Philippa Kaur

StatementsWe report our study in accordance with ARRIVE guidelines.Structure of artificial turf of ICUA schematic illustration of a ground plan of the artificial turf sports field of the ICU is shown in Fig. 1. This artificial turf was installed in 2013 by Japanese company B. The field is surrounded by ditches, and there are three drains that connect to sewer pipes. The artificial turf field of TGU was installed in 2011 by Japanese company C.Characterization of rubber tips of artificial turf field of ICU and TGURT were collected from the artificial fields of ICU and TGU. RT for the artificial turf field of ICU were made of residual part of rubber for making tires, window frames and windshields of automobiles. RT of ICU consists of a mixture of EPDM (ethylene-propylene-diene) and SBR (styrene-butadiene rubber) (personal communication from a Japanese company B). The RT of TGU was made of rubber of the residual part of rubber for making tires, window frames, etc. (personal communication from a Japanese company C). Information on raw material of the RT was not manifested.RT collected from the fields (ICU and TGU) was sieved to estimate the particle sizes. The RT of the ICU varied from 0.053 to 3.35 mm, and that of TGU varied from 0.212 to 3.35 mm. The specific gravity of the RT was obtained as follows: A certain amount of RT was weighed and poured into a 10 ml graduated cylinder containing some water. The total volume of the RT was obtained by measuring the rise in the meniscus of the water. The specific gravities of the tested RT were 1.28 (ICU) and 1.28 (TGU). Elemental analyses of RT (ICU and TGU) were conducted using micro-PIXE line analysis47, and calcium, sulfur, zinc, and iron were detected, but lead was under the detection limit from the RT of both ICU and TGU.Sampling of sediments in the ditches of the fieldTo examine the migration of RT from the field to the ditches, approximately 200 g of sediments in the ditches was sampled at four different sites, D1–D4 (Fig. 1), in the ICU. The ditch surrounding the field is made by connecting U-shaped concrete blocks and concrete lids. The inner width, length, and depth of the block are 24, 60, and 24 cm, respectively. The size of the lid is 33 × 60 × 4.5 cm with 1.5 × 9.0 cm snicks at short sides, which make an opening of the ditch of 3.0 × 9.0 cm size between two lids.Each sample was weighed (wet weight) and washed with water using a fine sieve to remove the soil. After the removal of the soil, the sediment was dried, and RT was collected manually. The collected RT was weighed, and the percentages of RT in the sediments were calculated (weight/weight).Goldfish and crucian carpA common variety of goldfish C. auratus of different sizes were obtained from a fish merchant in Saitama Prefecture and from a pet shop in Tokyo and then kept in the ICU. Approximately 200 fish of four different sizes (large, body weight (BW) ~ 100 g; medium, BW, ~ 30 g; small-medium, BW, ~ 15 g; small, BW ~ 4.0 g) were kept in three 800-L stock tanks maintained at 20 °C under a 16-h light/8-h dark (16 L/8 D) photoperiod (lights on at 06:00). Small body size fish were kept in a floating cage in one of the stock tanks. The fish were fed commercial floating goldfish feed (Itosui) once a day ad libitum. The fish stock tanks had circulation filtration systems equipped with sand filters. The filter was cleaned every week to maintain the water quality. The health condition of the fish was judged by their appetite. All the experimental fish (mixed sex) in the present study were kept in stock tanks for over two weeks before they were used for experiments. A total of 127 goldfish were used for the present study. The sample size of each experiment was determined by the results of preliminary experiments. Our preliminary survey confirmed that the fish feed we used did not contain RT-like substances. Therefore, the sample sizes of the control groups (goldfish) were smaller than those of the experimental groups to sacrifice fewer fish. All goldfish and crucian carp experiments were conducted in the ICU.Approximately 30 wild juvenile crucian carp C. auratus subsp. 2 weighing 1.4–4.6 g were obtained from a fish merchant in Saitama Prefecture and kept in an 800-L stock tank in the same conditions as that for goldfish. A total of 16 crucian carp were used for the present study.For the experiments, fish were transferred from the stock tanks to experimental 60-L glass aquaria, which were maintained at 20 °C under a 16-h light/8-h dark (16 L/8 D) photoperiod (lights on at 06:00). The experimental aquaria had a running water system, and dechlorinated tap water was added at 20 ml/min. Plastic box filters were also set to each experimental aquarium to maintain water quality. When stock fish were transferred to experimental aquaria, fish were randomly allocated to the aquaria. All the methods for using goldfish and crucian carp were performed in accordance with the guidelines of the Animal Experimentation Committee of International Christian University. The conduct of the present study was approved by the Animal Experimentation Committee of International Christian University.Co-ingestion of feed and RT by goldfish of three different body sizesWe examined whether RT are ingested by goldfish with feed and whether the body size of fish affects the ingestion of RT using three different body sizes of fish, large, medium, and small. First, we conducted an experiment using large body size fish (N = 24; BW, 91.9 ± 21.6 g, mean and SD). Three goldfish of large body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation of the environment and sinking fish feed. Fish were fed 3.0 g of large-size feed (Japan Pet Design Co. Ltd.) once a day. On the fourth day, fish were fed a mixture of RT collected from the field (ICU, 300 mg) and large feed (3.0 g). Control fish were fed only fish feed. At 90 min after feeding, the fish were transferred to a pail containing 0.05% 2-phenoxyethanol solution and deeply anesthetized. After body weight measurement, fish were dissected. We observed the intestine to determine whether RT was ingested. When RT was observed in the intestine, we collected the tips and counted the number of tips in each fish. The experimental tests were repeated eight times, and the data were combined.Second, we conducted an experiment using medium body size fish (N = 24; BW, 30.4 ± 12.4 g). Three goldfish of medium body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 1.0 g of medium-size feed (Kyorin) once a day. On the fourth day, fish were fed a mixture of RT (ICU, 300 mg) and medium feed (1.0 g). Control fish were fed only fish feed. At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated eight times, and the data were combined.Third, we conducted an experiment using small body size fish (N = 40; BW, 4.4 ± 1.5 g). Four goldfish of small body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 0.5 g of small-size feed (Kyorin) once a day. On the fourth day, fish were fed a mixture of RT (ICU, 300 mg) and small feed (0.5 g). Because of the small size of fish, RT of small size particles (212–500 µm) were collected with sieves and used for the tests. Control fish were fed only fish feed. At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated ten times, and the data were combined.In the first three experiments, all three control groups showed no ingestion of RT. From the results of the three experiments, it was clear that our experimental system was not contaminated with RT. Therefore, we omitted making control groups for further experiments to decrease the number of fish sacrificed from the standpoint of fish welfare.Fourth, we examined whether RT collected from TGU was ingested by goldfish. We conducted an experiment using large body size fish (N = 12; BW, 140.3 ± 27.0 g). Three goldfish of large body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On the fourth day, fish were fed a mixture of RT (TGU, 300 mg) and large feed (3.0 g). At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated four times, and the data were combined.We conducted an additional experiment with a similar design to those of the four experiments to take photographs of the fish and RT using fish of small-medium body size (N = 9; BW, 12.8 ± 2.7 g). Three fish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for two days for acclimation. Fish were given 0.5 g of medium-size feed once a day. On the third day, fish were given a mixture of RT of ICU (30 pieces; size 0.5–1.0 mm) and medium feed (0.5 g). At 60 min after feeding, fish were anesthetized and dissected, and photographs of RT in the intestine were taken. The experimental tests were repeated three times, and the data were combined.Active ingestion of RT by goldfishWe examined whether goldfish actively ingest RT when RT are given without fish feed using large body size fish (N = 9; BW, 122.4 ± 20.8 g). Three fish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On the fourth day, fish were given 300 mg of RT (ICU) on the bottom of the aquarium. At 90 min after the placement of RT, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated three times, and the data were combined.Retention and elimination of ingested RT in the intestine of goldfishWe examined how long RT was retained in the intestine using large body size goldfish (N = 9; BW, 101.6 ± 11.4 g). Three goldfish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On Day 4, fish were given 1.0 g of RT (ICU). At 90 min after the placement of RT, each fish was individually transferred to three experimental 60-L glass aquaria. Then, each fish was fed 1.0 g of the feed. At 24 and 48 h (Day 5 and Day 6) after the transfer, we collected feces from fish and some water from the bottom of the aquaria. We observed whether RT was eliminated from the fish into the aquaria. When RT was observed in the feces and the bottom of the aquarium, we collected the RT and counted the number of RT. On Day 5, after the RT observation, each fish was fed 1.0 g of the feed. On Day 6, after RT observation in feces and water, the fish were anesthetized and dissected. We observed whether the intestine retained RT. The experimental tests were repeated three times, and the data were combined.Ingestion of RT by wild crucian carpWe examined whether wild Japanese crucian carp ingest RT. The experiment was conducted using juvenile crucian carp (N = 16, BW, 2.8 ± 0.9 g). Sixteen fish were transferred from the stock tank to three experimental 60-L glass aquaria (5 or 6 fish per aquarium) and kept for six days for acclimation. Fish were fed with 0.2 g of small-size feed once a day. On the seventh day, fish were fed a mixture of RT (ICU, 30 mg) and the small feed (0.2 g) or RT alone (30 mg). Because of the small size of fish, RT of small size particles (212–500 µm) were collected with sieves and used for the test. Control fish were fed only fish feed (0.2 g). At 6 h after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. More

Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).Article 
CAS 

Google Scholar 
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).Article 

Google Scholar 
Geiger, F. et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 11, 97–105 (2010).Article 
CAS 

Google Scholar 
Zhang, W., Ricketts, T. H., Kremen, C., Carney, K. & Swinton, S. M. Ecosystem services and dis-services to agriculture. Ecol. Econ. 64, 253–260 (2007).Article 

Google Scholar 
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).Article 
CAS 

Google Scholar 
Helming, K. et al. Managing soil functions for a sustainable bioeconomy-assessment framework and state of the art. Land Degrad. Dev. 29, 3112–3126 (2018).Article 

Google Scholar 
Rockström, J. et al. Sustainable intensification of agriculture for human prosperity and global sustainability. Ambio 46, 4–17 (2017).Article 

Google Scholar 
Lehmann, J., Bossio, D. A., Kögel-Knabner, I. & Rillig, M. C. The concept and future prospects of soil health. Nat. Rev. Earth Environ. 1, 544–553 (2020).Article 

Google Scholar 
Smith, J., Pearce, B. D. & Wolfe, M. S. Reconciling productivity with protection of the environment: Is temperate agroforestry the answer? Renew. Agric. Food Syst. 28, 80–92 (2013).Article 

