Ecology

This section provides an overview of methods. The Supplementary Information provides additional detailed descriptions of the leech collections, laboratory processing, bioinformatics pipeline, and site-occupancy modelling. Code for our bioinformatics pipeline is available at Ji72 and Yu73. Code for our site-occupancy modelling and analysis is available at Baker et al.74.Leech collectionsSamples were collected during the rainy season, from July to September 2016, by park rangers from the Ailaoshan Forestry Bureau. The nature reserve is divided into 172 non-overlapping patrol areas defined by the Yunnan Forestry Survey and Planning Institute. These areas range in size from 0.5 to 12.5 km2 (mean 3.9 ± sd 2.5 km2), in part reflecting accessibility (smaller areas tend to be more rugged). These patrol areas pre-existed our study, and are used in the administration of the reserve. The reserve is divided into six parts, which are managed by six cities or autonomous counties (NanHua, ChuXiong, JingDong, ZhenYuan, ShuangBai, XinPing) which assign patrol areas to the villages within their jurisdiction based on proximity. The villages establish working groups to carry out work within the patrol areas. Thus, individual park rangers might change every year, but the patrol areas and the villages responsible for them are fixed.Each ranger was supplied with several small bags containing tubes filled with RNAlater preservative. Rangers were asked to place any leeches they could collect opportunistically during their patrols (e.g. from the ground or clothing) into the tubes, in exchange for a one-off payment of RMB 300 ( ~USD 45) for participation, plus RMB 100 if they caught one or more leeches. Multiple leeches could be placed into each tube, but the small tube sizes generally required the rangers to use multiple tubes for their collections.A total of 30,468 leeches were collected in 3 months by 163 rangers across all 172 patrol areas. When a bag of tubes contained  More

Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).
Google Scholar 
Wilson, R. J., Thomas, C. D., Fox, R., Roy, D. B. & Kunin, W. E. Spatial patterns in species distributions reveal biodiversity change. Nature 432, 393–396 (2004).CAS 
PubMed 

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

Google Scholar 
Ings, T. C. et al. Ecological networks—beyond food webs. J. Anim. Ecol. 78, 253–269 (2009).PubMed 

Google Scholar 
Dunne, J. A. & Williams, R. J. Cascading extinctions and community collapse in model food webs. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 364, 1711–1723 (2009).
Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
Vellend, M. The Theory of Ecological Communities Vol. 57 229 (Princeton University Press, 2016).Altermatt, F. Diversity in riverine metacommunities: a network perspective. Aquat. Ecol. 47, 365–377 (2013).
Google Scholar 
Peterson, E. E. et al. Modelling dendritic ecological networks in space: an integrated network perspective. Ecol. Lett. 16, 707–719 (2013).PubMed 

Google Scholar 
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: patterns, processes, and pathways. Freshw. Biol. 63, 141–163 (2018).
Google Scholar 
Muneepeerakul, R. et al. Neutral metacommunity models predict fish diversity patterns in Mississippi-Missouri basin. Nature 453, 220–222 (2008).CAS 
PubMed 

Google Scholar 
Besemer, K. et al. Headwaters are critical reservoirs of microbial diversity for fluvial networks. Proc. Biol. Sci. 280, 20131760 (2013).PubMed 
PubMed Central 

Google Scholar 
Finn, D. S., Bonada, N., Múrria, C. & Hughes, J. M. Small but mighty: headwaters are vital to stream network biodiversity at two levels of organization. J. North Am. Benthol. Soc. 30, 963–980 (2011).
Google Scholar 
Altermatt, F., Seymour, M. & Martinez, N. River network properties shape α-diversity and community similarity patterns of aquatic insect communities across major drainage basins. J. Biogeogr. 40, 2249–2260 (2013).
Google Scholar 
Harvey, E., Gounand, I., Fronhofer, E. A. & Altermatt, F. Disturbance reverses classic biodiversity predictions in river-like landscapes. Proc. R. Soc. B: Biol. Sci. 285, 20182441 (2018).
Google Scholar 
Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).
Google Scholar 
Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).PubMed 

Google Scholar 
Woodward, G. & Hildrew, A. G. Food web structure in riverine landscapes. Freshw. Biol. 47, 777–798 (2002).
Google Scholar 
Williams, R. J. & Martinez, N. D. Limits to trophic levels and omnivory in complex food webs: theory and data. Am. Nat. 163, 458–468 (2004).PubMed 

Google Scholar 
Thompson, R. M. & Townsend, C. R. The effect of seasonal variation on the community structure and food-web attributes of two streams: implications for food-web science. Oikos 87, 75–88 (1999).
Google Scholar 
Wood, S. A., Russell, R., Hanson, D., Williams, R. J. & Dunne, J. A. Effects of spatial scale of sampling on food web structure. Ecol. Evol. 5, 3769–3782 (2015).PubMed 
PubMed Central 

Google Scholar 
Tylianakis, J. M. & Morris, R. J. Ecological networks across environmental gradients. Annu. Rev. Ecol., Evolution, Syst. 48, 25–48 (2017).
Google Scholar 
Romanuk, T. N. et al. The structure of food webs along river networks. Ecography 29, 3–10 (2006).
Google Scholar 
Olivier, P. et al. Exploring the temporal variability of a food web using long‐term biomonitoring data. Ecography 42, 2107–2121 (2019).
Google Scholar 
Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).PubMed 

Google Scholar 
Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. Camb. Philos. Soc. https://doi.org/10.1111/brv.12433 (2018).Article 
PubMed 

Google Scholar 
Tavares-Cromar, A. F. & Williams, D. D. The importance of temporal resolution in food web analysis: Evidence from a detritus-based stream. Ecol. Monogr. 66, 91–113 (1996).
Google Scholar 
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).
Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA—an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).
Google Scholar 
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dunne, J. A. In Ecological Networks: Linking Structure and Dynamics (eds. Pascual, J. A. & Dunne, J. A.) 27–86 (University Press, 2006).Neff, F. et al. Changes in plant-herbivore network structure and robustness along land-use intensity gradients in grasslands and forests. Sci Adv 7, eabf3985 (2021).O’Connor, M. J. et al. Unveiling the food webs of tetrapods across Europe through the prism of the Eltonian niche. J. Biogeogr. 47, 181–192 (2020).
Google Scholar 
Pellissier, L. et al. Comparing species interaction networks along environmental gradients. Biol. Rev. Camb. Philos. Soc. 93, 785–800 (2018).PubMed 

Google Scholar 
Saravia, L. A. et al. Ecological network assembly: how the regional metaweb influences local food webs. BioRxiv, https://doi.org/10.1101/340430 (2021).Blackman, R. C. et al. Mapping biodiversity hotspots of fish communities in subtropical streams through environmental DNA. Sci. Rep. 4, e65352 (2021).
Google Scholar 
Baselga, A. & Orme, C. D. L. betapart: an R package for the study of beta diversity: Betapart package. Methods Ecol. Evol. 3, 808–812 (2012).
Google Scholar 
Seymour, M. et al. Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks. Sci. Total Environ. 729, 138801 (2020).CAS 
PubMed 

Google Scholar 
D’Alessandro, S. & Mariani, S. Sifting environmental DNA metabarcoding data sets for rapid reconstruction of marine food webs. Fish Fish 22, 822–833 (2021).
Google Scholar 
Zhang, Y. et al. Holistic pelagic biodiversity monitoring of the Black Sea via eDNA metabarcoding approach: From bacteria to marine mammals. Environ. Int. 135, 105307 (2020).PubMed 

Google Scholar 
Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).
Google Scholar 
Widder, S. et al. Fluvial network organization imprints on microbial co-occurrence networks. Proc. Natl Acad. Sci. USA 111, 12799–12804 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 512 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mächler, E. et al. Assessing different components of diversity across a river network using eDNA. Environ. DNA 1, 290–301 (2019).
Google Scholar 
Peralta-Maraver, I., López-Rodríguez, M. J. & de Figueroa, J. M. T. Structure, dynamics and stability of a Mediterranean river food web. Mar. Freshw. Res. 68, 484–495 (2017).
Google Scholar 
Woodward, G. et al. Ecological networks in a changing climate. Ecol. Netw. 42, 71–138 (2010).
Google Scholar 
Kondoh, M., Kato, S. & Sakato, Y. Food webs are built up with nested subwebs. Ecology 91, 3123–3130 (2010).PubMed 

Google Scholar 
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Google Scholar 
Power, M. E. & Dietrich, W. E. Food webs in river networks. Ecol. Res. https://doi.org/10.1046/j.0912-3814.2002.00503.x (2002).Montoya, D., Yallop, M. L. & Memmott, J. Functional group diversity increases with modularity in complex food webs. Nat. Commun. 6, 7379 (2015).CAS 
PubMed 

Google Scholar 
Gravel, D., Albouy, C. & Thuiller, W. The meaning of functional trait composition of food webs for ecosystem functioning. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 371, 20150268 (2016).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).
Google Scholar 
Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585 (2020).PubMed 
PubMed Central 

