
Tomas, F., Martínez-Crego, B., Hernán, G. & Santos, R. Responses of seagrass to anthropogenic and natural disturbances do not equally translate to its consumers. Glob. Chang. Biol. 21, 4021–4030 (2015).Article 
ADS 

Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).Article 

Google Scholar 
Cariveau, D. P., Williams, N. M., Benjamin, F. E. & Winfree, R. Response diversity to land use occurs but does not consistently stabilise ecosystem services provided by native pollinators. Ecol. Lett. 16, 903–911 (2013).Article 

Google Scholar 
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608. https://doi.org/10.1126/science.1230200 (2013).Article 
ADS 
CAS 

Google Scholar 
Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599 (2013).Article 

Google Scholar 
Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. 113, 146–151 (2016).Article 
ADS 
CAS 

Google Scholar 
Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

Google Scholar 
Smith, M. R., Singh, G. M., Mozaffarian, D. & Myers, S. S. Effects of decreases of animal pollinators on human nutrition and global health: A modelling analysis. Lancet 386, 1964–1972 (2015).Article 

Google Scholar 
González-Varo, J. P. et al. Combined effects of global change pressures on animal-mediated pollination. Trends Ecol. Evol. 28, 524–530 (2013).Article 

Google Scholar 
Marshall, L. et al. The interplay of climate and land use change affects the distribution of EU bumblebees. Glob. Change Biol. 24, 101–116 (2018).Article 
ADS 

Google Scholar 
Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 1–11 (2021).Article 
ADS 

Google Scholar 
Vasiliev, D. & Greenwood, S. The role of climate change in pollinator decline across the Northern Hemisphere is underestimated. Sci. Total Environ. 775, 145788 (2021).Article 
ADS 
CAS 

Google Scholar 
Steffan-Dewenter, I., Münzenberg, U., Bürger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).Article 

Google Scholar 
Hass, A. L. et al. Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in western Europe. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2017.2242 (2018).Article 

Google Scholar 
Winfree, R. & Kremen, C. Are ecosystem services stabilized by differences among species? A test using crop pollination. Proc. R. Soc. B Biol. Sci. 276, 229–237 (2009).Article 

Google Scholar 
Jauker, F., Diekoetter, T., Schwarzbach, F. & Wolters, V. Pollinator dispersal in an agricultural matrix: Opposing responses of wild bees and hoverflies to landscape structure and distance from main habitat. Landsc. Ecol. 24, 547–555 (2009).Article 

Google Scholar 
Weiner, C. N., Werner, M., Linsenmair, K. E. & Blüthgen, N. Land-use impacts on plant–pollinator networks: Interaction strength and specialization predict pollinator declines. Ecology 95, 466–474 (2014).Article 

Google Scholar 
Chain-Guadarrama, A., Martínez-Salinas, A., Aristizábal, N. & Ricketts, T. H. Ecosystem services by birds and bees to coffee in a changing climate: A review of coffee berry borer control and pollination. Agric. Ecosyst. Environ. 280, 53–67 (2019).Article 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A. L. & Totland, Ø. How does climate warming affect plant–pollinator interactions?. Ecol. Lett. 12, 184–195 (2009).Article 

Google Scholar 
Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328 (2014).Article 

Google Scholar 
Albrecht, M., Schmid, B., Hautier, Y. & Müller, C. B. Diverse pollinator communities enhance plant reproductive success. Proc. R. Soc. B Biol. Sci. 279, 4845–4852 (2012).Article 

Google Scholar 
Ellis, C. R., Feltham, H., Park, K., Hanley, N. & Goulson, D. Seasonal complementary in pollinators of soft-fruit crops. Basic Appl. Ecol. 19, 45–55 (2017).Article 

Google Scholar 
Brittain, C., Williams, N., Kremen, C. & Klein, A.-M. Synergistic effects of non-Apis bees and honey bees for pollination services. Proc. R. Soc. B Biol. Sci. 280, 20122767 (2013).Article 

Google Scholar 
Miñarro, M. & Twizell, K. W. Pollination services provided by wild insects to kiwifruit (Actinidia deliciosa). Apidologie 46, 276–285 (2015).Article 

Google Scholar 
Senapathi, D., Goddard, M. A., Kunin, W. E. & Baldock, K. C. Landscape impacts on pollinator communities in temperate systems: Evidence and knowledge gaps. Funct. Ecol. 31, 26–37 (2017).Article 

Google Scholar 
Papanikolaou, A. D., Kuehn, I., Frenzel, M. & Schweiger, O. Landscape heterogeneity enhances stability of wild bee abundance under highly varying temperature, but not under highly varying precipitation. Landsc. Ecol. 32, 581–593 (2017).Article 

Google Scholar 
Papanikolaou, A. D., Kühn, I., Frenzel, M. & Schweiger, O. Semi-natural habitats mitigate the effects of temperature rise on wild bees. J. Appl. Ecol. 54, 527–536 (2017).Article 

Google Scholar 
Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: The importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B Biol. Sci. 282, 20142934 (2015).Article 

Google Scholar 
Settele, J., Bishop, J. & Potts, S. G. Climate change impacts on pollination. Nat. Plants 2, 1–3 (2016).Article 

Google Scholar 
Taki, H., Okabe, K., Makino, S. I., Yamaura, Y. & Sueyoshi, M. Contribution of small insects to pollination of common buckwheat, a distylous crop. Ann. Appl. Biol. 155, 121–129 (2009).Article 

Google Scholar 
Krkošková, B. & Mrazova, Z. Prophylactic components of buckwheat. Food Res. Int. 38, 561–568 (2005).Article 