Google Scholar 
European Commission. A Greener and Fairer CAP (EC, 2021).Grass, I. et al. Trade-offs between multifunctionality and profit in tropical smallholder landscapes. Nat. Commun. 11, 1186 (2020).Article 
CAS 

Google Scholar 
Mayer, S. et al. Soil organic carbon sequestration in temperate agroforestry systems – a meta-analysis. Agric. Ecosyst. Environ. 323, 107689 (2022).Article 
CAS 

Google Scholar 
Pardon, P. et al. Juglans regia (walnut) in temperate arable agroforestry systems: effects on soil characteristics, arthropod diversity and crop yield. Renew. Agric. Food Syst. 35, 533–549 (2020).Article 

Google Scholar 
Schmidt, M. et al. Nutrient saturation of crop monocultures and agroforestry indicated by nutrient response efficiency. Nutr. Cycl. Agroecosyst. 119, 69–82 (2021).Article 
CAS 

Google Scholar 
Beule, L. & Karlovsky, P. Tree rows in temperate agroforestry croplands alter the composition of soil bacterial communities. PLoS ONE 16, e0246919 (2021).Article 
CAS 

Google Scholar 
Palma, J. H. N. et al. Modeling environmental benefits of silvoarable agroforestry in Europe. Agric. Ecosyst. Environ. 119, 320–334 (2007).Article 

Google Scholar 
Kay, S. et al. Spatial similarities between European agroforestry systems and ecosystem services at the landscape scale. Agroforest Syst. 92, 1075–1089 (2018).Article 

Google Scholar 
Swieter, A., Langhof, M., Lamerre, J. & Greef, J. M. Long-term yields of oilseed rape and winter wheat in a short rotation alley cropping agroforestry system. Agroforest Syst. 93, 1853–1864 (2019).Article 

Google Scholar 
Ivezić, V., Yu, Y. & van der Werf, W. Crop yields in European agroforestry systems: a meta-analysis. Front. Sustain. Food Syst. 5, 606631 (2021).Article 

Google Scholar 
Cardinael, R. et al. High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system – combining experimental and modeling approaches. Biogeosciences 15, 297–317 (2018).Article 
CAS 

Google Scholar 
Smith, P. Carbon sequestration in croplands: the potential in Europe and the global context. Eur. J. Agron. 20, 229–236 (2004).Article 
CAS 

Google Scholar 
Kay, S. et al. Agroforestry creates carbon sinks whilst enhancing the environment in agricultural landscapes in Europe. Land Use Policy 83, 581–593 (2019).Article 

Google Scholar 
Cardinael, R. et al. Impact of alley cropping agroforestry on stocks, forms and spatial distribution of soil organic carbon — a case study in a Mediterranean context. Geoderma 259–260, 288–299 (2015).Article 

Google Scholar 
Cardinael, R. et al. Spatial variation of earthworm communities and soil organic carbon in temperate agroforestry. Biol. Fertil. Soils 55, 171–183 (2019).Article 
CAS 

Google Scholar 
Boinot, S. et al. Alley cropping agroforestry systems: reservoirs for weeds or refugia for plant diversity? Agric. Ecosyst. Environ. 284, 106584 (2019).Article 

Google Scholar 
Barnes, A. D. et al. Direct and cascading impacts of tropical land-use change on multi-trophic biodiversity. Nat. Ecol. Evol. 1, 1511–1519 (2017).Article 

Google Scholar 
Kehoe, L. et al. Biodiversity at risk under future cropland expansion and intensification. Nat. Ecol. Evol. 1, 1129–1135 (2017).Article 

Google Scholar 
DuPont, S. T., Culman, S. W., Ferris, H., Buckley, D. H. & Glover, J. D. No-tillage conversion of harvested perennial grassland to annual cropland reduces root biomass, decreases active carbon stocks, and impacts soil biota. Agric. Ecosyst. Environ. 137, 25–32 (2010).Article 
CAS 

Google Scholar 
Bengtsson, J. et al. Grasslands-more important for ecosystem services than you might think. Ecosphere 10, e02582 (2019).Article 

Google Scholar 
Beule, L. et al. Conversion of monoculture cropland and open grassland to agroforestry alters the abundance of soil bacteria, fungi and soil-N-cycling genes. PLoS ONE 14, e0218779 (2019).Article 
CAS 

Google Scholar 
Borrelli, P., Ballabio, C., Panagos, P. & Montanarella, L. Wind erosion susceptibility of European soils. Geoderma 232–234, 471–478 (2014).Article 

Google Scholar 
Amundson, R. et al. Soil and human security in the 21st century. Science 348, 12610711–12610716 (2015).Article 

Google Scholar 
Olson, K. R., Al-Kaisi, M., Lal, R. & Cihacek, L. Impact of soil erosion on soil organic carbon stocks. J. Soil Water Conserv. 71, 61A–67A (2016).Article 

Google Scholar 
Larney, F. J., Bullock, M. S., Janzen, H. H., Ellert, B. H. & Olson, E. C. S. Wind erosion effects on nutrient redistribution and soil productivity. J. Soil Water Conserv. 53, 133–140 (1998).
Google Scholar 
de Jong, E. & Kowalchuk, T. E. The effect of shelterbelts on erosion and soil properties. Soil Sci. 159, 337–345 (1995).Article 

Google Scholar 
Deutsch, M. & Otter, V. Nachhaltigkeit und förderung? Akzeptanzfaktoren im Entscheidungsprozess deutscher Landwirte zur Anlage von Agroforstsystemen. Berichte über Landwirtschaft – Zeitschrift für Agrarpolitik und Landwirtschaft Aktuelle Beiträge (2021).Tsonkova, P., Böhm, C., Quinkenstein, A. & Freese, D. Ecological benefits provided by alley cropping systems for production of woody biomass in the temperate region: a review. Agroforest Syst. 85, 133–152 (2012).Article 

Google Scholar 
Lehmann, J., Weigl, D., Droppelmann, K., Huwe, B. & Zech, W. Nutrient cycling in an agroforestry system with runoff irrigation in Northern Kenya. Agroforestry Syst. 43, 49–70 (1998).Article 

Google Scholar 
Shao, G. et al. Impacts of monoculture cropland to alley cropping agroforestry conversion on soil N2O emissions. GCB Bioenergy https://doi.org/10.1111/gcbb.13007 (2022).Isaac, M. E. & Borden, K. A. Nutrient acquisition strategies in agroforestry systems. Plant Soil 444, 1–19 (2019).Article 
CAS 

Google Scholar 
Cannell, M. G. R., van Noordwijk, M. & Ong, C. K. The central agroforestry hypothesis: the trees must acquire resources that the crop would not otherwise acquire. Agroforestry Syst. 34, 27–31 (1996).Article 

Google Scholar 
Beule, L., Vaupel, A. & Moran-Rodas, V. E. Abundance, diversity, and function of soil microorganisms in temperate alley-cropping agroforestry systems: a review. Microorganisms 10, 616 (2022).Article 
CAS 

Google Scholar 
Thevathasan, N. V. & Gordon, A. M. in New Vistas in Agroforestry, Vol. 1 (eds Nair, P. K. R., Rao, M. R. & Buck, L. E.) 257–268 (Springer Netherlands, 2004).Veldkamp, E. & Keller, M. Fertilizer-induced nitric oxide emissions from agricultural soils. Nutr. Cycling Agroecosyst. 48, 69–77 (1997).Article 
CAS 

Google Scholar 
Luo, J., Beule, L., Shao, G., Veldkamp, E. & Corre, M. D. Reduced soil gross N2O emission driven by substrates rather than denitrification gene abundance in cropland agroforestry and monoculture. JGR Biogeosciences 127, e2021JG006629 (2022).Article 
CAS 

Google Scholar 
Langenberg, J., Feldmann, M. & Theuvsen, L. Alley cropping agroforestry systems: using Monte-Carlo simulation for a risk analysis in comparison with arable farming systems. German J. Agric. Econ. 67, 95–112 (2018).
Google Scholar 
Otter, V. & Langenberg, J. Willingness to pay for environmental effects of agroforestry systems: a PLS-model of the contingent evaluation from German taxpayers’ perspective. Agroforest Syst. 94, 811–829 (2020).Article 

Google Scholar 
Zhang, X. et al. Quantification of global and national nitrogen budgets for crop production. Nat. Food 2, 529–540 (2021).Article 

Google Scholar 
Markwitz, C., Knohl, A. & Siebicke, L. Evapotranspiration over agroforestry sites in Germany. Biogeosciences 17, 5183–5208 (2020).Article 

Google Scholar 
Pardon, P. et al. Trees increase soil organic carbon and nutrient availability in temperate agroforestry systems. Agric. Ecosyst. Environ. 247, 98–111 (2017).Article 
CAS 

Google Scholar 
European Commission. Commission regulation (EC) No 1120/2009 (EC, 2009).Piñeiro, V. et al. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat. Sustain. 3, 809–820 (2020).Article 

Google Scholar 
Kay, S. et al. Agroforestry is paying off – economic evaluation of ecosystem services in European landscapes with and without agroforestry systems. Ecosyst. Serv. 36, 100896 (2019).Article 

Google Scholar 
European Council. Council agrees its position on the next EU common agricultural policy. Press release. https://www.consilium.europa.eu/en/press/press-releases/2020/10/21/council-agrees-its-position-on-the-next-eu-common-agricultural-policy/ (2020).IUSS Working Group WRB. World Reference Base for Soil Resources 2014. International Soil Classification System for Naming Soils and Creating Legends for Soil Maps (FAO, 2014).Garland, G. et al. A closer look at the functions behind ecosystem multifunctionality: a review. J. Ecol. 109, 600–613 (2021).Article 

Google Scholar 
Naumann, C. & Bassler, R. Die Chemische Untersuchung von Futtermitteln 3. Auflage (Chemical Analysis of Feedstuff 3rd Edition) (VDLUFA-Verlag, 1976).Beule, L., Lehtsaar, E., Rathgeb, A. & Karlovsky, P. Crop diseases and mycotoxin accumulation in temperate agroforestry systems. Sustainability 11, 2925 (2019).Article 
CAS 

Google Scholar 
Verwijst, T. & Telenius, B. Biomass estimation procedures in short rotation forestry. For. Ecol. Manag. 121, 137–146 (1999).Article 

Google Scholar 
Harris, D., Horwáth, W. R. & van Kessel, C. Acid fumigation of soils to remove carbonates prior to total organic carbon or carbon-13 isotopic analysis. Soil Sci. Soc. Am. J. 65, 1853–1856 (2001).Article 
CAS 