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

Google Scholar 
Bista, I. et al. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 8, 14087 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Erickson, R. A., Merkes, C. M., Jackson, C. A., Goforth, R. R. & Amberg, J. J. Seasonal trends in eDNA detection and occupancy of bigheaded carps. J. Gt. Lakes Res. 43, 762–770 (2017).
Google Scholar 
Troth, C. R., Sweet, M. J., Nightingale, J. & Burian, A. Seasonality, DNA degradation and spatial heterogeneity as drivers of eDNA detection dynamics. Sci. Total Environ. 768, 144466 (2021).CAS 
PubMed 

Google Scholar 
Thalinger, B. et al. The effect of activity, energy use, and species identity on environmental DNA shedding of freshwater fish. Front. Ecol. Evolution 9, 73 (2021).
Google Scholar 
Kelly, R. P., Port, J. A., Yamahara, K. M. & Crowder, L. B. Using environmental DNA to census marine fishes in a large mesocosm. PLoS ONE 9, e86175 (2014).PubMed 
PubMed Central 

Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 

Google Scholar 
Liu, C. M. et al. BactQuant: An enhanced broad-coverage bacterial quantitative real-time PCR assay. BMC Microbiol. 12, 56 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mansfeldt, C. et al. Microbial community shifts in streams receiving treated wastewater effluent. Sci. Total Environ. 709, 135727 (2020).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Andrews, S. FASTQC A Quality Control tool for High Throughput Sequence Data (Babraham Institute, 2015).Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).PubMed 
PubMed Central 

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

Google Scholar 
Csárdi, G. & Nepusz, T. The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package 2.5-6. https://CRAN.Rproject.org/package=vegan (2019).Tachet, H., Bournaud, M., Richoux, P. & Usseglio-Polatera, P. Invertébrés d’eau douce—systématique, biologie, écologie (CNRS Editions, 2010).Schmidt-Kloiber, A. & Hering, D. www.freshwaterecology.info—an online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecol. Indic. 53, 271–282 (2015).Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. Rev. 75, 14–49 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).PubMed 

Google Scholar 
Johnson, S., Domínguez-García, V., Donetti, L. & Muñoz, M. A. Trophic coherence determines food-web stability. Proc. Natl Acad. Sci. USA 111, 17923–17928 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wootton, K. L. Omnivory and stability in freshwater habitats: Does theory match reality? Freshw. Biol. 62, 821–832 (2017).
Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw., Artic. 82, 1–26 (2017).
Google Scholar 
Lenth, R. V. Estimated Marginal Means, aka Least-Squares Means [R package emmeans version 1.6.1] (2021).RStudio Team RStudio: Integrated development for R. RStudio, PBC, Boston, MA. R version 4.0.4 Retrieved from http://www.rstudio.com/ (2021) More

Jeltsch, F. et al. Integrating movement ecology with biodiversity research—Exploring new avenues to address spatiotemporal biodiversity dynamics. Mov. Ecol. 1, 6 (2013).PubMed 
PubMed Central 

Google Scholar 
Dingle, H. Migration: The Biology of Life on the Move Migration (Oxford University Press, 2014).Joly, K. et al. Longest terrestrial migrations and movements around the world. Sci. Rep. 9, 15333 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lundberg, J. & Moberg, F. Mobile link organisms and ecosystem functioning: Implications for ecosystem resilience and management. Ecosystems 6, 0087–0098 (2003).
Google Scholar 
Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science 344, 1242552 (2014).CAS 
PubMed 

Google Scholar 
Nifong, J. C., Layman, C. A. & Silliman, B. R. Size, sex and individual-level behaviour drive intrapopulation variation in cross-ecosystem foraging of a top-predator. J. Anim. Ecol. 84, 35–48 (2015).PubMed 

Google Scholar 
Giroux, M.-A. et al. Benefiting from a migratory prey: Spatio-temporal patterns in allochthonous subsidization of an arctic predator. J. Anim. Ecol. 81, 533–542 (2012).PubMed 

Google Scholar 
Allen, A. M. & Singh, N. J. Linking movement ecology with wildlife management and conservation. Front. Ecol. Evol. 3, 155 (2016).
Google Scholar 
Bunnefeld, N. et al. A model-driven approach to quantify migration patterns: Individual, regional and yearly differences. J. Anim. Ecol. 80, 466–476 (2011).PubMed 

Google Scholar 
Teitelbaum, C. S. & Mueller, T. Beyond migration: Causes and consequences of nomadic animal movements. Trends Ecol. Evol. 34, 569–581 (2019).PubMed 

Google Scholar 
Berg, J. E., Hebblewhite, M., St. Clair, C. C. & Merrill, E. H. Prevalence and mechanisms of partial migration in ungulates. Front. Ecol. Evol. 7, 325 (2019).
Google Scholar 
Avgar, T., Street, G. & Fryxell, J. M. On the adaptive benefits of mammal migration. Can. J. Zool. 92, 481–490 (2014).
Google Scholar 
Barbour, M. G. & Billings, W. D. North American Terrestrial Vegetation (Cambridge University Press, 2000).
Google Scholar 
Smith, S. L., Throop, J. & Lewkowicz, A. G. Recent changes in climate and permafrost temperatures at forested and polar desert sites in northern Canada. Can. J. Earth Sci. 49, 914–924 (2012).ADS 

Google Scholar 
Lévesque, E. Plant Distribution and Colonization in Extreme Polar Deserts, Ellesmere Island, Canada (University of Toronto, 1997).
Google Scholar 
Bliss, L. C., Svoboda, J. & Bliss, D. I. Polar deserts, their plant cover and plant production in the Canadian High Arctic. Holarctic Ecol. 7, 305–324 (1984).
Google Scholar 
Berteaux, D. et al. Effects of changing permafrost and snow conditions on tundra wildlife: Critical places and times. Arctic Sci. 3, 65–90 (2017).
Google Scholar 
Duchesne, D., Gauthier, G. & Berteaux, D. Habitat selection, reproduction and predation of wintering lemmings in the Arctic. Oecologia 167, 967–980 (2011).ADS 
PubMed 

Google Scholar 
Fuglei, E., Blanchet, M.-A., Unander, S., Ims, R. A. & Pedersen, Å. Ø. Hidden in the darkness of the Polar night: A first glimpse into winter migration of the Svalbard rock ptarmigan. Wildl. Biol. 2017, SP1 (2017).
Google Scholar 
Schmidt, N. M. et al. Ungulate movement in an extreme seasonal environment: Year-round movement patterns of high-arctic muskoxen. Wildl. Biol. 22, 253–267 (2016).
Google Scholar 
Berteaux, D. & Lai, S. Walking on water: Terrestrial mammal migrations in the warming Arctic. Anim. Migr. 8, 65–73 (2021).
Google Scholar 
Gnanadesikan, G. E., Pearse, W. D. & Shaw, A. K. Evolution of mammalian migrations for refuge, breeding, and food. Ecol. Evol. 7, 5891–5900 (2017).PubMed 
PubMed Central 

Google Scholar 
Best, T. L. & Henry, T. H. Lepus arcticus. Mamm. Species 1–9 (1994).Dalerum, F. et al. Exploring the diet of arctic wolves (Canis lupus arctos) at their northern range limit. Can. J. Zool. 96, 277–281 (2018).
Google Scholar 
Mech, L. D. Annual arctic wolf pack size related to arctic hare numbers. Arctic 60, 309–311 (2007).
Google Scholar 
Small, R. J., Keith, L. B. & Barta, R. M. Demographic responses of Arctic hares Lepus arcticus placed on two predominantly forested islands in Newfoundland. Ecography 15, 161–165 (1992).
Google Scholar 
Small, R. J., Keith, L. B. & Barta, R. M. Dispersion of introduced arctic hares (Lepus arcticus) on islands off Newfoundland’s south coast. Can. J. Zool. 69, 2618–2623 (1991).
Google Scholar 
Hearn, B. J., Keith, L. B. & Rongstad, O. J. Demography and ecology of the arctic hare (Lepus arcticus) in southwestern Newfoundland. Can. J. Zool. 65, 852–861 (1987).
Google Scholar 
Harper, F. The Mammals of Keewatin Vol. 12 (Miscellaneaous Publications, Museum of Natural History, University of Kansas, 1956).
Google Scholar 
Dalerum, F. et al. Spatial variation in Arctic hare (Lepus arcticus) populations around the Hall Basin. Polar Biol. 40, 2113–2118 (2017).
Google Scholar 
Fraser, K. C. et al. Tracking the conservation promise of movement ecology. Front. Ecol. Evol. 6, 150 (2018).
Google Scholar 
CAFF. Arctic Biodiversity Assessment. Status and trends in Arctic biodiversity. Conservation of Arctic Flora and Fauna, Akureyri (2013).Desjardins, É. et al. Survey of the vascular plants of Alert (Ellesmere Island, Canada), a polar desert at the northern tip of the Americas. CheckList 17, 181–225 (2021).
Google Scholar 
Keith, L. B., Meslow, E. C. & Rongstad, O. J. Techniques for snowshoe hare population studies. J. Wildl. Manag. 32, 801–812 (1968).
Google Scholar 
Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing Arctic. Science 370, 712–715 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Wikelski, M., Davidson, S. C. & Kays, R. Movebank: Archive, analysis and sharing of animal movement data. Hosted by the Max Planck Institute of Animal Behavior. http://www.movebank.org (2021).Berteaux, D. Data from: Study ‘Arctic hare Alert—Argos tracking’. MoveBank Data Repository https://doi.org/10.5441/001/1.d5d912c4 (2021).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Christin, S., St-Laurent, M.-H. & Berteaux, D. Evaluation of Argos telemetry accuracy in the High-Arctic and implications for the estimation of home-range size. PLoS One 10, e0141999 (2015).PubMed 
PubMed Central 