Google Scholar 
Campbell, J. W., Irvin, A., Irvin, H., Stanley-Stahr, C. & Ellis, J. D. Insect visitors to flowering buckwheat, Fagopyrum esculentum (Polygonales: Polygonaceae), in north-central Florida. Fla. Entomol. 99, 264–268 (2016).Article 

Google Scholar 
Hadley, N. F. Water Relations of Terrestrial Arthropods (CUP Archive, 1994).
Google Scholar 
Sgolastra, F. et al. Temporal activity patterns in a flower visitor community of Dictamnus albus in relation to some biotic and abiotic factors. Bull. Insectol. 69, 291–300 (2016).
Google Scholar 
Vicens, N. & Bosch, J. Weather-dependent pollinator activity in an apple orchard, with special reference to Osmia cornuta and Apis mellifera (Hymenoptera: Megachilidae and Apidae). Environ. Entomol. 29, 413–420 (2000).Article 

Google Scholar 
Carlucci, M. B., Brancalion, P. H., Rodrigues, R. R., Loyola, R. & Cianciaruso, M. V. Functional traits and ecosystem services in ecological restoration. Restor. Ecol. 28, 1372–1383 (2020).Article 

Google Scholar 
Lavorel, S. Plant functional effects on ecosystem services. (2013).Defra. (ed Food and Rural Affairs Department for Environment) (2019).Agency, J. M. Amedas, https://tenki.jp/past/2019/09/amedas/ (2019).Jacquemart, A.-L., Gillet, C. & Cawoy, V. Floral visitors and the importance of honey bee on buckwheat (Fagopyrum esculentum Moench) in central Belgium. J. Hortic. Sci. Biotechnol. 82, 104–108 (2007).Article 

Google Scholar 
Taki, H. et al. Effects of landscape metrics on Apis and non-Apis pollinators and seed set in common buckwheat. Basic Appl. Ecol. 11, 594–602 (2010).Article 

Google Scholar 
Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).Article 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012).MATH 

Google Scholar 
Dray S, et al. adespatial: Multivariate Multiscale Spatial Analysis. R package version 0.3-20, https://CRAN.R-project.org/package=adespatial. (2022).Benjamin, F. E., Reilly, J. R. & Winfree, R. Pollinator body size mediates the scale at which land use drives crop pollination services. J. Appl. Ecol. 51, 440–449 (2014).Article 

Google Scholar 
Földesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75 (2016).Article 

Google Scholar 
Oksanen J, et al. vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan. (2022)Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis (Chapman and Hall/CRC, 1995).Book 
MATH 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing (2019).Sasaki, H. & Wagatsuma, T. Bumblebees (Apidae: Hymenoptera) are the main pollinators of common buckwheat, Fogopyrum esculentum, in Hokkaido, Japan. Appl. Entomol. Zool. 42, 659–661 (2007).Article 

Google Scholar 
Nagano, Y., Miyashita, T., Taki, H. & Yokoi, T. Diversity of co-flowering plants at field margins potentially sustains an abundance of insects visiting buckwheat, Fagopyrum esculentum, in an agricultural landscape. Ecol. Res. 36, 882–891 (2021).Article 

Google Scholar 
Samra, S., Samocha, Y., Eisikowitch, D. & Vaknin, Y. Can ants equal honeybees as effective pollinators of the energy crop Jatropha curcas L. under Mediterranean conditions?. Gcb Bioenergy 6, 756–767 (2014).Article 

Google Scholar 
Sugiura, N., Miyazaki, S. & Nagaishi, S. A supplementary contribution of ants in the pollination of an orchid, Epipactis thunbergii, usually pollinated by hover flies. Plant Syst. Evol. 258, 17–26 (2006).Article 

Google Scholar 
Natsume, K., Hayashi, S. & Miyashita, T. Ants are effective pollinators of common buckwheat Fagopyrum esculentum. Agric. For. Entomol. 24, 446–452 (2022).Article 

Google Scholar 
Carvalheiro, L. G., Seymour, C. L., Nicolson, S. W. & Veldtman, R. Creating patches of native flowers facilitates crop pollination in large agricultural fields: Mango as a case study. J. Appl. Ecol. 49, 1373–1383 (2012).Article 

Google Scholar 
Michiyama, H., Arikuni, M. & Hirano, T. Effect of air temperature on the growth, flowering and ripening in common buckwheat. In The Procceeding of the 8th ISB (2001)Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199-U196. https://doi.org/10.1038/nature10282 (2011).Article 
ADS 
CAS 

Google Scholar 
McCain, C. M. & Colwell, R. K. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecol. Lett. 14, 1236–1245 (2011).Article 

Google Scholar 
Choi, S.-W. Effects of weather factors on the abundance and diversity of moths in a temperate deciduous mixed forest of Korea. Zool. Sci. 25, 53–58 (2008).Article 

Google Scholar 
Feldmeier, S. et al. Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models. Divers. Distrib. 24, 1047–1060 (2018).Article 

Google Scholar  More

Pawlowski, J. et al. CBOL Protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLOS Biol. 10, e1001419 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
del Campo, J. et al. The others: our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29, 252–259 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Handbook of the Protists (Springer, 2017). https://doi.org/10.1007/978-3-319-28149-0.Lang, B. F., O’Kelly, C., Nerad, T., Gray, M. W. & Burger, G. The closest unicellular relatives of animals. Curr. Biol. 12, 1773–1778 (2002).Article 
CAS 
PubMed 