Google Scholar 
Blake, G. & Hartge, K. in Methods of Soil Analysis: Part 1 – Physical and Mineralogical Methods 363–375 (Americal Society of Agronomy, Inc., 1995).Davidson, E. A., Hart, S. C., Shanks, C. A. & Firestone, M. K. Measuring gross nitrogen mineralization, and nitrification by 15N isotopic pool dilution in intact soil cores. J. Soil Sci. 42, 335–349 (1991).Article 
CAS 

Google Scholar 
Tiessen, H. & Moir, J. O. in Soil Sampling and Methods of Analysis Ch. 25 (CRC Press, 1993).Beule, L. et al. Poplar rows in temperate agroforestry croplands promote bacteria, fungi, and denitrification genes in soils. Front. Microbiol. 10, 3108 (2020).Article 

Google Scholar 
Ando, S. et al. Detection of nifH sequences in sugarcane (Saccharum officinarum L.) and pineapple (Ananas comosus [L.] Merr.). Soil Sci. Plant Nutr. 51, 303–308 (2005).Article 
CAS 

Google Scholar 
Singh, J., Singh, S. & Vig, A. P. Extraction of earthworm from soil by different sampling methods: a review. Environ. Dev. Sustain. 18, 1521–1539 (2016).Article 

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).Article 
CAS 

Google Scholar 
Shen, S. M., Pruden, G. & Jenkinson, D. S. Mineralization and immobilization of nitrogen in fumigated soil and the measurement of microbial biomass nitrogen. Soil Biol. Biochem. 16, 437–444 (1984).Article 
CAS 

Google Scholar 
Marx, M.-C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).Article 
CAS 

Google Scholar 
Matson, A. L., Corre, M. D., Langs, K. & Veldkamp, E. Soil trace gas fluxes along orthogonal precipitation and soil fertility gradients in tropical lowland forests of Panama. Biogeosciences 14, 3509–3524 (2017).Article 
CAS 

Google Scholar 
Wen, Y., Corre, M. D., Schrell, W. & Veldkamp, E. Gross N2O emission and gross N2O uptake in soils under temperate spruce and beech forests. Soil Biol. Biochem. 112, 228–236 (2017).Article 
CAS 

Google Scholar 
McKenzie, N. J., Green, T. W. & Jacquier, D. W. in Soil Physical Measurement and Interpretation for Land Evaluation 150–162 (Csiro Publishing, 2002).Priesack, E. Expert-N model library documentation. https://expert-n.uni-hohenheim.de/en/documentation (2005).Formaglio, G., Veldkamp, E., Duan, X., Tjoa, A. & Corre, M. D. Herbicide weed control increases nutrient leaching compared to mechanical weeding in a large-scale oil palm plantation. Biogeosciences 17, 5243–5262 (2020).Article 
CAS 

Google Scholar 
Kroetsch, D. & Wang, C. in Soil sampling and methods of analysis (eds Angers, D. A. & Larney, F. J.) 713–725 (CRC Press, 2008).Kurniawan, S. et al. Conversion of tropical forests to smallholder rubber and oil palm plantations impacts nutrient leaching losses and nutrient retention efficiency in highly weathered soils. Biogeosciences 15, 5131–5154 (2018).Article 
CAS 

Google Scholar 
Markwitz, C. Micrometeorological Measurements and Numerical Simulations of Turbulence and Evapotranspiration over Agroforestry (University of Göttingen, 2021).Jarrah, M., Mayel, S., Tatarko, J., Funk, R. & Kuka, K. A review of wind erosion models: data requirements, processes, and validity. Catena 187, 104388 (2020).Article 

Google Scholar 
van Ramshorst, J. G. V. et al. Reducing wind erosion through agroforestry: a case study using large eddy simulations. Sustainability 14, 13372 (2022).Article 

Google Scholar 
Kanzler, M., Böhm, C., Mirck, J., Schmitt, D. & Veste, M. Microclimate effects on evaporation and winter wheat (Triticum aestivum L.) yield within a temperate agroforestry system. Agroforest Syst. 93, 1821–1841 (2019).Article 

Google Scholar 
Clough, Y. et al. Land-use choices follow profitability at the expense of ecological functions in Indonesian smallholder landscapes. Nat. Commun. 7, 13137 (2016).Article 
CAS 

Google Scholar  More

Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Science 312, 1477–1478 (2006).Article 
CAS 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 

Google Scholar 
Audzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. Nat. Ecol. Evol. 4, 809–814 (2020).Article 

Google Scholar 
Gardner, J. L., Heinsohn, R. & Joseph, L. Shifting latitudinal clines in avian body size correlate with global warming in Australian passerines. Proc. R. Soc. B 276, 3845–3852 (2009).Article 

Google Scholar 
Bergmann C. Über die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse (Göttinger Studien, 1847).Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Darimont, C. T. et al. Human predators outpace other agents of trait change in the wild. Proc. Natl Acad. Sci. USA 106, 952–954 (2009).Article 
CAS 

Google Scholar 
van Gils, J. A. et al. Body shrinkage due to Arctic warming reduces red knot fitness in tropical wintering range. Science 352, 819–821 (2016).Article 

Google Scholar 
Ryding, S., Klaassen, M., Tattersall, G. J., Gardner, J. L. & Symonds, M. R. E. Shape-shifting: changing animal morphologies as a response to climatic warming. Trends Ecol. Evol. 36, 1036–1048 (2021).Article 

Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).Article 

Google Scholar 
Enquist, B. J. et al. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. Adv. Ecol. Res 52, 249–318 (2015).Article 

Google Scholar 
González-Suárez, M. & Revilla, E. Variability in life-history and ecological traits is a buffer against extinction in mammals. Ecol. Lett. 16, 242–251 (2013).Article 

Google Scholar 
Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).Article 

Google Scholar 
Brady, S. P. et al. Causes of maladaptation. Evol. Appl. 12, 1229–1242 (2019).Article 

Google Scholar 
Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche contractions in declining species: mechanisms and consequences. Trends Ecol. Evol. 32, 346–355 (2017).Article 

Google Scholar 
Campbell-Staton, S. C. et al. Ivory poaching and the rapid evolution of tusklessness in African elephants. Science 374, 483–487 (2021).Article 
CAS 

Google Scholar 
Thompson M. J., Capilla-Lasheras P., Dominoni D. M., Réale D. & Charmantier A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).Starrfelt, J. & Kokko, H. Bet-hedging—a triple trade-off between means, variances and correlations. Biol. Rev. 87, 742–755 (2012).Article 

Google Scholar 
Heino, M., Díaz Pauli, B. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).Article 

Google Scholar 
Kindsvater, H. K. & Palkovacs, E. P. Predicting eco-evolutionary impacts of fishing on body size and trophic role of Atlantic cod. Copeia 105, 475–482 (2017).Article 

Google Scholar 
Hantak, M. M., McLean, B. S., Li, D. & Guralnick, R. P. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun. Biol. 4, 972 (2021).Article 

Google Scholar 
Freckleton, R. P., Harvey, P. H. & Pagel, M. Bergmann’s rule and body size in mammals. Am. Nat. 161, 821–825 (2003).Article 

Google Scholar 
Riddell, E. A., Odom, J. P., Damm, J. D. & Sears, M. W. Plasticity reveals hidden resistance to extinction under climate change in the global hotspot of salamander diversity. Sci. Adv. 4, eaar5471 (2018).Article 

Google Scholar 
Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).Article 

Google Scholar 
Yang, J. et al. Large underestimation of intraspecific trait variation and its improvements. Front. Plant Sci. 11, 53 (2020).Article 

Google Scholar 
Olsen, E. M. et al. Maturation trends indicative of rapid evolution preceded the collapse of northern cod. Nature 428, 932–935 (2004).Article 
CAS 

Google Scholar 
Antonson, N. D., Rubenstein, D. R., Hauber, M. E. & Botero, C. A. Ecological uncertainty favours the diversification of host use in avian brood parasites. Nat. Commun. 11, 4185 (2020).Article 

Google Scholar 
Rode, K. D., Amstrup, S. C. & Regehr, E. V. Reduced body size and cub recruitment in polar bears associated with sea ice decline. Ecol. Appl. 20, 768–782 (2010).Article 

Google Scholar 
Edeline, E. et al. Harvest-induced disruptive selection increases variance in fitness-related traits. Proc. R. Soc. B 276, 4163–4171 (2009).Article 

Google Scholar 
Hays, G. C. et al. Changes in mean body size in an expanding population of a threatened species. Proc. R Soc. B https://doi.org/10.1098/rspb.2022.0696 (2022).Halfwerk, W. et al. Adaptive changes in sexual signalling in response to urbanization. Nat. Ecol. Evol. 3, 374–380 (2019).Article 

Google Scholar 
Fernández-Chacón, A. et al. Protected areas buffer against harvest selection and rebuild phenotypic complexity. Ecol. Appl. 30, e02108 (2020).Article 

Google Scholar 
Sánchez-Tójar, A., Moran, N. P., O’Dea, R. E., Reinhold, K. & Nakagawa, S. Illustrating the importance of meta-analysing variances alongside means in ecology and evolution. J. Evol. Biol. 33, 1216–1223 (2020).Article 

Google Scholar 
Reed, T. E., Waples, R. S., Schindler, D. E., Hard, J. J. & Kinnison, M. T. Phenotypic plasticity and population viability: the importance of environmental predictability. Proc. R. Soc. B 277, 3391–3400 (2010).Article 

Google Scholar 
Klump, B. C. et al. Innovation and geographic spread of a complex foraging culture in an urban parrot. Science 373, 456–460 (2021).Article 
CAS 

Google Scholar 
Bosse, M. et al. Recent natural selection causes adaptive evolution of an avian polygenic trait. Science 358, 365–368 (2017).Article 
CAS 

Google Scholar 
Singer, M. C. & Parmesan, C. Lethal trap created by adaptive evolutionary response to an exotic resource. Nature 557, 238–241 (2018).Article 
CAS 

Google Scholar 
Usui, R., Sheeran, L. K., Asbury, A. M. & Blackson, M. Impacts of the COVID-19 pandemic on mammals at tourism destinations: a systematic review. Mamm. Rev. 51, 492–507 (2021).Article 

Google Scholar 
Meineke, E. K. & Daru, B. H. Bias assessments to expand research harnessing biological collections. Trends Ecol. Evol. 36, 1071–1082 (2021).Article 

Google Scholar 
The IUCN Red List of Threatened Species. Version 2021-2 (IUCN, accessed November 2021); https://www.iucnredlist.orgBoyd, R. J. et al. ROBITT: a tool for assessing the risk-of-bias in studies of temporal trends in ecology. Methods Ecol. Evol. 13, 1497–1507 (2022).Article 

Google Scholar 
Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: a review. Glob. Change Biol. 20, 3313–3328 (2014).Article 