Google Scholar 
QGIS Association. QGIS Geographic Information System (2021).Harris, S. et al. Home-range analysis using radio-tracking data? A review of problems and techniques particularly as applied to the study of mammals. Mamm. Rev. 20, 97–123 (1990).
Google Scholar 
Le Corre, M., Dussault, C. & Côté, S. D. Detecting changes in the annual movements of terrestrial migratory species: Using the first-passage time to document the spring migration of caribou. Mov. Ecol. 2, 19 (2014).PubMed 
PubMed Central 

Google Scholar 
Nicholson, K. L., Arthur, S. M., Horne, J. S., Garton, E. O. & Vecchio, P. A. D. Modeling caribou movements: Seasonal ranges and migration routes of the central Arctic herd. PLoS One 11, e0150333 (2016).PubMed 
PubMed Central 

Google Scholar 
Nelson, M. E., Mech, L. D. & Frame, P. F. Tracking of white-tailed deer migration by global positioning system. J. Mammal. 85, 505–510 (2004).
Google Scholar 
Singh, N. J. & Ericsson, G. Changing motivations during migration: Linking movement speed to reproductive status in a migratory large mammal. Biol. Lett. 10, 20140379 (2014).PubMed 
PubMed Central 

Google Scholar 
Jakes, A. F. et al. Classifying the migration behaviors of pronghorn on their northern range. J. Wildl. Manag. 82, 1229–1242 (2018).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. (2015).Duong, T. ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R. J. Stat. Softw. 21, 1–16 (2007).
Google Scholar 
Gitzen, R. A., Millspaugh, J. J. & Kernohan, B. J. Bandwidth selection for fixed-kernel analysis of animal utilization distributions. J. Wildl. Manag. 70, 1334–1344 (2006).
Google Scholar 
Austin, R. E. et al. Patterns of at-sea behaviour at a hybrid zone between two threatened seabirds. Sci. Rep. 9, 14720 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gillis, E. A. & Krebs, C. J. Natal dispersal of snowshoe hares during a cyclic population increase. J. Mammal. 80, 933–939 (1999).
Google Scholar 
Dahl, F. & Willebrand, T. Natal dispersal, adult home ranges and site fidelity of mountain hares (Lepus timidus) in the boreal forest of Sweden. Wildl. Biol. 11, 309–317 (2005).
Google Scholar 
Angerbjörn, A. & Flux, J. E. C. Lepus timidus. Mamm. Species 495, 1–11 (1995).
Google Scholar 
Smith, G. W., Stoddart, L. C. & Knowlton, F. F. Long-distance movements of black-tailed jackrabbits. J. Wildl. Manag. 66, 463 (2002).
Google Scholar 
Cote, J. et al. Behavioural synchronization of large-scale animal movements—Disperse alone, but migrate together?. Biol. Rev. 92, 1275–1296 (2017).PubMed 

Google Scholar 
Bauer, S., McNamara, J. M. & Barta, Z. Environmental variability, reliability of information and the timing of migration. Proc. R. Soc. B 287, 20200622 (2020).PubMed 
PubMed Central 

Google Scholar 
Couzin, I. D. Collective animal migration. Curr. Biol. 28, R976–R980 (2018).CAS 
PubMed 

Google Scholar 
Lai, S. et al. Unsuspected mobility of Arctic hares revealed by longest journey ever recorded in a lagomorph. Ecology 103(3), e3620 https://doi.org/10.1002/ecy.3620 (2022).PubMed 

Google Scholar 
Abrahms, B. et al. Suite of simple metrics reveals common movement syndromes across vertebrate taxa. Mov. Ecol. 5, 12 (2017).PubMed 
PubMed Central 

Google Scholar 
Chapman, B. B., Brönmark, C., Nilsson, J. -Å. & Hansson, L.-A. The ecology and evolution of partial migration. Oikos 120, 1764–1775 (2011).
Google Scholar 
Singh, N. J., Börger, L., Dettki, H., Bunnefeld, N. & Ericsson, G. From migration to nomadism: Movement variability in a northern ungulate across its latitudinal range. Ecol. Appl. 22, 2007–2020 (2012).PubMed 

Google Scholar 
Bastille-Rousseau, G. et al. Flexible characterization of animal movement pattern using net squared displacement and a latent state model. Mov. Ecol. 4, 15 (2016).PubMed 
PubMed Central 

Google Scholar 
Mueller, T. & Fagan, W. F. Search and navigation in dynamic environments—From individual behaviors to population distributions. Oikos 117, 654–664 (2008).
Google Scholar 
Krebs, C. J., Boonstra, R. & Boutin, S. Using experimentation to understand the 10-year snowshoe hare cycle in the boreal forest of North America. J. Anim. Ecol. 87, 87–100 (2018).PubMed 

Google Scholar 
Reid, N. & Harrison, A. Post-release GPS tracking of hand-reared Irish hare Lepus timidus hibernicus leverets, Slemish, Co. Antrim, Northern Ireland. J. Wildl. Rehabil. 31, 25 (2011).
Google Scholar 
Weterings, M. J. A. et al. Strong reactive movement response of the medium-sized European hare to elevated predation risk in short vegetation. Anim. Behav. 115, 107–114 (2016).
Google Scholar 
Krebs, C. J., Boutin, S. & Boonstra, R. Ecosystem Dynamics of the Boreal Forest: The Kluane Project (Oxford University Press, 2001).
Google Scholar 
Feierabend, D. & Kielland, K. Movements, activity patterns, and habitat use of snowshoe hares (Lepus americanus) in interior Alaska. J. Mammal. 95, 525–533 (2014).
Google Scholar 
Levänen, R., Pohjoismäki, J. L. O. & Kunnasranta, M. Home ranges of semi-urban brown hares (Lepus europaeus) and mountain hares (Lepus timidus) at northern latitudes. Ann. Zool. Fenn. 56, 107–120 (2019).
Google Scholar 
Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl. Acad. Sci. U.S.A. 105, 19052–19059 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Abrahms, B. et al. Emerging perspectives on resource tracking and animal movement ecology. Trends Ecol. Evol. 36, 308–320 (2021).PubMed 

Google Scholar 
France, R. L. The Lake Hazen trough: A late winter oasis in a polar desert. Biol. Conserv. 63, 149–151 (1993).
Google Scholar 
Jenkins, D. A., Campbell, M., Hope, G., Goorts, J. & McLoughlin, P. Recent trends in abundance of Peary caribou (Rangifer tarandus pearyi) and muskoxen (Ovibos moschatus) in the Canadian Arctic Archipelago, Nunavut 233.Mech, L. Proportion of calves and adult muskoxen, Ovibos moschatus killed by gray wolves, Canis lupus, in July on Ellesmere Island (USGS Northern Prairie Wildlife Research Center, 2010).
Google Scholar 
Gunn, A., Miller, F., Barry, S. & Buchan, A. A near-total decline in caribou on Prince of Wales, Somerset, and Russell Islands, Canadian Arctic. Arctic 59, 1–13 (2006).
Google Scholar 
Edwards, J. Diet shifts in moose due to predator avoidance. Oecologia 60, 185–189 (1983).ADS 
PubMed 

Google Scholar 
Gustine, D. D., Parker, K. L., Lay, R. J., Gillingham, M. P. & Heard, D. C. Calf survival of woodland caribou in a multi-predator ecosystem. Wildl. Monogr. 165, 1–32 (2006).
Google Scholar 
Klein, D. & Bay, C. Diet selection by vertebrate herbivores in the High Arctic of Greenland. Ecography 14, 152–155 (1991).
Google Scholar 
Parks Canada. Resource Description and Analysis—Ellesmere Island National Park Reserve Vol. 1 (Natural Resource Conservation Section, Parks Canada, Department of Canadian Heritage, 1994).
Google Scholar 
Parks Canada. Quttinirpaaq National Park of Canada: Management plan 76. https://www.pc.gc.ca/en/pn-np/nu/quttinirpaaq/info/index/gestion-management-2009 (2009).Winkler, D. W. et al. Cues, strategies, and outcomes: How migrating vertebrates track environmental change. Mov. Ecol. 2, 10 (2014).
Google Scholar 
Robinson, R. et al. Travelling through a warming world: Climate change and migratory species. Endang. Species Res. 7, 87–99 (2009).ADS 