Google Scholar 
del Campo, J. et al. Ecological and evolutionary significance of novel protist lineages. Eur. J. Protistol. 55, 4–11 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Grau-Bové, X. et al. Dynamics of genomic innovation in the unicellular ancestry of animals. Life 6, e26036 (2017).
Google Scholar 
Gawryluk, R. M. R. et al. Non-photosynthetic predators are sister to red algae. Nature 572, 240–243 (2019).Article 
CAS 
PubMed 

Google Scholar 
Gabr, A., Grossman, A. R. & Bhattacharya, D. Paulinella, a model for understanding plastid primary endosymbiosis. J. Phycol. 56, 837–843 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gao, Z., Karlsson, I., Geisen, S., Kowalchuk, G. & Jousset, A. Protists: Puppet masters of the rhizosphere microbiome. Trends Plant Sci. 24, 165–176 (2019).Article 
CAS 
PubMed 

Google Scholar 
Caron, D. A. New accomplishments and approaches for assessing protistan diversity and ecology in natural ecosystems. Bioscience 59, 287–299 (2009).Article 

Google Scholar 
Gooday, A. J., Schoenle, A., Dolan, J. R. & Arndt, H. Protist diversity and function in the dark ocean: Challenging the paradigms of deep-sea ecology with special emphasis on foraminiferans and naked protists. Eur. J. Protistol. 75, 125721 (2020).Article 
PubMed 

Google Scholar 
Stoecker, D. K., Johnson, M. D., de Vargas, C. & Not, F. Acquired phototrophy in aquatic protists. Aquat. Microb. Ecol. 57, 279–310 (2009).Article 

Google Scholar 
Strom, S. L., Benner, R., Ziegler, S. & Dagg, M. J. Planktonic grazers are a potentially important source of marine dissolved organic carbon. Limnol. Oceanogr. 42, 1364–1374 (1997).Article 
ADS 
CAS 

Google Scholar 
Orsi, W. D. et al. Identifying protist consumers of photosynthetic picoeukaryotes in the surface ocean using stable isotope probing. Environ. Microbiol. 20, 815–827 (2018).Article 
CAS 
PubMed 

Google Scholar 
Corno, G. & Jürgens, K. Direct and indirect effects of protist predation on population size structure of a bacterial strain with high phenotypic plasticity. Appl. Environ. Microbiol. 72, 78–86 (2006).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mahé, F. et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat. Ecol. Evol. 1, 91 (2017).Article 
PubMed 

Google Scholar 
Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

Google Scholar 
Epstein, S. & López-García, P. “Missing” protists: a molecular prospective. Biodivers. Conserv. 17, 261–276 (2008).Article 

Google Scholar 
López-García, P., Rodríguez-Valera, F., Pedrós-Alió, C. & Moreira, D. Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409, 603–607 (2001).Article 
ADS 
PubMed 

Google Scholar 
Lovejoy, C., Massana, R. & Pedrós-Alió, C. Diversity and distribution of marine microbial eukaryotes in the Arctic Ocean and adjacent seas. Appl. Environ. Microbiol. 72, 3085–3095 (2006).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Worden, A. Z., Cuvelier, M. L. & Bartlett, D. H. In-depth analyses of marine microbial community genomics. Trends Microbiol. 14, 331–336 (2006).Article 
CAS 
PubMed 

Google Scholar 
Countway, P. D. et al. Distinct protistan assemblages characterize the euphotic zone and deep sea (2500 m) of the western North Atlantic (Sargasso Sea and Gulf Stream). Environ. Microbiol. 9, 1219–1232 (2007).Article 
CAS 
PubMed 

Google Scholar 
Massana, R. & Pedrós-Alió, C. Unveiling new microbial eukaryotes in the surface ocean. Curr. Opin. Microbiol. 11, 213–218 (2008).Article 
PubMed 

Google Scholar 
Alexander, E. et al. Microbial eukaryotes in the hypersaline anoxic L’Atalante deep-sea basin. Environ. Microbiol. 11, 360–381 (2009).Article 
CAS 
PubMed 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).Article 
CAS 
PubMed 

Google Scholar 
Logares, R. et al. Patterns of rare and abundant marine microbial eukaryotes. Curr. Biol. 24, 813–821 (2014).Article 
CAS 
PubMed 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 150 (2015).Article 

Google Scholar 
Fell, J. W., Scorzetti, G., Connell, L. & Craig, S. Biodiversity of micro-eukaryotes in Antarctic Dry Valley soils with More

Strains belonging to the same species display distinct growth dynamics on the marine polysaccharide alginateWe first quantified the growth dynamics of the 12 Vibrionaceae strains (Supplementary Table 1) on alginate in well-mixed batch cultures. Growth of populations was initiated at approximately the same inoculum density (105 colony forming units (c.f.u.) ml−1). We tracked the growth dynamics by measuring the optical density at 600 nm and compared the maximum population size reached over the course of 36 h (Fig. 1 and S1). We found significant differences in the maximal optical density achieved by different strains within each species (Fig. 1 and S1). In V. splendidus, strains 12B01 and FF6 reached a lower maximum population size compared to strains 1S124 and 13B01 (Fig. 1 and S1A). In V. cyclitrophicus, strain ZF270 reached a lower maximum population size compared to strains 1F175, 1F111, and ZF28 (Fig. 1 and S1A). Similarly, in V. sp. F13, strain 9ZC77 reached a lower maximum population size than strains 9CS106, 9ZC13, and ZF57 (Fig. 1 and S1A). These findings suggest that some strains are limited in their growth abilities in well-mixed environments, perhaps as a consequence of differences in the amount and activity of enzymes they release (Supplementary Table 1).Fig. 1: Vibrionaceae strains differ in their growth dynamics on the marine polysaccharide alginate under well-mixed conditions.Maximum optical density (measured at 600 nm) achieved by populations of strains belonging to Vibrio splendidus, Vibrio cyclitrophicus, and Vibrio sp. F13 during the course of a 36 h growth cycle on the same concentration (0.1% weight/volume) of the polysaccharide alginate. Points and error bars indicate the mean of measurements across populations within each ecotype (npopulations = 3) and the 95% confidence interval (CI), respectively. Different letters indicate statistically significant differences between strains within one species (One-way ANOVA and Dunnett’s post-hoc test; V. splendidus: p  More