Google Scholar 
Botero, C. A., Weissing, F. J., Wright, J. & Rubenstein, D. R. Evolutionary tipping points in the capacity to adapt to environmental change. Proc. Natl Acad. Sci. USA 112, 184–189 (2015).Article 
CAS 

Google Scholar 
Niklas, K. J. The scaling of plant and animal body mass, length, and diameter. Evolution 48, 44–54 (1994).Article 
CAS 

Google Scholar 
Van Valen, L. Morphological variation and width of ecological niche. Am. Nat. 99, 377–390 (1965).Article 

Google Scholar 
Gaillard, J. M. et al. Generation time: a reliable metric to measure life-history variation among mammalian populations. Am. Nat. 166, 119–123 (2005).Article 

Google Scholar 
Postma, E. in Quantitative Genetics in the Wild (eds Charmantier, A. et al.) 16–33 (Oxford Univ. Press, 2014).Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Bates D., Mächler M., Bolker B. & Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Ives, A. R., Dinnage, R., Nell, L. A., Helmus, M. & Li, D. phyr: Model based phylogenetic analysis. R package version 1.1.0 https://CRAN.R-project.org/package=phyr (2020).Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).Article 
CAS 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
CAS 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Article 

Google Scholar 
Hurlbert, S. H. & Lombardi, C. M. Final collapse of the Neyman–Pearson decision theoretic framework and rise of the neoFisherian. Ann. Zool. Fenn. 46, 311–349 (2009).Article 

Google Scholar  More

Akçakaya, H. R. et al. Quantifying species recovery and conservation success to develop an IUCN Green List of Species. Conserv. Biol. 32, 1128–1138. https://doi.org/10.1111/cobi.13112 (2018).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-3 (2022).Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341. https://doi.org/10.1016/j.tree.2018.12.012 (2019).Article 

Google Scholar 
Mohan, M. et al. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forestshttps://doi.org/10.3390/f8090340 (2017).Article 

Google Scholar 
Dronova, I., Kislik, C., Dinh, Z. & Kelly, M. A review of unoccupied aerial vehicle use in wetland applications: Emerging opportunities in approach, technology, and data. Droneshttps://doi.org/10.3390/drones5020045 (2021).Article 

Google Scholar 
Farinha, A. & Lima, P. U. A novel underactuated hand suitable for human-oriented domestic environments. In: Proceedings – 2016 International Conference on Autonomous Robot Systems and Competitions, ICARSC 2016 106–111, https://doi.org/10.1109/ICARSC.2016.21 (2016).Hamaza, S., Georgilas, I., Heredia, G., Ollero, A. & Richardson, T. Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment. J. Field Robotics 37, 1224–1245. https://doi.org/10.1002/rob.21963 (2020).Article 

Google Scholar 
Nakamura, A. et al. Forests and their canopies: Achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451. https://doi.org/10.1016/j.tree.2017.02.020 (2017).Article 

Google Scholar 
Hang, K. et al. Perching and resting – A paradigm for UAV maneuvering with modularized landing gears. Sci. Roboticshttps://doi.org/10.1126/scirobotics.aau6637 (2019).Article 

Google Scholar 
Danko, T. W., Kellas, A. & Oh, P. Y. Robotic rotorcraft and perch-and-stare: Sensing landing zones and handling obscurants. In ICAR ’05. Proceedings., 12th International Conference on Advanced Robotics, 2005 296–302, https://doi.org/10.1109/ICAR.2005.1507427 (2005).Pauli, J. N., Zachariah Peery, M., Fountain, E. D. & Karasov, W. H. Arboreal folivores limit their energetic output, all the way to slothfulness. Am. Nat. 188, 196–204, https://doi.org/10.1086/687032 (2016).Olson, R. A., Glenn, Z. D., Cliffe, R. N. & Butcher, M. T. Architectural properties of sloth forelimb muscles (Pilosa: Bradypodidae). J. Mamm. Evol. 25, 573–588. https://doi.org/10.1007/s10914-017-9411-z (2018).Article 

Google Scholar 
Kovač, M., Germann, J., Hürzeler, C., Siegwart, R. Y. & Floreano, D. A perching mechanism for micro aerial vehicles. J. Micro-Nano Mechatron. 5, 77–91. https://doi.org/10.1007/s12213-010-0026-1 (2009).Article 

Google Scholar 
Toon, J. ’SlothBot in the Garden’ Demonstrates Hyper-Efficient Conservation Robot.Thomas, J. et al. Aggressive flight with quadrotors for perching on inclined surfaces. J. Mech. Robot. 8, 51007. https://doi.org/10.1115/1.4032250 (2016).Article 

Google Scholar 
Daler, L., Klaptocz, A., Briod, A., Sitti, M. & Floreano, D. A perching mechanism for flying robots using a fibre-based adhesive. In 2013 IEEE International Conference on Robotics and Automation, 4433–4438 (IEEE, 2013).Kovač, M., Germann, J., Hürzeler, C., Siegwart, R. Y. & Floreano, D. A perching mechanism for micro aerial vehicles. J. Micro-Nano Mechatron. 5, 77–91 (2009).Article 

Google Scholar 
Pope, M. T. et al. A multimodal robot for perching and climbing on vertical outdoor surfaces. IEEE Trans. Rob. 33, 38–48. https://doi.org/10.1109/TRO.2016.2623346 (2017).Article 

Google Scholar 
Lussier Desbiens, A., Asbeck, A. T. & Cutkosky, M. R. Landing, perching and taking off from vertical surfaces. Int. J. Robotics Res. 30, 355–370 (2011).Article 

Google Scholar 
Nguyen, H.-N., Siddall, R., Stephens, B., Navarro-Rubio, A. & Kovač, M. A Passively adaptive microspine grapple for robust, controllable perching. In 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), 80–87 (IEEE, 2019).Braithwaite, A., Al Hinai, T., Haas-Heger, M., McFarlane, E. & Kovač, M. Tensile web construction and perching with nano aerial vehicles. In Robotics Research (eds Bicchi, A. & Burgard, W.) (Springer, Cham, 2018).
Google Scholar 
Zhang, K., Chermprayong, P., Alhinai, T. M., Siddall, R. & Kovac, M. SpiderMAV: Perching and stabilizing micro aerial vehicles with bio-inspired tensile anchoring systems. In International Conference on Intelligent Robots and Systems (2017).Roderick, W. R. T., Jiang, H., Wang, S., Lentink, D. & Cutkosky, M. R. Bioinspired grippers for natural curved surface perching. In Conference on Biomimetic and Biohybrid Systems, 604–610 (Springer, 2017).Thomas, J., Loianno, G., Daniilidis, K. & Kumar, V. Visual servoing of quadrotors for perching by hanging from cylindrical objects. IEEE Robotics Automation Lett.https://doi.org/10.1109/LRA.2015.2506001 (2016).Article 

Google Scholar 
McLaren, A., Fitzgerald, Z., Gao, G. & Liarokapis, M. A passive closing, tendon driven, adaptive robot hand for ultra-fast, aerial grasping and perching. In IEEE International Conference on Intelligent Robots and Systems 5602–5607, https://doi.org/10.1109/IROS40897.2019.8968076 (2019).Zhang, Z., Xie, P. & Ma, O. Bio-inspired trajectory generation for UAV perching. In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 997–1002 (IEEE, 2013).Doyle, C. E. et al. An avian-inspired passive mechanism for quadrotor perching. IEEE/ASME Trans. Mechatron. 18, 506–517. https://doi.org/10.1109/TMECH.2012.2211081 (2013).Article 

Google Scholar 
Erbil, M. A., Prior, S. D. & Keane, A. J. Design optimisation of a reconfigurable perching element for vertical take-off and landing unmanned aerial vehicles. Int. J. Micro Air Veh. 5, 207–228 (2013).Article 

Google Scholar 
Chi, W., Low, K. H., Hoon, K. H. & Tang, J. An optimized perching mechanism for autonomous perching with a quadrotor. In IEEE International Conference on Robotics and Automation, 3109–3115, (2014). https://doi.org/10.1109/ICRA.2014.6907306Roderick, W. R. T., Cutkosky, M. R. & Lentink, D. Bird-inspired dynamic grasping and perching in arboreal environments. Sci. Roboticshttps://doi.org/10.1126/scirobotics.abj7562 (2021).Article 

Google Scholar 
Garcia-Rubiales, F. J., Ramon-Soria, P., Arrue, B. C., Ollero, A. Magnetic & detaching system for Modular UAVs with perching capabilities in industrial environments.,. International Workshop on Research. Education and Development on Unmanned Aerial Systems, RED-UAS2019(172–176), 2019. https://doi.org/10.1109/REDUAS47371.2019.8999704 (2019).Bai, L. et al. Design and experiment of a deformable bird-inspired UAV perching mechanism. J. Bionic Eng. 18, 1304–1316. https://doi.org/10.1007/s42235-021-00098-5 (2021).Article 

Google Scholar 
Joachimczak, M., Suzuki, R. & Arita, T. Artificial metamorphosis: Evolutionary design of transforming, soft-bodied robots. Artif. Life 22(271–298), 2016. https://doi.org/10.1162/artl_a_00207 (2016).Article 

Google Scholar 
Sims, K. Evolving Virtual Creatures. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’94, 15-22, https://doi.org/10.1145/192161.192167 (Association for Computing Machinery, 1994).Bongard, J. Morphological change in machines accelerates the evolution of robust behavior. Proc. Natl. Acad. Sci. 108, 1234–1239. https://doi.org/10.1073/pnas.1015390108 (2011).Article 
ADS 

Google Scholar 
Truman, J. W. & Riddiford, L. M. The origins of insect metamorphosis. Nature 401, 447–452. https://doi.org/10.1038/46737 (1999).Article 
ADS 
CAS 

Google Scholar 
Campbell, N. A. et al. Biology: A Global Approach (Pearson New Your, NY, 2018).
Google Scholar 
Dai, J. S. & Rees Jones, J. Mobility in metamorphic mechanisms of foldable/erectable kinds. J. Mech. Des. 121, 375. https://doi.org/10.1115/1.2829470 (1999).Article 

Google Scholar 
Mintchev, S. & Floreano, D. Adaptive morphology: A design principle for multimodal and multifunctional robots. IEEE Robot. Autom. Mag. 23, 42–54 (2016).Article 

Google Scholar 
Shah, D. et al. Shape changing robots: Bioinspiration, simulation, and physical realization. Adv. Mater. 33, 2002882 (2021).Article 
CAS 