Google Scholar  More

Site descriptionIn 2012, a field experiment was conducted in the Aksu National Experimental Station of Oasis Farmland Ecosystem27 (40°37′ N, 80°45′ E, altitude 1028 m) (Fig. 1), located in the west of Tarim River Basin in Xinjiang Province, China. The experimental area had a typical temperate arid climate. During the study period (May to October), the average minimum and maximum temperatures varied between 16.7 and 34.8 ℃ respectively.Figure 1Location of the Aksu National Experimental Station of Oasis Farmland Ecosystem (the map was created by software: QGIS Version 3.16.15 LTR: URL, https://www.qgis.org/en/site/).Full size imageThe cotton fields where the experiment conducted were public land, belong to Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, China. With the permissions of Xinjiang Institute of Ecology and Geography, we conducted experiments in the cotton field of the Aksu National Experimental Station of Oasis Farmland Ecosystem.Experimental designTwo treatments, each 10 m × 10 m in size, were established on one of cotton fields at the Aksu National Experimental Station of Oasis Farmland Ecosystem on April 5, 2012.One treatment planting cotton with TC method, the other with MC method. For the MC method, a high-density and air-tight transparent polythene film (0.01–0.02 mm thick, 1.25 m wide) was placed over the soil surface before sowing. Small holes (0.02 m × 0.02 m, at 0.1 m intervals within a row) in the plastic film were made to place cotton seeds. Four rows were sown on each strip of plastic film. For the TC treatment, the plants were sown as that for the MC treatment. The planting density (266 667plant ha−1) and irrigation pattern (frequency and volume of irrigation) for the TC method were entirely consistent with those for the MC method.Half-hourly measurements of soil CO2 efflux, soil temperature and moisture were made on 6 June 2012. The whole experiment was completed on 4 November 2012. According to irrigation, the whole experiment can be divided into three stages: stage before irrigation (from 6 to 24 June), during irrigation (from 25 June to 10 October) and irrigation stop stage (from 11October to 4 November). During the irrigation period, we conducted seven times of irrigation (once in two week). The water-soluble compound fertilizer (N + P2O5 + K2O ≥ 51%) was used for fertilization in the experimental field, and the application rate was 30 g m−2. We dissolved water-soluble compound fertilizer in water and sprayed into the field by sprayer. During the irrigation period, the fertilizer was applied for 5 times.The cottonseeds we used in this study comply with the provisions of the regulations of the People’s Republic of China on Seed Administration and the detailed rules for the implementation of crop seeds. The fertilization we used in this study comply with the provisions of the People’s Republic of China on Chemical fertilizer standard. All the experiments we conducted in the cotton field of Aksu oasis farmland ecosystem National Experimental Station met the provisions of the agricultural law of the People’s Republic of China. We also carried out the experiment of this study under the guidance of the provisions of the measures for the administration of national field scientific observation and research stations.Field measurement of soil CO2 concentrationSolid-state CO2 sensors (GMM221 and GMM222, Vaisala, Finland) were installed in the midpoint of each treatment to measure soil CO2 concentration. A cable connected each soil probe with a transimitter body placed on the ground. The transimitter sent output signals from the probe to a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA) and to an optional LCC display on the transmitter.In each treatment, four CO2 concentration sensors were buried at depths of 0 cm, 5 cm, 10 cm and 15 cm. Soil CO2 concentrations were recorded once in 30 min. The measurement of soil CO2 concentrations were conducted from 6 June 2012 to 4 November 2012.On 8 November, these sensors were excavated and recalibrated in the laboratory. We found no change in the slope or offset.Environmental and soil CO2 efflux measurementsThe soil water content and temperature at the same soil depth with solid-state CO2 sensors were measured on the cotton fields at the Aksu National Experimental Station of Oasis Farmland Ecosystem27,28, respectively. Soil volumetric water content and soil temperature were measured using soil moisture probes (pF-Meter, EcoTech GmbH, Bonn, Germany)26 and temperature probes (PT100,Heraeus Sensor Technology, Kleinostheim, Germany)26, respectively.Bulk density was determined by core method29. Briefly, a cylindrical metal sampler (volume of 100cm3) was inserted into the soil and carefully removed to preserve the sample. The sample was oven-dried at 105 °C and weighed. The ratio between dry weight of the soil sample and the cylinder volume was applied to provide the bulk density.Half-hourly soil CO2 efflux measurements were conducted using a closed dynamic chamber method26 (CIRAS-1 PP Systems, Hitchin, UK) on the TC treatment, beginning on 6 June 2012. A chamber, with a diameter of 9.96 cm and a volume of 1, 170 cm3 was inserted into the soil at depth of 3 cm. Soil CO2 concentrations were measured by infrared gas analyzer. The collecting of CO2 from each sampling point took 120 s to get reliable estimates of soil CO2 efflux.Data analysisIn order to calculate CO2 efflux in soil, Fick’s first law of diffusion was used:$$F_{i} = – D_{s} frac{dc}{{dz}}$$
(1)
where Fi is the CO2 efflux at depth zi, Ds the CO2 diffusion coefficient in the soil, and dc / dz the vertical soil CO2 gradient. In this study, the vertical CO2 gradient (dC/dz) was approximately a constant at different depths of soil in our site for the field conditions experienced in the TC treatment during study period. However, a quadratic function of depth to concentrations fitted to soil CO2 concentration gradients in the MC treatment.Ds can be estimated as$$D_{s} = xi D_{a}$$
(2)
where ξ is the gas tortuosity factor and Da is the CO2 diffusion coefficient in free air. The effect of temperature and pressure on Da is given by$$D_{a} = D_{a} 0left( {frac{T}{293.15}} right)^{1.75} left( {frac{P}{101.3}} right)$$
(3)
where T is the temperature (K), P the air pressure (kPa), Dao a reference value of Da at 20 °C (293.15 K) and 101.3 kPa, and is given as 14.7 mm2 s–130 .There are several empirical models in the literature for computing ξ31. We used the Millington–Quirk model32:$$xi = frac{{alpha^{10/3} }}{{phi^{2} }}$$
(4)
where a is the volumetric air content (air-filled porosity), Φ is the porosity. Note,$$phi = alpha + theta = 1 – frac{{rho_{b} }}{{rho_{m} }}$$
(5)
where ρb is the bulk density, and ρm is the particle density for the mineral soil.Soil surface CO2 efflux was calculated using the CO2 gradient flux method based on CO2 concentrations within the soil profile1. Briefly, the flux of CO2 between any two layers in the soil profile was calculated using the Moldrup model33.In order to determine soil CO2 storage, the equation for CO2 was performed.$${S}_{C{O}_{2}}=frac{partial (aC)}{partial t}$$
(6)
where C (ppm) is the concentration of CO2 within the soil pores, (a) is the aerial porosity of the soil layer, D is the molecular diffusivity of CO2 with the soil, and S(µmol m−3 s−1)is the source strength in the soil layer at depth.We determined temperature responses for soil CO2 efflux using the van’t Hoff equation34 (Eq. 7);$$R = R0e^{BT}$$
(7)
where R is soil CO2 efflux, T is soil temperature (°C) at 10 cm depth, and R0 is the soil respiration rate at a reference temperature of 0 °C (µmol m−3 s−1).The Q10 value for Eq. (8) was calculated according to definition as:$$Q_{{{1}0}} = R_{{{text{T}} + {1}0}} /R_{{text{T}}} = {text{ e}}^{{{1}0{text{B}}}}$$
(8)
where RT and RT+10 are Rr or Rd rates at temperature T and T + 10, respectively. The Q10 value is independent of temperature in Eq. (8). More

Global patterns of ARG distributionWe used a set of 4572 metagenomic samples to illustrate the global patterns of ARG distribution (Supplementary Data 1). These samples were collected from six types of habitats: air, aquatic, terrestrial, engineered, humans and other hosts (Fig. 1a and Supplementary Data 1). From these samples, we identified a total of 2561 ARGs that conferred resistance to 24 drug classes of antibiotics based on the Comprehensive Antibiotic Research Database (CARD). Of these, 2401 were genes conferring resistance to only one drug class, and 160 conferred resistances to multiple drug classes (Supplementary Data 2). Twenty-five ARGs were found in more than 75% samples, however, the frequency of most ARGs (2313/2561) were More

Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94(1), 11–17 (2013).PubMed 

Google Scholar 
Alexander, R. M. When is migration worthwhile for animals that walk, swim or fly?. J. Avian Biol. 29(4), 387–394 (1998).
Google Scholar 
Wikelski, M. et al. Costs of migration in free-flying songbirds. Nature 423, 704 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103(2), 247–260 (2003).
Google Scholar 
Alerstam, T. Optimal bird migration revisited. J. Ornithol. 152, 5–23 (2011).
Google Scholar 
Hedenstrom, A. & Alerstam, T. Optimum fuel loads in migratory birds: Distinguishing between time and energy minimization. J. Theor. Biol. 189, 227–234 (1997).ADS 
CAS 
PubMed 

Google Scholar 
Åkesson, S. & Helm, B. Endogenous programs and flexibility in bird migration. Front. Ecol. Evol. 8, 78 (2020).
Google Scholar 
Jiguet, F. et al. Desert crossing strategies of migrant songbirds vary between and within species. Sci. Rep. 9(1), 20248 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Senner, N. R., Morbey, Y. E. & Sandercock, B. K. Editorial: Flexibility in the migration strategies of animals. Front. Ecol. Evol. 8, 111 (2020).
Google Scholar 
Mellone, U., López-López, P., Limiñana, R., Piasevoli, G. & Urios, V. The trans-equatorial loop migration system of Eleonora’s falcon: Differences in migration patterns between age classes, regions and seasons. J. Avian Biol. 44, 417–426 (2013).
Google Scholar 
Chevallier, D. et al. Influence of weather conditions on the flight of migrating black storks. Proc. Biol. Sci. 277(1695), 2755–2764 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Verhelst, B., Jansen, J. & Vansteelant, W. South West Georgia: An important bottleneck for raptor migration during autumn. Ardea 99, 137–146 (2011).
Google Scholar 
Klaassen, R. H. G., Strandberg, R., Hake, M. & Alerstam, T. Flexibility in daily travel routines causes regional variation in bird migration speed. Behav. Ecol. Sociobiol. 62(9), 1427–1432 (2008).
Google Scholar 
Alerstam, T. Detours in bird migration. J. Theor. Biol. 209(3), 319–331 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Alerstam, T. & Hedenström, A. The development of bird migration theory. J. Avian Biol. 29(4), 343–369 (1998).
Google Scholar 
Liechti, F., Klaassen, M. & Bruderer, B. Predicting migratory flight altitudes by physiological migration models. Auk 117, 205–214 (2000).
Google Scholar 
Senner, N. R. et al. High-altitude shorebird migration in the absence of topographical barriers: Avoiding high air temperatures and searching for profitable winds. Proc. Biol. Sci. 2018, 285 (1881).
Google Scholar 
Norevik, G., Akesson, S., Andersson, A., Backman, J. & Hedenstrom, A. Flight altitude dynamics of migrating European nightjars across regions and seasons. J. Exp. Biol. 224(20), jeb242836 (2021).PubMed 
PubMed Central 

Google Scholar 
Hadjikyriakou, T. G., Nwankwo, E. C., Virani, M. Z. & Kirschel, A. N. G. Habitat availability influences migration speed, refueling patterns and seasonal flyways of a fly-and-forage migrant. Mov. Ecol. 8, 10 (2020).PubMed 
PubMed Central 

Google Scholar 
Strandberg, R., Klaassen, R. H. G., Olofsson, P. & Alerstam, T. Daily travel schedules of adult Eurasian Hobbies Falco subbuteo—Variability in flight hours and migration speed along the route. Ardea 97(3), 287–295 (2009).
Google Scholar 
Strandberg, R. & Alerstam, T. The strategy of fly-and-forage migration, illustrated for the osprey (Pandion haliaetus). Behav. Ecol. Sociobiol. 61(12), 1865–1875 (2007).
Google Scholar 
McKinnon, E. A. & Love, O. P. Ten years tracking the migrations of small landbirds: Lessons learned in the golden age of bio-logging. Auk 135(4), 834–856 (2018).
Google Scholar 
Backman, J. et al. Actogram analysis of free-flying migratory birds: New perspectives based on acceleration logging. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 203(6–7), 543–564 (2017).PubMed 
PubMed Central 

Google Scholar 
Evens, R., Beenaerts, N., Witters, N. & Artois, T. Study on the foraging behaviour of the European nightjar Caprimulgus europaeus reveals the need for a change in conservation strategy in Belgium. J. Avian Biol. 48(9), 1238–1245 (2017).
Google Scholar 
Evens, R. et al. Lunar synchronization of daily activity patterns in a crepuscular avian insectivore. Ecol. Evol. 10(14), 7106–7116 (2020).PubMed 
PubMed Central 

Google Scholar 
Liechti, F. et al. Miniaturized multi-sensor loggers provide new insight into year-round flight behaviour of small trans-Sahara avian migrants. Mov. Ecol. 6, 19 (2018).PubMed 
PubMed Central 

Google Scholar 
Liechti, F., Witvliet, W., Weber, R. & Bachler, E. First evidence of a 200-day non-stop flight in a bird. Nat. Commun. 4, 2554 (2013).ADS 
PubMed 

Google Scholar 
Dhanjal-Adams, K. L. PAMLr: Suite of functions for manipulating pressure, activity, magnetism and light data in R. (2020).Lisovski, S. et al. Light-level geolocator analyses: A user’s guide. J. Anim. Ecol. 89(1), 221–236 (2020).PubMed 

Google Scholar 
Lisovski, S. et al. Geolocation by light: Accuracy and precision affected by environmental factors. Methods Ecol. Evol. 3(3), 603–612 (2012).
Google Scholar 
Wotherspoon, S., Sumner, M., Lisovski, S. SGAT-Package: Solar/Satellite Geolocation for Animal Tracking. (2021). R package version 0.1.3. GitHub Repository.Bauer, R. RchivalTag: Analyzing Archival Tagging Data. R package version 0.1.2. (2020).Sjöberg, S. et al. Barometer logging reveals new dimensions of individual songbird migration. J. Avian Biol. 49(9), e01821 (2018).
Google Scholar 
Evens, R. et al. Migratory pathways, stopover zones and wintering destinations of Western European Nightjars Caprimulgus europaeus. Ibis 159(3), 680–686 (2017).
Google Scholar 
Becker, J. J. et al. Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar. Geodesy 32(4), 355–371 (2009).
Google Scholar 
Ricketts, T. H. Terrestrial Ecoregions of North America: A Conservation Assessment (Island Press, 1999).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience 51(11), 933–938 (2001).
Google Scholar 
QGIS-Development-Team: QGIS Geographic Information System. Open Source Geospatial Foundation (2021).Vansteelant, W. M. G., Gangoso, L., Bouten, W., Viana, D. S. & Figuerola, J. Adaptive drift and barrier-avoidance by a fly-forage migrant along a climate-driven flyway. Mov. Ecol. 9(1), 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).
Google Scholar 
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical Multi-Level/Mixed) Regression Models. R package version 0.3.3.0. (2020).Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.1. (2020).Akesson, S., Bianco, G. & Hedenstrom, A. Negotiating an ecological barrier: Crossing the Sahara in relation to winds by common swifts. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371(1704), 20150393 (2016).PubMed 
PubMed Central 

Google Scholar 
Strandberg, R., Klaassen, R. H. G., Hake, M., Olofsson, P. & Alerstam, T. Converging migration routes of Eurasian Hobbies Falco subbuteo crossing the African equatorial rain forest. Proc. R. Soc. B 276, 727–733 (2009).PubMed 

Google Scholar 
Rodriguez-Ruiz, J. et al. Disentangling migratory routes and wintering grounds of Iberian near-threatened European Rollers Coracias garrulus. PLoS ONE 9(12), e115615 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Vickery, J. A. et al. The decline of Afro-Palaearctic migrants and an assessment of potential causes. Ibis 156, 1–22 (2014).
Google Scholar 
Evens, R. et al. Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird. Sci. Rep. 8(1), 3008 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Conring, C. M., Brautigam, K., Grisham, B. A., Collins, D. P. & Conway, W. C. Identifying the migratory strategy of the Lower Colorado River Valley population of Greater Sandhill Cranes. Avian Conserv. Ecol. 14(1), 11 (2019).
Google Scholar 
Imlay, T. L., Saldanha, S. & Taylor, P. D. The fall migratory movements of Bank Swallows, Riparia riparia: Fly-and-forage migration?. Avian Conserv. Ecol. 15(1), 2 (2020).
Google Scholar 
Piersma, T. Hop, skip, or jump? Constraints on migration of arctic waders by feeding, fattening, and flight speed. Limosa 60, 185–194 (1987).
Google Scholar 
Warnock, N. Stopping vs. staging: The difference between a hop and a jump. J. Avian Biol. 41(6), 621–626 (2010).
Google Scholar 
Gomez, C. et al. Fuel loads acquired at a stopover site influence the pace of intercontinental migration in a boreal songbird. Sci. Rep. 7(1), 3405 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tottrup, A. P. et al. The annual cycle of a trans-equatorial Eurasian-African passerine migrant: different spatio-temporal strategies for autumn and spring migration. Proc. Biol. Sci. 279(1730), 1008–1016 (2012).PubMed 