Moore, J. An overview of parasite-induced behavioral alterations – and some lessons from bats. J. Exp. Biol. 216, 11–17 (2012).Article 

Google Scholar 
Nunn, C. L. & Altizer, S. Infectious Diseases in Primates: Behavior, Ecology and Evolution (Oxford University Press, 2006).Book 

Google Scholar 
Hutchings, M. R., Athanasiadou, S., Kyriazakis, I. & Gordon, I. J. Nutrition and Behaviour Group Symposium on ‘Exploitation of medicinal properties of plants by animals and man through food intake and foraging behaviour’: Can animals use foraging behaviour to combat parasites?. Proc. Nutr. Soc. 62, 361–370 (2003).Article 

Google Scholar 
Hawley, D. M., Etienne, R. S., Ezenwa, V. O. & Jolles, A. E. Does animal behavior underlie covariation between hosts’ exposure to infectious agents and susceptibility to infection? Implications for disease dynamics. Integr. Comp. Biol. 51, 528–539 (2011).Article 

Google Scholar 
Rimbach, R. et al. Brown spider monkeys (Ateles hybridus): a model for differentiating the role of social networks and physical contact on parasite transmission dynamics. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140110 (2015).Article 

Google Scholar 
Friant, S., Ziegler, T. E. & Goldberg, T. L. Changes in physiological stress and behaviour in semi-free-ranging red-capped mangabeys (Cercocebus torquatus) following antiparasitic treatment. Proc. R. Soc. B Biol. Sci. 283, 20161201 (2016).Article 

Google Scholar 
Hudson, P. J. & Dobson, A. P. Macroparasites: Observed patterns in naturally fluctuating animal populations. In Ecology of infectious diseases in natural populations (eds Grenfell, B. T. & Dobson, A. P.) 144–176 (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511629396.006.Chapter 

Google Scholar 
Murray, D. L., Lloyd, B. K. & Cary, J. R. Do parasitism and nutritional status interact to affect production in snowshoe hares?. Ecology 79, 1209–1222 (1998).Article 

Google Scholar 
Coop, R. L. & Holmes, P. H. Nutrition and parasite interaction. Int. J. Parasitol. 26, 951–962 (1996).Article 
CAS 

Google Scholar 
Møller, A. P., de Lope, F., Moreno, J., González, G. & Pérez, J. J. Ectoparasites and host energetics: House martin bugs and house martin nestlings. Oecologia 98, 263–268 (1994).Article 
ADS 

Google Scholar 
Munger, J. C. & Karasov, W. H. Sublethal parasites and host energy budgets: Tapeworm infection in white-footed mice. Ecology 70, 904–921 (1989).Article 

Google Scholar 
Hicks, O. et al. The energetic cost of parasitism in a wild population. Proc. R. Soc. B Biol. Sci. 285, 20180489 (2018).Article 

Google Scholar 
Sánchez, C. A. et al. On the relationship between body condition and parasite infection in wildlife: A review and meta-analysis. Ecol. Lett. 21, 1869–1884 (2018).Article 

Google Scholar 
Kyriazakis, I., Tolkamp, B. J. & Hutchings, M. R. Towards a functional explanation for the occurrence of anorexia during parasitic infections. Anim. Behav. 56, 265–274 (1998).Article 
CAS 

Google Scholar 
Hart, B. L. Behavioral adaptations to pathogens and parasites: Five strategies. Neurosci. Biobehav. Rev. 14, 273–294 (1990).Article 
CAS 

Google Scholar 
Lopes, P. C., Block, P. & König, B. Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Pelletier, F. & Festa-Bianchet, M. Effects of body mass, age, dominance and parasite load on foraging time of bighorn rams. Ovis canadensis. Behav. Ecol. Sociobiol. 56, 546–551 (2004).Article 

Google Scholar 
Bonneaud, C. et al. Assessing the cost of mounting an immune response. Am. Nat. 161, 367–379 (2003).Article 

Google Scholar 
Hart, B. L. The behavior of sick animals. Vet. Clin. North Am. Small Anim. Pract. 21, 225–237 (1991).Article 
CAS 

Google Scholar 
Poulin, R. Meta-analysis of parasite-induced behavioural changes. Anim. Behav. 48, 137–146 (1994).Article 

Google Scholar 
Janson, C. H. Toward an experiemental socioecology of primates. Examples from Argentine brown capuchin monkeys (Cebus apella nigritus). In Adaptive Radiations of Neotropical Primates (eds Janson, C. H. et al.) 309–325 (Plenum Press, 1996).Chapter 

Google Scholar 
Robinson, J. G. Seasonal variation in use of time and space by the wedge-capped capuchin monkey, Cebus olivaceus: Implications for foraging theory. Smithson. Contrib. Zool. https://doi.org/10.5479/si.00810282.431 (1986).Article 