Google Scholar 
Sareh, S., Siddall, R., Alhinai, T. & Kovac, M. Bio-inspired soft aerial robots: Adaptive morphology for high-performance flight. In Soft Robotics: Trends, Applications and Challenges, 65–74 (Springer, 2017).Derrouaoui, S. H., Bouzid, Y., Guiatni, M. & Dib, I. A comprehensive review on reconfigurable drones: classification characteristics design and control technologies. Unmanned Syst. 10(01), 3–29. https://doi.org/10.1142/S2301385022300013 (2022).Floreano, D. & Wood, R. J. Science, technology and the future of small autonomous drones. Nature 521, 460–466 (2015).Article 
ADS 
CAS 

Google Scholar 
Hwang, D., Barron, E. J., Haque, A. B. & Bartlett, M. D. Shape morphing mechanical metamaterials through reversible plasticity. Sci. Robotics 7, eabg2171. https://doi.org/10.1126/scirobotics.abg2171 (2022).Article 

Google Scholar 
Siddall, R., Ortega Ancel, A. & Kovač, M. Wind and water tunnel testing of a morphing aquatic micro air vehicle. Interface focus 7, 20160085. https://doi.org/10.1098/rsfs.2016.0085 (2017).Article 

Google Scholar 
Chen, Y. et al. A biologically inspired, flapping-wing, hybrid aerial-aquatic microrobot. Sci. Roboticshttps://doi.org/10.1126/scirobotics.aao5619 (2017).Article 

Google Scholar 
Daler, L., Mintchev, S., Stefanini, C. & Floreano, D. A bioinspired multi-modal flying and walking robot. Bioinspiration Biomim.https://doi.org/10.1088/1748-3190/10/1/016005 (2015).Article 

Google Scholar 
Kovač, M., Wassim-Hraiz, Fauria, O., Zufferey, J. C. & Floreano, D. The EPFL jumpglider: A hybrid jumping and gliding robot with rigid or folding wings. In 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 1503–1508, https://doi.org/10.1109/ROBIO.2011.6181502 (2011).Riviere, V., Manecy, A. & Viollet, S. Agile robotic fliers: A morphing-based approach. Soft Roboticshttps://doi.org/10.1089/soro.2017.0120 (2018).Article 

Google Scholar 
Bucki, N. & Mueller, M. W. Design and control of a passively morphing quadcopter. In IEEE International Conference on Robotics and Automation, vol. 2019-May, 9116–9122, https://doi.org/10.1109/ICRA.2019.8794373 (2019).Mintchev, S., Daler, L., Eplattenier, G. L., Floreano, D. & Member, S. Foldable and self – deployable pocket sized quadrotor. In Proc. of the IEEE Conference on Robotics and Automation 2190–2195 (2015).Mintchev, S., Shintake, J. & Floreano, D. Bioinspired dual-stiffness origami. Sci. Robotics 0275, 1–8. https://doi.org/10.1126/scirobotics.aau0275 (2018).Article 

Google Scholar 
Zhao, M., Kawasaki, K., Anzai, T., Chen, X. & Noda, S. Transformable multirotor with two-dimensional multilinks : Modeling, control, and whole-body aerial manipulation. Int. J. Robot. Res.https://doi.org/10.1177/0278364918801639 (2018).Article 

Google Scholar 
Bucki, N., Tang, J. & Mueller, M. W. Design and control of a midair-reconfigurable quadcopter using unactuated hinges. IEEE Trans. Rob.https://doi.org/10.1109/TRO.2022.3193792 (2022).Article 

Google Scholar 
Shimoyama, I., Miura, H., Suzuki, K. & Ezura, Y. Insect-like microrobots with external skeletons. IEEE Control Syst. Mag. 13, 37–41. https://doi.org/10.1109/37.184791 (1993).Article 

Google Scholar 
Noh, M., Kim, S.-W., An, S., Koh, J.-S. & Cho, K.-J. Flea-inspired catapult mechanism for miniature jumping robots. IEEE Trans. Rob. 28, 1007–1018. https://doi.org/10.1109/tro.2012.2198510 (2012).Article 
ADS 

Google Scholar 
Miyashita, S., Guitron, S., Ludersdorfer, M., Sung, C. R. & Rus, D. An untethered miniature origami robot that self-folds, walks, swims, and degrades. In Proceedings – IEEE International Conference on Robotics and Automation 2015-June, 1490–1496, https://doi.org/10.1109/ICRA.2015.7139386 (2015).Morgan, J., Magleby, S. P. & Howell, L. L. An approach to designing origami-adapted aerospace mechanisms. J. Mech. Des.https://doi.org/10.1115/1.4032973 (2016).Article 

Google Scholar 
Liang, X. et al. The AmphiHex: A novel amphibious robot with transformable leg-flipper composite propulsion mechanism. In IEEE International Conference on Intelligent Robots and Systems 3667–3672, https://doi.org/10.1109/IROS.2012.6386238 (2012).Polygerinos, P. et al. Soft robotics: Review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Adv. Eng. Mater.https://doi.org/10.1002/adem.201700016 (2017).Article 

Google Scholar 
Coyle, S., Majidi, C., LeDuc, P. & Hsia, K. J. Bio-inspired soft robotics: Material selection, actuation, and design. Extreme Mech. Lett. 22, 51–59. https://doi.org/10.1016/j.eml.2018.05.003 (2018).Article 

Google Scholar 
Rus, D. & Tolley, M. T. Design, fabrication and control of soft robots. Nature 521, 467–475. https://doi.org/10.1038/nature14543 (2015).Article 
ADS 
CAS 

Google Scholar 
Laschi, C., Mazzolai, B. & Cianchetti, M. Soft robotics: Technologies and systems pushing the boundaries of robot abilities. Sci. Robotics 1, eaah3690. https://doi.org/10.1126/scirobotics.aah3690 (2016).Article 

Google Scholar 
Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics. High Throughput 7, 1–21. https://doi.org/10.3390/act7030048 (2018).Article 

Google Scholar 
Miriyev, A., Stack, K. & Lipson, H. Soft material for soft actuators. Nat. Commun. 8, 1–8. https://doi.org/10.1038/s41467-017-00685-3 (2017).Article 
CAS 

Google Scholar 
Nguyen, P. H. & Kovač, M. Adopting physical artificial intelligence in soft aerial robots. IOP Conf. Ser.: Mater. Sci. Eng. 1261, 012006. https://doi.org/10.1088/1757-899X/1261/1/012006 (2022).Article 

Google Scholar 
Kim, S.-J., Lee, D.-Y., Jung, G.-P. & Cho, K.-J. An origami-inspired, self-locking robotic arm that can be folded flat. Sci. Robotics 3, eaar2915. https://doi.org/10.1126/scirobotics.aar2915 (2018).Article 

Google Scholar 
Ruiz, F., Arrue, B. C. & Ollero, A. SOPHIE: Soft and flexible aerial vehicle for physical interaction with the environment. IEEE Robotics Automation Lett. 7, 11086–11093. https://doi.org/10.1109/LRA.2022.3196768 (2022).Article 

Google Scholar 
Doshi, N. et al. Model driven design for flexure-based microrobots. In IEEE International Conference on Intelligent Robots and Systems 2015-Decem, 4119–4126, https://doi.org/10.1109/IROS.2015.7353959 (2015).Koh, J.-S., Doshi, N., Wood, R. J., Temel, F. Z. & McClintock, H. The milliDelta: A high-bandwidth, high-precision, millimeter-scale Delta robot. Sci. Robotics 3, eaar3018. https://doi.org/10.1126/scirobotics.aar3018 (2018).Article 

Google Scholar 
Backus, S. B., Sustaita, D., Odhner, L. U. & Dollar, A. M. Mechanical analysis of avian feet: Multiarticular muscles in grasping and perching. R. Soc. Open Sci.https://doi.org/10.1098/rsos.140350 (2015).Article 

Google Scholar 
Paine, C. E. T. et al. Functional explanations for variation in bark thickness in tropical rain forest trees. Funct. Ecol. 24, 1202–1210. https://doi.org/10.1111/j.1365-2435.2010.01736.x (2010).Article 

Google Scholar 
Miriyev, A. & Kovač, M. Skills for physical artificial intelligence. Nat. Mach. Intell. 2, 658–660. https://doi.org/10.1038/s42256-020-00258-y (2020).Article 

Google Scholar 
Felton, S., Tolley, M., Demaine, E., Rus, D. & Wood, R. A method for building self-folding machines. Science 345, 644–646. https://doi.org/10.1126/science.1252610 (2014).Article 
ADS 
CAS 

Google Scholar 
Siddall, R., Byrnes, G., Full, R. J. & Jusufi, A. Tails stabilize landing of gliding geckos crashing head-first into tree trunks. Commun. Biol. 4, 1–12. https://doi.org/10.1038/s42003-021-02378-6 (2021).Article 
CAS 

Google Scholar 
Feduccia, A. Evidence from claw geometry indicating arboreal habits of Archaeopteryx. Science 259, 790–793. https://doi.org/10.1126/science.259.5096.790 (1993).Article 
ADS 
CAS 

Google Scholar  More

Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113(48), 13785–13790. https://doi.org/10.1073/pnas.1606102113 (2016).Article 
ADS 
CAS 

Google Scholar 
Wernberg, T. et al. Biology and ecology of the globally significant kelp Ecklonia radiata. Oceanogr. Mar. Biol. https://doi.org/10.1201/9780429026379-6 (2019).Article 

Google Scholar 
Bennett, S. et al. The ‘Great Southern Reef’: Social, ecological and economic value of Australia’s neglected kelp forests. Mar. Freshw. Res. 67(1), 47–56. https://doi.org/10.1071/MF15232 (2015).Article 

Google Scholar 
Eger, A. et al. The economic value of fisheries, blue carbon, and nutrient cycling in global marine forests. EcoEvoRxiv. https://doi.org/10.32942/osf.io/n7kjs (2021).Article 

Google Scholar 
Smith, K. E. et al. Socioeconomic impacts of marine heatwaves: Global issues and opportunities. Science 374, 6566. https://doi.org/10.1126/science.abj3593 (2021).Article 
CAS 

Google Scholar 
Coleman, M. et al. Loss of a globally unique kelp forest and genetic diversity from the northern hemisphere. Sci. Rep. 12, 5020. https://doi.org/10.1038/s41598-022-08264-3 (2022).Article 
ADS 
CAS 

Google Scholar 
Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl. Acad. Sci. 113(48), 13791–13796. https://doi.org/10.1073/pnas.1610725113 (2016).Article 
ADS 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353(6295), 169–172. https://doi.org/10.1126/science.aad8745 (2016).Article 
ADS 
CAS 

Google Scholar 
Wood, G. et al. Genomic vulnerability of a dominant seaweed points to future-proofing pathways for Australia’s underwater forests. Glob. Change Biol. 27(10), 2200–2212. https://doi.org/10.1111/gcb.15534 (2021).Article 
ADS 
CAS 