Google Scholar 
Lisovski, S. et al. Inherent limits of light-level geolocation may lead to over-interpretation. Curr. Biol. 28(3), R99–R100 (2018).CAS 
PubMed 

Google Scholar 
Buler, J. J., Moore, F. R. & Woltmann, S. A multi-scale examination of stopover habitat use by birds. Ecology 88(7), 1789–1802 (2007).PubMed 

Google Scholar 
Loon, A. V. et al. Migratory stopover timing is predicted by breeding latitude, not habitat quality, in a long-distance migratory songbird. J. Ornithol. 158(3), 745–752 (2017).
Google Scholar 
Norevik, G. et al. Wind-associated detours promote seasonal migratory connectivity in a flapping flying long-distance avian migrant. J. Anim. Ecol. 89(2), 635–646 (2020).PubMed 

Google Scholar 
Norevik, G., Åkesson, S. & Hedenström, A. Migration strategies and annual space-use in an Afro-Palaearctic aerial insectivore—The European nightjar Caprimulgus europaeus. J. Avian Biol. 48(5), 738–747 (2017).
Google Scholar 
Cresswell, B. & Edwards, D. Geolocators reveal wintering areas of European Nightjar (Caprimulgus europaeus). Bird Study 60(1), 77–86 (2013).
Google Scholar 
Jacobsen, L. B. et al. Annual spatiotemporal migration schedules in three larger insectivorous birds: European nightjar, common swift and common cuckoo. Anim. Biotelem. 5(1), 1–11 (2017).
Google Scholar 
Liechti, F. & Bruderer, B. The relevance of wind for optimal migration theory. J. Avian Biol. 29(4), 561–568 (1998).
Google Scholar 
Schmaljohann, H., Bruderer, B. & Liechti, F. Sustained bird flights occur at temperatures far beyond expected limits. Anim. Behav. 76(4), 1133–1138 (2008).
Google Scholar 
Schmaljohann, H., Liechti, F. & Bruderer, B. Trans-Sahara migrants select flight altitudes to minimize energy costs rather than water loss. Behav. Ecol. Sociobiol. 63(11), 1609–1619 (2009).
Google Scholar 
Sjöberg, S. et al. Extreme altitudes during diurnal flights in a nocturnal songbird migrant. Science 372, 646–648 (2021).ADS 
PubMed 

Google Scholar 
Bruderer, B., Peter, D. & Korner-Nievergelt, F. Vertical distribution of bird migration between the Baltic Sea and the Sahara. J. Ornithol. 159(2), 315–336 (2018).
Google Scholar  More

This is the first study analyzing mandible shape in both mink species and, together with a previous study on their cranial shape38, it has revealed how small morphological differences in highly similar species can lead to substantial biomechanical differences (see breakdown below). As with cranial shape, mandible shape in minks is influenced by the complex interaction of size and sexual dimorphism both at the inter- and intraspecific levels. However, while in cranial shape both species had divergent shape allometries and parallel interspecific sexual allometries, the opposite was true for mandible shape.Differences in mandible shape between European and American mink were summarized by PC1 (Fig. 2, Fig. S1) and can be mainly related to muscle size and jaw biomechanics (i.e., in-levers and out-levers). The relatively taller and slightly wider coronoid process of European minks suggests a relatively larger temporalis muscle, while the anteriorly expanded masseteric fossa of American mink is indicative of a relatively larger masseter complex17,22,25. The relatively enlarged angular process of European mink provides a larger attachment area for the superficial masseter, with both mink species having a distinctive fossa on the lateral side of the angular process where this muscle attaches. This angular fossa is not present in European polecats (Gálvez-López, pers. obs.), part of the sister clade to European mink41.Regarding jaw biomechanics, the particular morphology of the American mink illustrates the compromise between maximizing both bite force efficiency and increased gape. The MAs for all masticatory muscles were higher in European mink due to their relatively longer in-levers (and also shorter out-levers if measured on PC1 configurations), with the exception of the MA of the deep masseter which was considerably higher in American mink (Table S2; Fig. 1D). These findings indicate that American mink exhibit features that allow them to produce larger forces at wide gape, which is particularly useful for holding and killing terrestrial vertebrates22,42. In agreement with this, a short moment arm of the superficial masseter (as observed in American mink) has been associated with increased gape in other mammals43. It is also worth noting that low MAs for the posterior temporalis and superficial masseter have also been associated with fish capture, as they indicate a relatively longer mandible relative to the muscle in-levers, which in turn allows the mouth to close faster when trying to catch elusive prey underwater21. In contrast, the characteristic features of European mink are indicative of stronger bites at the carnassials, which would allow them to cut through relatively tougher tissues and also to crush harder objects (e.g. shells of aquatic prey). Favoring carnassial over anterior bites could also be advantageous to feeding on fish. Mink catch fish underwater by grabbing them by the fins or back with their anterior teeth, and then dragging them to the surface where they are processed using cheek (carnassial) bites (Gálvez-López, pers. obs.).In our previous study on cranial shape in mink38, morphological differences between both species indicated relatively larger muscle volumes overall in the American mink (temporalis: more developed sagittal and nuchal crests, narrower braincase; masseter: longer and more curved zygomatic arches, larger infratemporal fossa), which suggested that bite forces both at the anterior dentition and at the carnassials were larger in this species. However, when combined with the MA results from this study on mandible shape, the relationship between muscle volume and force production becomes less straightforward. In the case of the European mink, the relatively smaller temporalis has a larger attachment site on the mandible (i.e., a broader and taller coronoid) and becomes more efficient (i.e., has higher MAs) due to the relatively longer in-lever. Similarly, in the American mink the effective length of the superficial masseter is increased by the marked curvature of the zygomatic arches, which mitigates the dorsal displacement of the angular process. However, the efficiency of the relatively larger temporalis is diminished by a smaller coronoid (i.e., reduced attachment area and shorter in-levers). The remaining differences in cranial morphology align with differences in mandible shape. Namely, the relatively broader zygomatic arches of the European mink support a strong superficial masseter, while the larger infratemporal fossae of American mink account for their enlarged deep masseter. On a final note, another finding common to both cranial and mandible shape was the relatively larger crushing dentition of American mink.Thus, after combining the results of cranial and mandible shape, it appears that, while the characteristic features of European mink indeed allow stronger carnassial bites, American mink present morphological indicators of both strong killing bites at wide gapes and powerful carnassial bites with a marked crushing component.The allometric effect on mandible size common to both species was represented by PC2 (Fig. 2, Fig. S3), which complements the common allometric trend recovered for both mink species in cranial shape38. The relative expansion of the masseteric fossa and the angular process with increasing size suggests that larger mink present a larger masseter complex. However, most of the allometric shape changes are related to muscle in-levers and out-levers. With increasing size, the length of both the out-lever at the anterior teeth and the in-levers of its related muscles (anterior temporalis, deep masseter) increases (Table S2), but the in-levers scale faster than the out-lever (Table S2). Thus, the mechanical advantages of both muscles at the anterior teeth also increase with size (Table S2), indicating that larger mink have markedly stronger and more efficient killing bites (particularly true for the deep masseter, which also becomes larger with size). This, together with their relatively larger anterior dentition (both in the mandible and the cranium) and taller anterior corpus, can be related to feeding on larger prey as size increases (i.e., stronger bites to perforate tougher skulls and hold onto stronger struggling prey, which would also require more robust teeth and corpora to resist the stresses placed on them). Similar features have been described for felids18, which also kill prey in this way22,32.Note, however, that one of the shape changes along PC2 does not accurately reflect the common allometric pattern: the lever arm of the superficial masseter, which slightly decreases along PC2 (Fig. 2; Table S2) and results in a decrease of the mechanical advantage of the superficial masseter and hence bite force at the carnassials along this axis (Table S2). In contrast, this lever arm significantly increases with size in the original specimens (Table S2), in agreement with the common allometric trend in cranial shape suggesting stronger bites at all teeth with increasing size38. A likely explanation for this phenomenon is that the common allometric trend is being confounded with interspecific shape differences, as American mink have significantly shorter superficial masseter in-levers than European mink (Fig. 1F; Table S2) yet their males are significantly larger than all other specimens (Fig. 1A). As mentioned above, the relative decrease in MA might reflect the trade-off between producing strong bite forces at the anterior teeth and having a wider gape to capture larger prey43, both of which are heavily supported by other morphological features in this common allometric trend.Sexual dimorphism in mandible shape was significant both within each species, and when grouping sexes from both species together. In her study of Palearctic mustelids, Romaniuk28 also found evidence for interspecific sexual dimorphism in mandible shape, but within species it was only significant for the Siberian weasel (Mustela sibirica). The different results for the European mink in that study might be related to its smaller sample. Note, however, that Hernández-Romero et al.40 did not find evidence for sexual dimorphism in mandible shape within Neotropical otters (Lontra longicaudis) even though their sample sizes were equivalent to those in the present study.Overall, the results of the present study reveal that mandible shape differences between males and females are the consequence of a complex interaction between sex and size at both inter- and intraspecific levels. For instance, each sex in each species has a mandible shape significantly different from each other (Table 1), but allometric shape changes within each of them are similar (except maybe female American mink; Fig. S5A). Additionally, while trajectory analysis indicates that the degree of sexual dimorphism in mandible shape is similar within each species, the specific differences between sexes are different in each species (i.e., same magnitude, different orientation; Table 2, Fig. S5B). While at the interspecific level, male and female mandible shapes change differently with increasing size even though the change per unit size is similar in both sexes (Tables 1, 2; Fig. S5C,D), and some of the allometric changes are common to both species and sexes (see section above; PC2 in Fig. 2). Finally, another set of shape changes related to sexual dimorphism and common to both species are those related to sexual dimorphism in mandible size, illustrated by PC3 (Figs. 2, Fig. S4).Shape changes related to sexual dimorphism in size are represented along PC3 and can be related to an overall increase in bite force (i.e., at all teeth), as higher scores on this axis correspond to increased muscle attachment areas and longer in-levers (taller and wider coronoid, anteriorly expanded masseteric fossa, ventrally expanded angular process), shorter out-levers (particularly at the anterior teeth), and a more robust corpus (dorsoventrally and mediolaterally expanded). This interpretation of shape changes along PC3 is supported by the results of the ANOVAs on the lever arms and MAs measured on the PC3 configurations (Table S2). These variables were only related to sex and size, with female mink having longer out-levers and male mink presenting longer in-levers and higher MAs, while out-levers decreased with increasing size and in-levers and MAs increased in both sexes (no significant interaction between sex and size indicates parallel allometric trajectories in both sexes). This trend is consistent with the common sexual allometry described for cranial shape, which suggested that larger males have bigger masticatory muscles than smaller females and thus produce higher bite forces38. Additionally, even though the relative length of the toothrow decreases, the size of the canine markedly increases and there is no change in molar size or the relative proportions in its shearing and crushing regions. Although this might be interpreted as reinforcing the canines to cope with killing larger prey while maintaining an otherwise similar dietary regime20, it is worth noting that larger canines have been long described as a feature of sexual size dimorphism in mustelids19,44,45.In terms of interspecific differences in sexual allometry, with increasing size the following shape changes were observed in females but not in males (Fig. S5C): a dorsoventrally more robust corpus, a ventral expansion of the angular process, longer in-levers for all masticatory muscles, larger incisors, and an increase in the shearing portion of m1 relative to the crushing portion. Most of these shape changes are similar to those described for PC3, which suggests that the female interspecific allometry bridges the bite force gap caused by sexual dimorphism in size. The changes to the female dentition suggest a shift in diet from crushing tough food items (e.g. aquatic invertebrates) towards slicing meat, which makes sense since these changes occur simultaneously with the common allometric trend (related to improved capabilities for killing larger vertebrate prey). However, as noted earlier, the increased shearing component is also advantageous for a piscivorous diet. Shape changes in male mandibles not observed in females seem to emphasize the common allometric trend (i.e., stronger killing bite at larger gapes) (Fig. S5D): a wider coronoid process for more muscle attachment, a dorsally displaced angular process to allow wider gapes, and mediolateral expansion of the corpus to increase its strength. Regarding their dentition, the opposite trend to females was observed (i.e., slightly smaller anterior teeth and a longer crushing molar portion), suggesting a larger durophagous component in the diet of larger males.As expected, variation in mandible shape could be linked to potential dietary differences between European and American mink, and also between sexes. In summary, the results of the present study show that:

American mink are better equipped for preying on terrestrial vertebrates, as they can achieve relatively larger gapes and their mandibles are able to produce larger forces during the killing bite (i.e., at the anterior teeth and with an open mouth).

European mink, on the other hand, can produce relatively stronger bites at the carnassials, suggesting that they rely more on tougher prey and/or fish.

Regardless of species and sex, morphological features in larger mink demonstrate increased capabilities for feeding on larger terrestrial prey (stronger killing bites and more robust anterior teeth and corpora to resist the stresses caused by struggling prey).

Due to their larger size, male mink of both species have stronger bites than females at both the anterior teeth and the carnassials. However, with increasing size, females bridge the gap by developing relatively stronger bites overall while shifting their diet from tougher or harder prey (probably aquatic invertebrates) towards less mechanically demanding food items (e.g. terrestrial vertebrates and/or fish). In contrast, increasing size in males leads to even more specialization towards feeding on larger terrestrial prey while tough items become more relevant in their diets (probably crushing bones of small prey).

These findings confirm our original predictions based on previous results on cranial shape differences, but do they agree with observed dietary preferences in minks? Diet studies in American mink are numerous, and provide a wide picture of seasonal and regional variation8,11 as well as intraspecific dietary competition6,7,12. However, studies on European mink diet are scarcer9,14, particularly those comparing the sexes13. Additionally, a few studies have compared diets of sympatric European and American mink10,15. All these studies can be summarized as: A, male American mink favor medium-sized mammals and birds usually heavier than themselves; B, female American mink favor aquatic prey, but are displaced towards small mammals and birds when seasonal changes in prey availability shift the males’ diet towards aquatic prey; C, European mink favor aquatic prey, particularly fish and crayfish; but D, they are displaced towards amphibians and small mammals when sympatric with American mink. From these, our results on mandible shape variation support A and somewhat B and C, but provide no information on the interspecific competition scenario or on potential seasonal or local dietary differences. Additionally, there is no information on size-related dietary changes in either species that could validate our findings on sexual allometry in mandible shape. Thus, while mandible shape is very useful for identifying broad dietary indicators even between highly similar species, its ability to provide accurate information on their potential prey is limited.As a final note on mink diets, our previous study on cranial shape38, suggested a gradient in muscle force (and potential dietary range) from female European mink to male American mink. Based on those results and studies on social interactions between and within species35,46, we hypothesized that competition between both mink species could be displacing female European mink towards narrower and poorer diets, which could affect their survivability and ability to successfully reproduce. Fortunately, the results of the present study not only propose that there might be less overlap in diets between species and sexes than suggested by dietary studies7,10,13,15, but also indicate that dietary competition seems to be higher for small terrestrial vertebrates, not aquatic prey (on which female European mink are particularly well equipped to feed). More

Lees, A. C., Attwood, S., Barlow, J. & Phalan, B. Biodiversity scientists must fight the creeping rise of extinction denial. Nat. Ecol. Evol. 4, 1440–1443 (2020).PubMed 

Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar 
Driscoll, D. A. et al. A biodiversity-crisis hierarchy to evaluate and refine conservation indicators. Nat. Ecol. Evol. 2, 775–781 (2018).PubMed 

Google Scholar 
Jackson, J. B. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
PubMed 

Google Scholar 
McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 6219 (2015).
Google Scholar 
Sala, E. & Knowlton, N. Global marine biodiversity trends. Annu. Rev. Environ. Resour. 31, 93–122 (2006).
Google Scholar 
Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Beaumont, N. et al. Identification, definition and quantification of goods and services provided by marine biodiversity: Implications for the ecosystem approach. Mar. Pollut. Bull. 54, 253–265 (2007).CAS 
PubMed 

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Turpie, J. K. The existence value of biodiversity in South Africa: How interest, experience, knowledge, income and perceived level of threat influence local willingness to pay. Ecol. Econ. 46, 199–216 (2003).
Google Scholar 
Ruiz-Frau, A., Hinz, H., Edwards-Jones, G. & Kaiser, M. Spatially explicit economic assessment of cultural ecosystem services: Non-extractive recreational uses of the coastal environment related to marine biodiversity. Mar. Policy 38, 90–98 (2013).
Google Scholar 
Thrush, S. F., Gray, J. S., Hewitt, J. E. & Ugland, K. I. Predicting the effects of habitat homogenization on marine biodiversity. Ecol. Appl. 16, 1636–1642 (2006).PubMed 

Google Scholar 
Gillies, C. L. et al. Australian shellfish ecosystems: Past distribution, current status and future direction. PLoS ONE 13, e0190914 (2018).PubMed 
PubMed Central 

Google Scholar 
Commito, J. A., Como, S., Grupe, B. M. & Dow, W. E. Species diversity in the soft-bottom intertidal zone: Biogenic structure, sediment, and macrofauna across mussel bed spatial scales. J. Exp. Mar. Biol. Ecol. 366, 70–81 (2008).
Google Scholar 
Tokeshi, M. Species Coexistence: Ecological and Evolutionary Perspectives (Wiley, Hoboken, 2009).
Google Scholar 
Paul, L. J. A history of the Firth of Thames dredge fishrey for mussels: Use and abuse of a coastal resource. Report No. 94, (Wellington, New Zealand, 2012).Enderlein, P. & Wahl, M. Dominance of blue mussels versus consumer-mediated enhancement of benthic diversity. J. Sea Res. 51, 145–155 (2004).ADS 