Google Scholar 
Saj, T., Sicotte, P. & Paterson, J. D. Influence of human food consumption on the time budget of vervets. Int. J. Primatol. 20, 977–994 (1999).Article 

Google Scholar 
Ghai, R. R., Fugère, V., Chapman, C. A., Goldberg, T. L. & Davies, T. J. Sickness behaviour associated with non-lethal infections in wild primates. Proc. Biol. Sci. 282, 20151436 (2015).
Google Scholar 
Blersch, R. et al. Sick and tired: Sickness behaviour, polyparasitism and food stress in a gregarious mammal. Behav. Ecol. Sociobiol. 75, 169 (2021).Article 

Google Scholar 
Müller-Klein, N. et al. Physiological and social consequences of gastrointestinal nematode infection in a nonhuman primate. Behav. Ecol. 30, 322–335 (2019).Article 

Google Scholar 
Chapman, C. A. et al. Social behaviours and networks of vervet monkeys are influenced by gastrointestinal parasites. PLoS ONE 11, e0161113 (2016).Article 

Google Scholar 
Owen-Ashley, N. T. & Wingfield, J. C. Acute phase responses of passerine birds: characterization and seasonal variation. J. Ornithol. 148, 583–591 (2007).Article 

Google Scholar 
Owen-Ashley, N. T. & Wingfield, J. C. Seasonal modulation of sickness behavior in free-living northwestern song sparrows (Melospiza melodia morphna). J. Exp. Biol. 209, 3062–3070 (2006).Article 

Google Scholar 
Janson, C. H. & Di Bitetti, M. S. Experimental analysis of food detection in capuchin monkeys: Effects of distance, travel speed, and resource size. Behav. Ecol. Sociobiol. 41, 17–24 (1997).Article 

Google Scholar 
Di Bitetti, M. S. Food-associated calls in the tufted capuchin monkey (Cebus apella). PhD Thesis. (Stony Brook University, New York, 2001).Di Bitetti, M. S. & Janson, C. H. Reproductive socioecology of tufted capuchins (Cebus apella nigritus) in Norteastern Argentina. Int. J. Primatol. 22, 127–142 (2001).Article 

Google Scholar 
Janson, C., Baldovino, M. C. & Di Bitetti, M. The group life cycle and demography of brown capuchin monkeys (Cebus [apella] nigritus) in Iguazú National Park, Argentina. In Long-Term Field Studies of Primates (eds Kappeler, P. M. & Watts, D. P.) 185–212 (Springer, Berlin, 2012). https://doi.org/10.1007/978-3-642-22514-7_9.Chapter 

Google Scholar 
Robinson, J. C. & Galán Saúco, V. Bananas and plantains. (Crop production science in horticulture series N. 19, CAB International, 2010). https://doi.org/10.1079/9781845936587.0000Tiddi, B., Pfoh, R. & Agostini, I. The impact of food provisioning on parasite infection in wild black capuchin monkeys: A network approach. Primates 60, 297–306 (2019).Article 

Google Scholar 
Agostini, I., Vanderhoeven, E., Di Bitetti, M. S. & Beldomenico, P. M. Experimental testing of reciprocal effects of nutrition and parasitism in wild black capuchin monkeys. Sci. Rep. 7, 1–11 (2017).Article 

Google Scholar 
de Vries, H., Netto, W. J. & Hanegraaf, P. L. H. Matman: a program for the analysis of sociometric matrices and behavioural transition matrices. Behaviour 125, 157–175 (1993).Article 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour (Cambridge University Press, 1993). https://doi.org/10.1017/cbo9780511810893.Book 

Google Scholar 
Cox, D. D. & Todd, A. C. Survey of gastrointestinal parasitism in Wisconsin dairy cattle. J. Am. Vet. Med. Assoc. 141, 706–709 (1962).CAS 

Google Scholar 
Ballweber, L. R., Beugnet, F., Marchiondo, A. A. & Payne, P. A. American association of veterinary parasitologists’ review of veterinary fecal flotation methods and factors influencing their accuracy and use—Is there really one best technique?. Vet. Parasitol. 204, 73–80 (2014).Article 
CAS 

Google Scholar 
Godfrey, S. S. Networks and the ecology of parasite transmission: a framework for wildlife parasitology. Int. J. Parasitol. Parasites Wildl. 2, 235–245 (2013).Article 

Google Scholar 
Sosa, S., Sueur, C. & Puga-Gonzalez, I. Network measures in animal social network analysis: Their strengths, limits, interpretations and uses. Methods Ecol. Evol. 2020, 1–12 (2020).
Google Scholar 
Sosa, S. et al. A multilevel statistical toolkit to study animal social networks: The Animal Network Toolkit Software (ANTs) R package. Sci. Rep. 10, 12507 (2020).Article 
ADS 
CAS 

Google Scholar 
Croft, D. P., Madden, J. R., Franks, D. W. & James, R. Hypothesis testing in animal social networks. Trends Ecol. Evol. 26, 502–507 (2011).Article 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Ecological Modelling (Springer, 2002). https://doi.org/10.1016/j.ecolmodel.2003.11.004Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Barton, K. MuMIn: Multi-model inference. R package version 1.15.6. 63 (2016). citeulike:11961261Carlton, E. D., Demas, G. E. & French, S. S. Leptin, a neuroendocrine mediator of immune responses, inflammation, and sickness behaviors. Horm. Behav. 62, 272–279 (2012).Article 
CAS 

Google Scholar 
Tizard, I. Sickness behavior, its mechanisms and significance. Anim. Health Res. Rev. 9, 87–99 (2008).Article 