Google Scholar 
Vranken, S. et al. Genotype-environment mismatch of kelp forests under climate change. Mol. Ecol. 30(15), 3730. https://doi.org/10.1111/mec.15993 (2021).Article 

Google Scholar 
Assis, J. et al. Deep reefs are climatic refugia for genetic diversity of marine forests. J. Biogeogr. 43(4), 833–844. https://doi.org/10.1111/jbi.12677 (2016).Article 

Google Scholar 
Lourenço, C. R. et al. Upwelling areas as climate change refugia for the distribution and genetic diversity of a marine macroalga. J. Biogeogr. 43(8), 1595–1607. https://doi.org/10.1111/jbi.12744 (2016).Article 

Google Scholar 
Graham, M. H., Kinlan, B. P., Druehl, L. D., Garske, L. E. & Banks, S. Deep-water kelp refugia as potential hotspots of tropical marine diversity and productivity. Proc. Natl. Acad. Sci. 104(42), 16576–16580. https://doi.org/10.1073/pnas.0704778104 (2007).Article 
ADS 

Google Scholar 
Marzinelli, E. M. et al. Large-scale geographic variation in distribution and abundance of Australian deep-water kelp forests. PLoS ONE 10, e0118390. https://doi.org/10.1371/journal.pone.0118390 (2015).Article 
CAS 

Google Scholar 
Coleman, M. A. et al. Variation in the strength of continental boundary currents determines continent-wide connectivity in kelp. J. Ecol. 99(4), 1026–1032. https://doi.org/10.1111/j.1365-2745.2011.01822.x (2011).Article 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8(5), 461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).Article 

Google Scholar 
Maggs, C. A. et al. Evaluating signatures of glacial refugia for North Atlantic benthic marine taxa. Ecology 89(sp11), S108–S122. https://doi.org/10.1890/08-0257.1 (2008).Article 

Google Scholar 
Grant, W. S., Lydon, A. & Bringloe, T. T. Phylogeography of split kelp Hedophyllum nigripes: Northern ice-age refugia and trans-Arctic dispersal. Polar Biol. 43, 1829–1841. https://doi.org/10.1007/s00300-020-02748-6 (2020).Article 

Google Scholar 
Hoarau, G., Coyer, J. A., Veldsink, J. H., Stam, W. T. & Olsen, J. L. Glacial refugia and recolonization pathways in the brown seaweed Fucus serratus. Mol. Ecol. 16(17), 3606–3616. https://doi.org/10.1111/j.1365-294X.2007.03408.x (2007).Article 
CAS 

Google Scholar 
Fraser, C. I., Nikula, R., Spencer, H. G. & Waters, J. M. Kelp genes reveal effects of subantarctic sea ice during the Last Glacial Maximum. Proc. Natl. Acad. Sci. 106(9), 3249–3253. https://doi.org/10.1073/pnas.0810635106 (2009).Article 
ADS 

Google Scholar 
Assis, J. et al. Past climate changes and strong oceanographic barriers structured low-latitude genetic relics for the golden kelp Laminaria ochroleuca. J. Biogeogr. 45(10), 2326–2336. https://doi.org/10.1111/jbi.13425 (2018).Article 

Google Scholar 
Gersonde, R., Crosta, X., Abelmann, A. & Armand, L. Sea-surface temperature and sea ice distribution of the Southern Ocean at the EPILOG last glacial maximum—A circum-Antarctic view based on siliceous microfossil records. Quat. Sci. Rev. 24(7–9), 869–896. https://doi.org/10.1016/j.quascirev.2004.07.015 (2005).Article 
ADS 

Google Scholar 
Bostock, H. C., Opdyke, B. N., Gagan, M. K., Kiss, A. E. & Fifield, L. K. Glacial/interglacial changes in the East Australian current. Clim. Dyn. 26, 645–659. https://doi.org/10.1007/s00382-005-0103-7 (2006).Article 

Google Scholar 
Brooke, B. P., Nichol, S. L., Huang, Z. & Beaman, R. J. Palaeoshorelines on the Australian continental shelf: Morphology, sea-level relationship and applications to environmental management and archaeology. Cont. Shelf Res. 134, 26–38. https://doi.org/10.1016/j.csr.2016.12.012 (2017).Article 
ADS 

Google Scholar 
Williams, A. N., Ulm, S., Sapienza, T., Lewis, S. & Turney, C. S. M. Sea-level change and demography during the last glacial termination and early Holocene across the Australian continent. Quat. Sci. Rev. 182, 144–154. https://doi.org/10.1016/j.quascirev.2017.11.030 (2018).Article 
ADS 

Google Scholar 
Durrant, H. M. S., Barrett, N. S., Edgar, G. J., Coleman, M. A. & Burridge, C. P. Shallow phylogeographic histories of key species in a biodiversity hotspot. Phycologia 54(6), 556–565. https://doi.org/10.2216/15-24.1 (2015).Article 

Google Scholar 
O’Hara, T. D. & Poore, G. C. B. Patterns of distribution for southern Australian marine echinoderms and decapods. J. Biogeogr. 27(6), 1321–1335. https://doi.org/10.1046/j.1365-2699.2000.00499.x (2000).Article 

Google Scholar 
Waters, J. M. Marine biogeographical disjunction in temperate Australia: Historical landbridge, contemporary currents, or both? Divers. Distrib. 14(4), 692–700. https://doi.org/10.1111/j.1472-4642.2008.00481.x (2008).Article 

Google Scholar 
Davis, T. R., Champion, C. & Coleman, M. A. Climate refugia for kelp within an ocean warming hotspot revealed by stacked species distribution modelling. Mar. Environ. Res. 166, 105267. https://doi.org/10.1016/j.marenvres.2021.105267 (2021).Article 
CAS 

Google Scholar 
Barrows, T. T. & Juggins, S. Sea-surface temperatures around the Australian margin and Indian Ocean during the last glacial maximum. Quat. Sci. Rev. 24(7–9), 1017–1047. https://doi.org/10.1016/j.quascirev.2004.07.020 (2005).Article 
ADS 

Google Scholar 
Richmond, S. & Stevens, T. Classifying benthic biotopes on sub-tropical continental shelf reefs: How useful are abiotic surrogates? Estuar. Coast. Shelf Sci. 138, 79–89. https://doi.org/10.1016/j.ecss.2013.12.012 (2014).Article 
ADS 

Google Scholar 
Jordan, A. et al. Seabed Habitat Mapping of the Continental Shelf of NSW (New South Wales Department of Environment, Climate Change and Water, 2010).
Google Scholar 
Lewis, S. E., Sloss, C. R., Murray-Wallace, C. V., Woodroffe, C. D. & Smithers, S. G. Post-glacial sea-level changes around the Australian margin: A review. Quat. Sci. Rev. 74, 115–138. https://doi.org/10.1016/j.quascirev.2012.09.006 (2013).Article 
ADS 

Google Scholar 
Millar, A. J. K. Marine benthic algae of Norfolk island, South Pacific. Aust. Syst. Bot. 12(4), 479–547. https://doi.org/10.1071/SB98004 (1999).Article 

Google Scholar 
Ridgway, K. R. & Dunn, J. R. Mesoscale structure of the mean East Australian current system and its relationship with topography. Prog. Oceanogr. 56, 189–222. https://doi.org/10.1016/S0079-6611(03)00004-1 (2003).Article 
ADS 

Google Scholar 
Lough, J. M. & Hobday, A. J. Observed climate change in Australian marine and freshwater environments. Mar. Freshw. Res. 62(9), 984–999. https://doi.org/10.1071/MF10272 (2011).Article 

Google Scholar 
Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18(9), 944–953. https://doi.org/10.1111/ele.12474 (2015).Article 

Google Scholar 
Coleman, M. A. et al. Variation in the strength of continental boundary currents determines patterns of large-scale connectivity in kelp. J. Ecol. 99, 1026–1032 (2011).Article 

Google Scholar 
Maeda, T., Kawai, T., Nakaoka, M. & Yotsukura, N. Effective DNA extraction method for fragment analysis using capillary sequencer of the kelp, Saccharina. J. Appl. Phycol. 25, 337–347. https://doi.org/10.1007/s10811-012-9868-3 (2013).Article 
CAS 

Google Scholar 
Lane, C. E., Lindstrom, S. C. & Saunders, G. W. A molecular assessment of northeast Pacific Alaria species (Laminariales, Phaeophyceae) with reference to the utility of DNA barcoding. Mol. Phylogenet. Evol. 44(2), 634–648. https://doi.org/10.1016/j.ympev.2007.03.016 (2007).Article 
CAS 

Google Scholar 
Saunders, G. W. & McDevit, D. C. Acquiring DNA sequence data from dried archival red algae (Florideophyceae) for the purpose of applying available names to contemporary genetic species: A critical assessment. Botany 90, 191–203 (2012).Article 
CAS 

Google Scholar 
Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12), 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).Article 
CAS 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215(3), 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).Article 
CAS 

Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34(12), 3299–3302. https://doi.org/10.1093/molbev/msx248 (2017).Article 
CAS 

Google Scholar 
Clement, M., Posada, D. & Crandall, K. A. TCS: A computer program to estimate gene genealogies. Mol. Ecol. 9(10), 1657–1659. https://doi.org/10.1046/j.1365-294x.2000.01020.x (2000).Article 
CAS 

Google Scholar 
Leigh, J. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6(9), 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).Article 

Google Scholar 
Inkscape Project. Inkscape Project. https://inkscape.org/ (2020).Coleman, M. A. et al. Connectivity within and among a network of temperate marine reserves. PLoS ONE 6(5), e20168. https://doi.org/10.1371/journal.pone.0020168 (2011).Article 
ADS 
CAS 

Google Scholar 
Davis, T. R., Cadiou, G., Champion, C. & Coleman, M. A. Environmental drivers and indicators of change in habitat and fish assemblages within a climate change hotspot. Reg. Mar. Stud. https://doi.org/10.1016/j.rsma.2020.101295 (2020).Article 

Google Scholar 
Mix, A. C., Bard, E. & Schneider, R. Environmental processes of the ice age: Land, oceans, glaciers (EPILOG). Quat. Sci. Rev. 20(4), 627–657. https://doi.org/10.1016/S0277-3791(00)00145-1 (2001).Article 
ADS 

Google Scholar 
Waters, J. M. Competitive exclusion: Phylogeography’s ‘elephant in the room’? Mol. Ecol. 20(21), 4388–4394. https://doi.org/10.1111/j.1365-294X.2011.05286.x (2011).Article 

Google Scholar 
Cresswell, G. R., Peterson, J. L. & Pender, L. F. The East Australian current, upwellings and downwellings off eastern-most Australia in summer. Mar. Freshw. Res. 68(7), 1208–1223. https://doi.org/10.1071/MF16051 (2016).Article 

Google Scholar 
Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58(3), 247–276. https://doi.org/10.1006/bijl.1996.0035 (1995).Article 

Google Scholar 
Waters, J. M., Fraser, C. I. & Hewitt, G. M. Founder takes all: Density-dependent processes structure biodiversity. Trends Ecol. Evol. 28(2), 78–85. https://doi.org/10.1016/j.tree.2012.08.024 (2013).Article 

Google Scholar 
Wernberg, T. et al. Genetic diversity and kelp forest vulnerability to climatic stress. Sci. Rep. 8(1851), 1–8. https://doi.org/10.1038/s41598-018-20009-9 (2018).Article 
CAS 

Google Scholar 
Coleman, M. A. & Kelaher, B. P. Connectivity among fragmented populations of a habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales) on an urbanised coast. Mar. Ecol. Prog. Ser. 381, 63–70 (2009).Article 
ADS 

Google Scholar 
Drábková, L. Z. DNA extraction from herbarium specimens. In Molecular Plant Taxonomy. Methods in Molecular Biology Vol. 1115 (ed. Besse, P.) (Humana Press, 2014).
Google Scholar 
Goff, L. J. & Moon, D. A. PCR amplification of nuclear and plastid genes from algal herbarium specimens and algal spores 1. J. Phycol. 29, 381 (1993).Article 
CAS 

Google Scholar 
Nahor, O., Luzzatto-Knaan, T. & Israel, A. A new genetic lineage of Asparagopsis taxiformis (Rhodophyta) in the Mediterranean Sea: As the DNA barcoding indicates a recent Lessepsian introduction. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.873817 (2022).Article 

Google Scholar 
Coleman, M. A. & Brawley, S. H. Variability in temperature and historical patterns in reproduction in the Fucus distichus complex (Heterokontophyta; Phaeophyceae): Implications for speciation and collection of herbarium specimens. J. Phycol. 41, 1110–1119 (2005).Article 

Google Scholar 
Martins, N. et al. Hybrid vigour for thermal tolerance in hybrids between the allopatric kelps Laminaria digitata and L. pallida (Laminariales, Phaeophyceae) with contrasting thermal affinities. Eur. J. Phys. 54(4), 548–561 (2019).CAS 

Google Scholar  More

Bt rice led to the redistribution of soil nitrogenTo characterize the influence of Bt rice on soil environmental biochemistry, samples were first separated into two portions including soils and surface waters. Bt proteins were not detected in surface waters from all cultivars (Supplemental Table S2). However, Bt protein contents for rhizospheres from all three cultivars and bulk soils ranged between 64.14 and 126.68 pg/g soil (Supplemental Table S3). Bt protein contents in samples from Bt rice grown in IRRI rice nutrient solution reached 850 pg/ml (Supplementary Table S1). We speculated that the vast majority of Bt protein released from Bt plants was bound tightly to soil particles and was thus difficult to isolate, purify, and detect. Total N, NH4+-N, NO3−-N, and NO2−-N contents in T1C-1 rhizospheres were significantly higher than in the Minghui 63 rhizospheres, although the soil pH of T1C-1 rhizospheres was also significantly lower than for Minghui 63 soils (Supplemental Table S3). Interestingly, the total N, NH4+-N, and NO3−-N contents in the Zhonghua11 rhizospheres were significantly higher than in the Minghui 63 rhizospheres, pointing to an apparent impact of genotypic differences from different conventional cultivars on soil nitrogen. No differences in organic matter and total P contents were identified among all soil samples (Supplemental Table S3). In addition, the surface waters of T1C-1 exhibited higher NO3-N contents than Minghui 63 soils, but lower pH values than Minghui 63 (Supplemental Table 2), consistent with soil results.
Bt rice altered soil microbial communities, but not surface water communitiesSoil and surface water samples were collected and analyzed to characterize metagenomic profiles associated with different cultivars. A total of 11,529,157 and 2,880,919 genes were obtained for soil and surface water samples, respectively (Supplementary Table S4). The α diversity indices, Shannon–Wiener index (H’), Simpson index (D), and Evenness (E) were significantly higher in soils than in surface waters, but significant differences were not observed for Richness (R) (Fig. 2A). Except for R, the α diversity indices E, H′, and D were significantly higher in the T1C-1 rhizosphere than in the other samples, suggesting that Bt rice increased soil microbial diversity rather than altering taxonomic compositions. Differences in α diversity indices were not observed among all of the surface water samples (Supplementary Table S5). Principal coordinates analysis (PCoA) (Fig. 2B) based on microbial taxonomic level (genera) and functional classifications (clusters of orthologous groups of proteins, COG) indicated that soil samples from different rice cultivars and bulk soils formed distinct clusters in ordination space. These distinct groupings were not observed for surface water samples, suggesting that Bt rice cultivation altered soil microbial community composition and functions, but these changes did not occur in surface waters. The rhizospheres of T1C-1, Minghui 63, and Zhonghua 11 shared substantial overlap in total genera (Supplementary Fig. S2A). In addition, 40 genera specifically inhabited T1C-1 rhizospheres (Supplementary Fig. S2B). To further identify taxa that were differential between T1C-1 and Minghui 63 soils, the 50 most abundant genera that were differentially abundant for T1C-1 or Minghui 63 were specifically analyzed using a T-test. Among these, 33 were elevated in T1C-1 soils compared with Minghui 63 soils (Supplementary Fig. S3). Thus, the strongest enrichment was observed for taxa in T1C-1 soils, which is consistent with the general increased α diversity indices for T1C-1 communities (Supplementary Table S5).Fig. 2: Comparison of soil and surface water shotgun metagenomic sequencing data.A Differences in α-diversity metrics, Shannon–Wiener index (H′), Simpson index (D), Richness (R), and Evenness (E) between soil and surface water communities. Black asterisks indicate that the α-diversity index was significantly higher in soils (***, p  More

Burt, W. H. J. Mamm. 24, 346–352 (1943).Article 

Google Scholar 
Heathcote, R. J. P. et al. Nature Ecol. Evol. https://doi.org/10.1038/s41559-022-01950-5 (2023).Article 

Google Scholar 
Moorcroft, P. R., Lewis, M. A. & Crabtree, R. L. Proc. R. Soc. Lond. B 273, 1651–1659 (2006).
Google Scholar 
Moorcroft, P. R. & Barnett, A. Ecology 89, 1112–1119 (2008).Article 

Google Scholar 
Hattori, A. & Takuro, S. J. Mar. Biol. Assoc. U.K. 93, 2265–2272 (2013).Article 

Google Scholar 
Van Moorter, B. et al. Oikos 118, 641–652 (2009).Article 

Google Scholar 
Merkle, J. A., Potts, J. R. & Fortin, D. Oikos 126, https://doi.org/10.1111/oik.03356 (2017).Bracis, C., Gurarie, E., Van Moorter, B. & Goodwin, R. A. PLoS ONE 10, e0136057 (2015).Article 

Google Scholar 
Ranc, N., Cagnacci, F. & Moorcroft, P. R. Ecol. Lett. 25, 716–728 (2022).Article 

Google Scholar 
Schlägel, U. E. & Lewis, M. A. Methods Ecol. Evol. 5, 1236–1246 (2014).Article 

Google Scholar 
Ranc, N., Moorcroft, P. R., Ossi, F. & Cagnacci, F. Proc. Natl Acad. Sci. USA 118, e2014856118 (2021).Article 
CAS 

Google Scholar 
Ranc, N. et al. Sci. Rep. 10, 11946 (2020).Merkle, J. A., Fortin, D. & Morales, J. M. Ecol. Lett. 17, 924–931 (2014).Article 
CAS 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Trends Ecol. Evol. 34, 355–368 (2019).Article 

Google Scholar 
Rigoudy, N. L. A. et al. Behav. Ecol. 33, 789–797 (2022).Article 

Google Scholar 
Forrester, T. D., Casady, D. S. & Wittmer, H. U. Behav. Ecol. Sociobiol. 69, 603–612 (2015).Article 

Google Scholar 
Jesmer, B. R. et al. Science 361, 1023–1025 (2018).Article 
CAS 

Google Scholar  More

Börger, L., Dalziel, B. D. & Fryxell, J. M. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol. Lett. 11, 637–650 (2008).Article 

Google Scholar 
Burt, W. H. Territoriality and home range concepts as applied to mammals. J. Mammal. 24, 346 (1943).Article 

Google Scholar 
Darwin, C. On the Origin of Species by Means of Natural Selection (D. Appleton Co., 1859).Merkle, J., Fortin, D. & Morales, J. M. A memory‐based foraging tactic reveals an adaptive mechanism for restricted space use. Ecol. Lett. 17, 924–931 (2014).Article 
CAS 

Google Scholar 
Bordes, F., Morand, S., Kelt, D. A. & Van Vuren, D. H. Home range and parasite diversity in mammals. Am. Nat. 173, 467–474 (2009).Article 

Google Scholar 
Morales, J. M. et al. Building the bridge between animal movement and population dynamics. Philos. Trans. R. Soc. B: Biol. Sci. 365, 2289–2301 (2010).Article 

Google Scholar 
Lewis, M. A. & Murray, J. D. Modelling territoriality and wolf-deer interactions. Nature 366, 738–740 (1993).Article 

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

Google Scholar 
Wang, M. & Grimm, V. Home range dynamics and population regulation: an individual-based model of the common shrew Sorex araneus. Ecol. Modell. 205, 397–409 (2007).Article 

Google Scholar 
Moorcroft, P. R., Lewis, M. A. & Crabtree, R. L. Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proc. R. Soc. B: Biol. Sci. 273, 1651–1659 (2006).Article 

Google Scholar 
Powell, R. A. in Research Techniques in Animal Ecology Vol. 65 (eds. Boitani, L. & Fuller, T. K.) 599 (Columbia Univ. Press, 2000).Spencer, W. D. Home ranges and the value of spatial information. J. Mammal. 93, 929–947 (2012).Article 

Google Scholar 
Bracis, C., Gurarie, E., Van Moorter, B. & Goodwin, R. A. Memory effects on movement behavior in animal foraging. PLoS ONE 10, e0136057 (2015).Article 

Google Scholar 
Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).Article 

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

Google Scholar 
Stamps, J. Motor learning and the value of familiar space. Am. Nat. 146, 41–58 (1995).Article 

Google Scholar 
Gautestad, A. O. & Mysterud, I. Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns. Ecol. Modell. 221, 2741–2750 (2010).Article 