Google Scholar 
Inoue, W. & Luheshi, G. N. Acute starvation alters lipopolysaccharide-induced fever in leptin-dependent and -independent mechanisms in rats. Am. J. Physiol. Regul. Integr. Comp. Physiol. 299, R1709-19 (2010).Article 

Google Scholar 
Macdonald, L., Radler, M., Paolini, A. G. & Kent, S. Calorie restriction attenuates LPS-induced sickness behavior and shifts hypothalamic signaling pathways to an antiinflammatory bias. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, 172–184 (2011).Article 

Google Scholar 
Wisse, B. E. et al. Physiological regulation of hypothalamic IL-1β gene expression by leptin and glucocorticoids: implications for energy homeostasis. Am. J. Physiol. Endocrinol. Metab. 287, R1107–R1113 (2004).Article 

Google Scholar 
Pohl, J., Woodside, B. & Luheshi, G. N. Changes in hypothalamically mediated acute-phase inflammatory responses to lipopolysaccharide in diet-induced obese rats. Endocrinology 150, 4901–4910 (2009).Article 
CAS 

Google Scholar 
Bretscher, P. On analyzing how the Th1/Th2 phenotype of an immune response is determined: classical observations must not be ignored. Front. Immunol. 10, 1–7 (2019).Article 

Google Scholar 
Poppi, D. P., Sykes, A. R. & Dynes, R. A. The effect of endoparasitism on host nutrition – the implications for nutrient manipulation. Proc. New Zeal. Soc. Anim. Prod. 50, 237–243 (1990).
Google Scholar 
Coulson, G., Cripps, J. K., Garnick, S., Bristow, V. & Beveridge, I. Parasite insight: assessing fitness costs, infection risks and foraging benefits relating to gastrointestinal nematodes in wild mammalian herbivores. Philos. Trans. R. Soc. B Biol. Sci. 373, 197 (2018).Article 

Google Scholar 
Worsley-Tonks, K. E. L. & Ezenwa, V. O. Anthelmintic treatment affects behavioural time allocation in a free-ranging ungulate. Anim. Behav. 108, 47–54 (2015).Article 

Google Scholar 
Jones, O. R., Anderson, R. M. & Pilkington, J. G. Parasite-induced anorexia in a free-ranging mammalian herbivore: An experimental test using Soay sheep. Can. J. Zool. 84, 685–692 (2006).Article 

Google Scholar 
Cripps, J. K., Martin, J. K. & Coulson, G. Anthelmintic treatment does not change foraging strategies of female eastern grey kangaroos, Macropus giganteus. PLoS ONE 11, e0147384 (2016).Article 

Google Scholar 
Giles, N. Predation risk and reduced foraging activity in fish: experiments with parasitized and non-parasitized three-spined sticklebacks, Gasterosteus aculeatus L.. J. Fish Biol. 31, 37–44 (1987).Article 

Google Scholar 
Knutie, S. A., Wilkinson, C. L., Wu, Q. C., Ortega, C. N. & Rohr, J. R. Host resistance and tolerance of parasitic gut worms depend on resource availability. Oecologia 183, 1031–1040 (2017).Article 
ADS 

Google Scholar 
Lopes, P. C., French, S. S., Woodhams, D. C. & Binning, S. A. Metabolic response of dolphins to short-term fasting reveals physiological changes that differ from the traditional fasting model. J. Exp. Biol. 224, jeb225847 (2021).Article 

Google Scholar 
Behringer, D. C., Butler, M. J. & Shields, J. D. Ecology: Avoidance of disease by social lobsters. Nature 441, 421 (2006).Article 
ADS 
CAS 

Google Scholar 
Poirotte, C. et al. Mandrills use olfaction to socially avoid parasitized conspecifics. Sci. Adv. 3, e1601721 (2017).Article 
ADS 

Google Scholar  More

Ouellet, V. et al. Sci. Total Environ. 736, 139679 (2020).Article 
CAS 
PubMed 

Google Scholar 
Sutadian, A. D., Muttil, N., Yilmaz, A. G. & Perera, B. J. C. Environ. Monit. Assess. 188, 58 (2016).Article 
PubMed 

Google Scholar 
Murdoch, P. S., Baron, J. S. & Miller, T. L. J. Am. Water Resour. Assoc. 36, 347–366 (2000).Article 
CAS 

Google Scholar 
Hannah, D. M. & Garner, G. Prog Phys Geogr. 39, 68–92 (2015).Article 

Google Scholar 
Abbott, B. W. et al. Nat. Geosci. 12, 533–540 (2019).Article 
CAS 

Google Scholar 
Grill, G. et al. Nature 569, 215–221 (2019).Article 
CAS 
PubMed 

Google Scholar 
Hermanson, L. et al. Bull. Am. Meteorol. Soc. 103, E1117–E1129 (2022).Article 

Google Scholar 
Webb, B. W., Hannah, D. M., Moore, R. D., Brown, L. E. & Nobilis, F. Hydrol. Process. 22, 902–918 (2008).Article 

Google Scholar 
Hester, E. T. & Doyle, M. W. J. Am. Water Resour. Assoc. 47, 571–587 (2011).Article 

Google Scholar 
Schliemann, S. A., Grevstad, N. & Brazeau, R. H. Hydrol. Process 35, e14001 (2021).Article 

Google Scholar 
Jackson, F. L., Fryer, R. J., Hannah, D. M., Millar, C. P. & Malcolm, I. A. Sci. Total Environ. 612, 1543–1558 (2018).Article 
CAS 
PubMed 