Google Scholar 
Gautestad, A. O. & Mysterud, I. Intrinsic scaling complexity in animal dispersion and abundance. Am. Nat. 165, 44–55 (2005).Article 

Google Scholar 
Merkle, J. A., Potts, J. R. & Fortin, D. Energy benefits and emergent space use patterns of an empirically parameterized model of memory‐based patch selection. Oikos 126, 185–196 (2017).Schlägel, U. E. & Lewis, M. A. Detecting effects of spatial memory and dynamic information on animal movement decisions. Methods Ecol. Evolution 5, 1236–1246 (2014).Article 

Google Scholar 
Van Moorter, B. et al. Memory keeps you at home: a mechanistic model for home range emergence. Oikos 118, 641–652 (2009).Article 

Google Scholar 
Riotte-Lambert, L., Benhamou, S. & Chamaillé-Jammes, S. How memory-based movement leads to nonterritorial spatial segregation. Am. Naturalist 185, E103–E116 (2015).Article 

Google Scholar 
Marchand, P. et al. Combining familiarity and landscape features helps break down the barriers between movements and home ranges in a non‐territorial large herbivore. J. Anim. Ecol. 86, 371–383 (2017).Article 

Google Scholar 
Gautestad, A. O., Loe, L. E. & Mysterud, A. Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models. J. Anim. Ecol. 82, 572–586 (2013).Article 

Google Scholar 
Ranc, N., Cagnacci, F. & Moorcroft, P. R. Memory drives the formation of animal home ranges: evidence from a reintroduction. Ecol. Lett. 25, 716–728 (2022).Article 

Google Scholar 
Ranc, N., Moorcroft, P. R., Ossi, F. & Cagnacci, F. Experimental evidence of memory-based foraging decisions in a large wild mammal. Proc. Natl Acad. Sci. USA 118, e2014856118 (2021).Article 
CAS 

Google Scholar 
Potts, J. R. & Lewis, M. A. A mathematical approach to territorial pattern formation. Am. Math. Monthly 121, 754–770 (2014).Article 

Google Scholar 
Shettleworth, S. J. Cognition, Evolution, and Behavior (Oxford Univ. Press, 2009).van Asselen, M. et al. Brain areas involved in spatial working memory. Neuropsychologia 44, 1185–1194 (2006).Article 

Google Scholar 
Paul, C., Magda, G. & Abel, S. Spatial memory: theoretical basis and comparative review on experimental methods in rodents. Behav. Brain Res. 203, 151–164 (2009).Article 

Google Scholar 
Boratyński, Z. Energetic constraints on mammalian home-range size. Funct. Ecol. 34, 468–474 (2020).Article 

Google Scholar 
Tamburello, N., Côté, I. M. & Dulvy, N. K. Energy and the scaling of animal space use. Am. Naturalist 186, 196–211 (2015).Article 

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

Google Scholar 
McNab, B. K. Food habits, energetics, and the population biology of mammals. Am. Naturalist 116, 106–124 (1980).Article 

Google Scholar 
Fokidis, H. B., Risch, T. S. & Glenn, T. C. Reproductive and resource benefits to large female body size in a mammal with female-biased sexual size dimorphism. Anim. Behav. 73, 479–488 (2007).Article 

Google Scholar 
Saïd, S. et al. What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118, 1299–1306 (2009).Article 

Google Scholar 
Schradin, C. et al. Female home range size is regulated by resource distribution and intraspecific competition: a long-term field study. Anim. Behav. 79, 195–203 (2010).Article 

Google Scholar 
Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evolution 1, 1123–1128 (2017).Article 

Google Scholar 
Croston, R., Branch, C., Kozlovsky, D., Dukas, R. & Pravosudov, V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459 (2015).Article 

Google Scholar 
Ashton, B. J., Ridley, A. R., Edwards, E. K. & Thornton, A. Cognitive performance is linked to group size and affects fitness in Australian magpies. Nature 554, 364–367 (2018).Article 
CAS 

Google Scholar 
Madden, J. R., Langley, E. J. G., Whiteside, M. A., Beardsworth, C. E. & Van Horik, J. O. The quick are the dead: pheasants that are slow to reverse a learned association survive for longer in the wild. Philos. Trans. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rstb.2017.0297 (2018).Sonnenberg, B. R., Branch, C. L., Pitera, A. M., Bridge, E. & Pravosudov, V. V. Natural selection and spatial cognition in wild food-caching mountain chickadees. Curr. Biol. 29, 670–676 (2019).Article 
CAS 

Google Scholar 
Shaw, R. C., MacKinlay, R. D., Clayton, N. S. & Burns, K. C. Memory performance influences male reproductive success in a wild bird. Curr. Biol. 29, 1498–1502.e3 (2019).Article 
CAS 

Google Scholar 
Gehr, B. et al. Stay home, stay safe—site familiarity reduces predation risk in a large herbivore in two contrasting study sites. J. Anim. Ecol. 89, 1329–1339 (2020).Article 

Google Scholar 
Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article 
CAS 

Google Scholar 
Willems, E. P. & Hill, R. A. Predator-specific landscapes of fear and resource distribution: effects on spatial range use. Ecology 90, 546–555 (2009).Article 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: spatial patterns of risk perception and response. Trends Ecol. Evolution 34, 355–368 (2019).Article 

Google Scholar 
Bose, S. et al. Implications of fidelity and philopatry for the population structure of female black-tailed deer. Behav. Ecol. 28, 983–990 (2017).Article 

Google Scholar 
Forrester, T. D., Casady, D. S. & Wittmer, H. U. Home sweet home: fitness consequences of site familiarity in female black-tailed deer. Behav. Ecol. Sociobiol. 69, 603–612 (2015).Article 

Google Scholar 
Magrath, R. D., Haff, T. M., Fallow, P. M. & Radford, A. N. Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biol. Rev. 90, 560–586 (2015).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Cognition and the evolution of camouflage. Proc. R. Soc. B: Biol. Sci. 283, 20152890 (2016).Article 

Google Scholar 
Dickinson, A. Associative learning and animal cognition. Philos. Trans. R. Soc. B: Biol. Sci. 367, 2733–2742 (2012).Article 

Google Scholar 
Baddeley, A. D. & Lieberman, K. in Exploring Working Memory 206–223 (Routledge, 2017).Olton, D. S. & Samuelson, R. J. Remembrance of places passed: spatial memory in rats. J. Exp. Psychol. Anim. Behav. Process. 2, 97–116 (1976).Article 

Google Scholar 
Lashley, K. S. Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain (Univ. Chicago Press, 1929).O’keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).Beardsworth, C. E. et al. Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy? Ecol. Lett. 24, 751–760 (2021).Article 

Google Scholar 
Rowe, C. & Healy, S. D. Measuring variation in cognition. Behav. Ecol. 25, 1287–1292 (2014).Article 

Google Scholar 
Warner, R. E. Use of cover by pheasant broods in east-central Illinois. J. Wildl. Manag. 43, 334 (1979).Article 

Google Scholar 
Toledo, S. et al. Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system. Science 369, 188–193 (2020).Article 
CAS 

Google Scholar 
Weiser, A. W. et al. Characterizing the accuracy of a self-synchronized reverse-GPS wildlife localization system. In Proc. 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 1–12 (IEEE, 2016).Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).Article 
CAS 

Google Scholar 
Beardsworth, C. E. et al. Validating ATLAS: a regional-scale high-throughput tracking system. Methods Ecol. Evolution 13, 1990–2004 (2022).Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: an r package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evolution 7, 1124–1132 (2016).Article 

Google Scholar 
Clutton‐Brock, T. H. & Harvey, P. H. Primates, brains and ecology. J. Zool. 190, 309–323 (1980).Article 

Google Scholar 
Avgar, T. et al. Space-use behaviour of woodland caribou based on a cognitive movement model. J. Anim. Ecol. 84, 1059–1070 (2015).Article 

Google Scholar 
Laundré, J. W., Hernández, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).Article 

Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory (Princeton Univ. Press, 2019).Beauchamp, G. Animal Vigilance: Monitoring Predators and Competitors. Animal Vigilance: Monitoring Predators and Competitors (Elsevier, 2015).Langley, E. J. G. et al. Heritability and correlations among learning and inhibitory control traits. Behav. Ecol. 31, 798–806 (2020).Article 

Google Scholar 
Chen, J., Zou, Y., Sun, Y.-H. & Ten Cate, C. Problem-solving males become more attractive to female budgerigars. Science 363, 166–167 (2019).Article 
CAS 

Google Scholar 
Vale, R., Evans, D. A. & Branco, T. Rapid spatial learning controls instinctive defensive behavior in mice. Curr. Biol. 27, 1342–1349 (2017).Article 
CAS 

Google Scholar 
Burt de Perera, T. & Guilford, T. Rapid learning of shelter position in an intertidal fish, the shanny Lipophrys pholis L. J. Fish. Biol. 72, 1386–1392 (2008).Article 

Google Scholar 
Font, E. Rapid learning of a spatial memory task in a lacertid lizard (Podarcis liolepis). Behav. Procs. 169, 103963 (2019).Article 

Google Scholar 
Senar, J. & Pascual, J. Keel and tarsus length may provide a good predictor of avian body size. Ard.-Wageningen 85, 269–274 (1997).
Google Scholar 
Lavielle, M. Detection of multiple changes in a sequence of dependent variables. Stoch. Process. Appl. 83, 79–102 (1999).Article 

Google Scholar 
Calenge, C. The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Millspaugh, J. J. A Manual for Wildlife Radio Tagging Robert E. Kenward. The Auk 118 (Academic Press, 2001).Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Grahn, M., Göransson, G. & Von Schantz, T. Territory acquisition and mating success in pheasants, Phasianus colchicus: an experiment. Anim. Behav. 46, 721–730 (1993).Article 

Google Scholar 
Ridley, M. W. & Hill, D. A. Social organization in the pheasant (Phasianus colchicus): harem formation, mate selection and the role of mate guarding. J. Zool. 211, 619–630 (1987).Article 

Google Scholar 
Gompper, M. E. & Gittleman, J. L. Home range scaling: intraspecific and comparative trends. Oecologia 87, 343–348 (1991).Article 

Google Scholar 
Fisher, R. A. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N. L.) 66–70 (Springer, 1992).Barton, K. MuMIn: Multi-Model Inference (cran.r-project.org, 2022).Nakagawa, S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav. Ecol. 15, 1044–1045 (2004).Article 

Google Scholar 
Heathcote, R. Data for ‘Spatial memory predicts home range size and predation risk in pheasants’ nature ecology and evolution. Mendeley Data https://doi.org/10.17632/m89226xg6p.1 (2022). More