Google Scholar 
O’Sullivan, A. M., Devito, K. J. & Curry, R. A. Catena 177, 70–83 (2019).Article 

Google Scholar 
Chang, H. & Psaris, M. Sci. Total Environ. 461, 587–600 (2013).Article 
PubMed 

Google Scholar 
Hester, E. T. & Bauman, K. S. J. Am. Water Resour. Assoc. 49, 328–342 (2013).Article 

Google Scholar 
Croghan, D., Van Loon, A. F., Sadler, J. P., Bradley, C. & Hannah, D. M. Hydrol. Process. 33, 144–159 (2018).Article 

Google Scholar 
Levia, D. F. et al. Nat. Geosci. 13, 656–658 (2020).Article 
CAS 

Google Scholar 
Nelson, K. C. & Palmer, M. A. J. Am. Water Resour. Assoc 43, 440–452 (2007).Article 

Google Scholar 
Heggenes, J. et al. River Res. Appl. 37, 743–765 (2021).Article 

Google Scholar 
Menberg, K., Blum, P., Kurylyk, B. L. & Bayer, P. Hydrol. Earth Syst. Sci. 18, 4453–4466 (2014).Article 

Google Scholar 
Tissen, C., Benz, S. A., Menberg, K., Bayer, P. & Blum, P. Environ. Res. Lett. 14, 104012 (2019).Article 
CAS 

Google Scholar 
Hannah, D. M. et al. Hydrol. Process. 36, e14525 (2022).Article 

Google Scholar 
Carothers, C. et al. Ecol. Soc. https://doi.org/10.5751/ES-11972-260116 (2021).Dugdale, S. J., Hannah, D. M. & Malcolm, I. A. Earth Sci. Rev. 175, 97–113 (2017).Article 

Google Scholar 
Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P. & van Beek, L. P. H. Water Resour. Res. 55, 2760–2778 (2019).Article 

Google Scholar 
Tavares, M. H. et al. Remote Sens. Environ. 241, 11172 (2020).Article 

Google Scholar 
Dugdale, S. J., Klaus, J. & Hannah, D. M. Water Resour. Res. 58, e2021WR031168 (2022).Article 

Google Scholar 
Mao, F. et al. Environ. Sci. Technol. 54, 9145–9158 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hannah, D. M. et al. Hydrol. Process. 25, 1191–1200 (2011).Article 

Google Scholar 
Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. Earth Syst. Sci. Data 10, 765–785 (2018).Article 

Google Scholar  More

Commensal rodents serve as important reservoirs of rodent-borne pathogens. Efforts to reduce the risk of pathogen transmission include decimating rodent populations, altering access pathways, upholding good waste management practices and denying easy access to food sources. In our study, we examined the incidence of rodent activity in waste collection premises in public residential estates in Singapore and examined the factors associated with rodent activity to inform the priority of rodent control measures of resource limited municipal estate managers.Of the three types of waste collection premises, rodent activity had the highest incidence in refuse bin centres followed by CRCs and IRC bin chambers. Refuse bin centres are prone to refuse spillage because refuse is manually transferred from refuse collection carts into bulk bins and refuse compactors located within the centres. Bin centres tend to be larger than CRCs and IRC bin chambers and the storage of bulky waste that provide additional areas of rodent harbourage are a common sight in Singapore. IRC chambers and refuse bin centres in combination far outnumber CRCs, and the former two are a distinct characteristic of older public housing estates in Singapore. This suggests that older public housing estates have a higher propensity for rodent infestation compared to newer ones. Aging infrastructure can also provide a greater number of harbourage areas and alternate access pathways for rodent travel that increase their ability to obtain food sources. Our study findings were in support of previous studies which found that older infrastructure was associated with a greater likelihood of rodent activity22,23.We also found that the number of IRC bin chutes was positively associated with rodent infestation. Fluids from food waste in IRC bin chambers are drained directly into a sanitary line that is common to all other bin chambers within the same building. A possible explanation therefore is that rodents which find their way into the sanitary line can easily access all bin chambers in the same building. This suggests that preventing individual bin chamber access may reduce food availability to rodents which traverse the sanitary line in search of food sources.In the present study, we observed that rodent sightings were relatively higher in some months in the first half of the calendar year compared to the second half. Even though our estimates were positive, those for some months were not statistically significant. In Singapore, end-December, January to February are usually associated with increased food production due to the year-end (Christmas and New Year celebrations) and early-year (Chinese New Year) festivities. A proportionate increase in food waste over that period could improve survivability of rodents that leads to increased mating and reproduction. We therefore postulate that the higher seasonal rodent activity is plausible but recommend that future studies be conducted with sufficient longitude to examine the differences in the seasonal pattern across the three categories of premises more closely. A previous study in Harbin, China27 reported a seasonal pattern in the age composition of R. norvegicus while an ecological study on R. norvegicus in Salvador Brazil did not find any difference in the number of rats trapped between the dry and rainy seasons28. The inconsistent seasonal findings between studies could be due to the differences in the climate, degree of urbanization and environmental conditions of study locations.The relative rise in rodent activity in the first half of the year coupled with older estates being at greater risk of rodent activity suggest that municipal town councils which prioritize regular infrastructural repairs and improvements in older estates and complete them in the second half of each calendar year would help mitigate the anticipated rise in the first half of the new calendar year.In our study, we examined the relations between visual cues and rodent activity to help estate managers prioritise their control efforts. We found that rodent droppings were a common positive predictor of rodent activity across all three categories of waste collection premises. In particular, the odds of droppings in IRC bin chambers were the highest among the three categories of premises. We hypothesize that the probability of rodent dropping sightings was in part related to the accessibility of food waste and thus time spent by rodents within the respective waste collection premises. Each IRC chamber contains an open top bin that receives waste that is disposed down the IRC chamber chute. Food waste in IRC bin chambers are thus more easily accessed by rodents compared to in CRCs where waste is stored in a compactor and in bin centres where bulk bins are covered until the waste is compacted or collected.In Salvador, Brazil, the presence of Rattus norvegicus droppings were independently associated with an increased risk of Leptospira infection in humans29. Further research on site-specific Leptospira infection risks in Singapore are required to affirm the utility of droppings as an indicator for Leptospira infection risk. In addition, rub marks and gnaw marks were also positive predictors of rodent activity in CRCs and IRC bin chambers. A study in Chile reported that gnaw marks and holes, as well as grease or rub marks left behind by rodent travel were indicators of rodent activity30. A previous study carried out in an urban city in Taiwan reported that rodent droppings and rub marks were well correlated with rodent infestation31. Our findings, which were in support of these previous studies, suggest that estate managers can maximise the cost effectiveness of their resources by focusing their control efforts based on visual cues without relying solely on trapping activities for surveillance.We found a positive relationship between the number of rodent burrows and rodent activity in all three waste collection premises, though this was only significant for refuse bin centres. That the direction of effect for burrows was consistent these three premises, was a reassuring observation. It is possible that we did not have enough study power to establish the observed positive relations in CRCs and IRC bin chambers. Therefore future studies should seek to confirm our findings. R. norvegicus excavate extensive burrow systems that are able to house a large number of rats32. They exhibit a strong preference for creating burrows in loose soil and on sloping terrain33 and construct shallow burrows in close proximity to water bodies and food sources34. As rodent burrows are primarily used for nesting, food storage and harbourage purposes35, burrows can provide important information about the extent of rodent activity in an area and may be used as an indicator for estate managers to focus their investigations.A previous study in New York, United States found that the presence of numerous restaurants, or having older infrastructure were associated with increased levels of R. norvegicus22. Unexpectedly, we did not find any evidence that the number of dining establishments was associated with rodent activity. However, instances of rodent activity have been reported in food establishments in Singapore36,37,38. We hypothesize that rodent movement is restricted to the surrounding area of the food establishments due to the plethora of food available, with little reason for rodents to venture into waste collection premises. Future studies examining the relationship between rodent activity in food establishments and waste collection premises are required to confirm this.In our study, the presence of gnaw marks (aOR: 5.61), rub marks (aOR: 5.04) in CRCs and rodent droppings in CRCs (aOR: 6.20), IRC bin chambers (aOR: 90.84) and bin centres (aOR: 3.61) had the largest strengths of association with rodent activity. Comparatively, in a study in Johannesburg, South Africa, predictors such as dampness (aOR: 2.54) and cracks (aOR: 1.92) in homes had relatively smaller effects on rodent activity20, while a study in Salvador, Brazil found relatively larger effects of homes with dilapidated fences and walls (aOR: 8.95) and those built on earthen slopes (aOR: 4.95)21. This suggests that rodent activity can be strongly influenced by site- and setting-specific factors, and supports the body of evidence on the strong adaptability of rodents in our urban environment”.Urban environments have the capacity to alter the biology of the pathogens, hosts and vectors, which can influence disease transmission39. The proximate setting of dense urban environments allows for close contact between humans and synanthropic rodents, thereby increasing the transmission risk of zoonotic diseases4. In addition to causing diseases in human populations, urban rats are also known to compromise food safety, damage infrastructure and cause mental health distress25,40. The responsibility of rodent control in residential estates is important but may be one among many other competing public health and estate management responsibilities that municipal town councils have to undertake. Consequently, estate managers have to prioritize their limited resources in order to maximise the cost effectiveness of their resource allocation choices. Based on our study findings, we recommend that estate managers adopt a risk-based approach in vector control resource allocation in waste collection premises according to infrastructural age and visual cues for rodent activity.IRC bin chambers which are a distinct feature of the oldest residential buildings, were observed with a substantially higher odds of rodent activity compared to the other categories of waste collection premises. This suggests that rodent control resource allocation should be prioritized in older residential estates. The clear seasonal pattern of rodent activity in CRCs suggests that estate managers can increase their rodent control activities thereat in the first half of the year. Finally, easy access to food waste directly increases the probability of survival and consequently the rodent population size. Future research should examine the quality of municipal solid waste management and the waste processing flow in residential estates to determine how rodent access to food waste can be further minimized to reduce the population of rodents.Study strengths and limitationsWe analysed data from all public residential estates in Singapore; our findings are thus generalizable at the national level. The use of outcomes and independent measures from individual waste collection premises over multiple cycles of inspection provided stronger evidence for causal inferences. We analysed data over 12 months to account for within-year variations that could influence the outcome measure. Rodents were visually identified without molecular speciation because no trapping was carried out. Though the majority of rodents were observed to be Rattus norvegicus, which is the most common species of rodents in public housing estates in Singapore, we could not rule out misclassification of rodents. However, our findings remain relevant for municipal authorities seeking to prioritize resources for vector control in waste collection premises under their care. More

Plankton are microscopic organisms at the base of aquatic food webs and therefore essential to all life on Earth. In our view, international legal protection of plankton is urgently needed because of their high susceptibility to the effects of climate change, including ocean warming and acidification.
Competing Interests
The authors declare no competing interests. More