Google Scholar 
Lejart, M. & Hily, C. Differential response of benthic macrofauna to the formation of novel oyster reefs (Crassostrea gigas, Thunberg) on soft and rocky substrate in the intertidal of the Bay of Brest, France. J. Sea Res. 65, 84–93 (2011).ADS 

Google Scholar 
Norling, P. & Kautsky, N. Patches of the mussel Mytilus sp. are islands of high biodiversity in subtidal sediment habitats in the Baltic Sea. Aquat. Biol. 4, 75–87 (2008).
Google Scholar 
Norling, P., Lindegarth, M., Lindegarth, S. & Strand, Å. Effects of live and post-mortem shell structures of invasive Pacific oysters and native blue mussels on macrofauna and fish. Mar. Ecol. Prog. Ser. 518, 123–138 (2015).ADS 

Google Scholar 
McLeod, I., Parsons, D., Morrison, M., Van Dijken, S. & Taylor, R. Mussel reefs on soft sediments: A severely reduced but important habitat for macroinvertebrates and fishes in New Zealand. N. Z. J. Mar. Freshw. Res. 48, 48–59 (2014).CAS 

Google Scholar 
Seitz, R. D., Wennhage, H., Bergström, U., Lipcius, R. N. & Ysebaert, T. Ecological value of coastal habitats for commercially and ecologically important species. ICES J. Mar. Sci. 71, 648–665 (2014).
Google Scholar 
zu Ermgassen, P. S., Grabowski, J. H., Gair, J. R. & Powers, S. P. Quantifying fish and mobile invertebrate production from a threatened nursery habitat. J. Appl. Ecol. 53, 596–606 (2016).
Google Scholar 
Grabowski, J. H. The influence of trophic interactions, habitat complexity, and landscape setting on community dynamics and restoration of oyster reefs. Ph.D., The University of North Carolina at Chapel Hill (2002).Harding, J. M., Allen, D. M., Haffey, E. R. & Hoffman, K. M. Site fidelity of oyster reef blennies and gobies in saltmarsh tidal creeks. Estuaries Coasts 43, 409–423 (2020).CAS 

Google Scholar 
Parsons, D. et al. Snapper (Chrysophrys auratus): A review of life history and key vulnerabilities in New Zealand. N. Z. J. Mar. Freshw. Res. 48, 256–283 (2014).
Google Scholar 
Callier, M. D., Richard, M., McKindsey, C. W., Archambault, P. & Desrosiers, G. Responses of benthic macrofauna and biogeochemical fluxes to various levels of mussel biodeposition: An in situ “benthocosm” experiment. Mar. Pollut. Bull. 58, 1544–1553. https://doi.org/10.1016/j.marpolbul.2009.05.010 (2009).CAS 
Article 
PubMed 

Google Scholar 
Ysebaert, T., Hart, M. & Herman, P. M. Impacts of bottom and suspended cultures of mussels Mytilus spp. on the surrounding sedimentary environment and macrobenthic biodiversity. Helgol. Mar. Res. 63, 59–74 (2009).ADS 

Google Scholar 
Sea, M. A., Thrush, S. F. & Hillman, J. R. Environmental predictors of sediment denitrification rates within restored green-lipped mussel (Perna canaliculus) beds. Mar. Ecol. Prog. Ser. 667, 1–13 (2021).ADS 
CAS 

Google Scholar 
Hillman, J. R., O’Meara, T. A., Lohrer, A. M., & Thrush, S. F. Influence of restored mussel reefs on denitrification in
marine sediments. J. Sea Res. 175, 102099 (2021).
Google Scholar 
Bacheler, N. M. et al. Comparison of trap and underwater video gears for indexing reef fish presence and abundance in the southeast United States. Fish. Res. 143, 81–88 (2013).
Google Scholar 
Wells, R. D., Boswell, K. M., Cowan, J. H. Jr. & Patterson, W. F. III. Size selectivity of sampling gears targeting red snapper in the northern Gulf of Mexico. Fish. Res. 89, 294–299 (2008).
Google Scholar 
Emslie, M. J., Cheal, A. J., MacNeil, M. A., Miller, I. R. & Sweatman, H. P. Reef fish communities are spooked by scuba surveys and may take hours to recover. PeerJ 6, e4886 (2018).PubMed 
PubMed Central 

Google Scholar 
Piggott, C. V., Depczynski, M., Gagliano, M. & Langlois, T. J. Remote video methods for studying juvenile fish populations in challenging environments. J. Exp. Mar. Biol. Ecol. 532, 151454 (2020).
Google Scholar 
Dean, W. E. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition: Comparison with other methods. J. Sediment. Res. 44, 242–248 (1974).CAS 

Google Scholar 
Lorenzen, C. J. Determination of chlorophyll and pheo-pigments: Spectrophotometric equations. Limnol. Oceanogr. 12, 343–346 (1967).ADS 
CAS 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
Google Scholar 
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).
Google Scholar 
Clarke, K. R. & Gorley, R. N. PRIMER v7: User Manual/Tutorial (2015).Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to software and statistical methods (2008).R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).Saier, B. Subtidal and intertidal mussel beds (Mytilus edulis L.) in the Wadden Sea: Diversity differences of associated epifauna. Helgol. Mar. Res. 56, 44–50 (2002).ADS 

Google Scholar 
Peterson, C. H., Grabowski, J. H. & Powers, S. P. Estimated enhancement of fish production resulting from restoring oyster reef habitat: Quantitative valuation. Mar. Ecol. Prog. Ser. 264, 249–264 (2003).ADS 

Google Scholar 
Gutiérrez, J. L., Jones, C. G., Strayer, D. L. & Iribarne, O. O. Mollusks as ecosystem engineers: The role of shell production in aquatic habitats. Oikos 101, 79–90 (2003).
Google Scholar 
Norkko, A., Hewitt, J. E., Thrush, S. F. & Funnell, T. Benthic-pelagic coupling and suspension-feeding bivalves: Linking site-specific sediment flux and biodeposition to benthic community structure. Limnol. Oceanogr. 46, 2067–2072 (2001).ADS 

Google Scholar 
Russell, B. The food and feeding habits of rocky reef fish of north-eastern New Zealand. N. Z. J. Mar. Freshw. Res. 17, 121–145 (1983).
Google Scholar 
Gillies, C., Creighton, C. & McLeod, I. Shellfish reef habitats: A synopsis to underpin the repair and conservation of Australia’s environmentally, socially and economically important bays and estuaries. Report to the National Environmental Science Programme, Marine Biodiversity Hub, Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER) Publication, James Cook University, Townsville, Qld, Australia (2015).Lenihan, H. S. et al. Cascading of habitat degradation: Oyster reefs invaded by refugee fishes escaping stress. Ecol. Appl. 11, 764–782 (2001).
Google Scholar 
Connell, S. & Jones, G. The influence of habitat complexity on postrecruitment processes in a temperate reef fish population. J. Exp. Mar. Biol. Ecol. 151, 271–294 (1991).
Google Scholar 
Usmar, N. Ontogeny and Ecology of Snapper (Pagrus auratus) in an estuary, the Mahurangi Harbour (University of Auckland, 2009).
Google Scholar 
Willis, T. J. & Anderson, M. J. Structure of cryptic reef fish assemblages: Relationships with habitat characteristics and predator density. Mar. Ecol. Prog. Ser. 257, 209–221 (2003).ADS 

Google Scholar 
Thompson, S. Homing in a territorial reef fish. Copeia 1983, 832–834 (1983).
Google Scholar 
Thrush, S. F., Schultz, D., Hewitt, J. E. & Talley, D. Habitat structure in soft-sediment environments and abundance of juvenile snapper Pagrus auratus. Mar. Ecol. Prog. Ser. 245, 273–280 (2002).ADS 

Google Scholar 
Pickering, H. & Whitmarsh, D. Artificial reefs and fisheries exploitation: A review of the ‘attraction versus production’debate, the influence of design and its significance for policy. Fish. Res. 31, 39–59 (1997).
Google Scholar 
Karp, M. A., Seitz, R. D. & Fabrizio, M. C. Faunal communities on restored oyster reefs: Effects of habitat complexity and environmental conditions. Mar. Ecol. Prog. Ser. 590, 35–51 (2018).ADS 

Google Scholar 
Hanke, M. H., Posey, M. H. & Alphin, T. D. The effects of intertidal oyster reef habitat characteristics on faunal utilization. Mar. Ecol. Prog. Ser. 581, 57–70 (2017).ADS 

Google Scholar 
Cranfield, H., Rowden, A., Smith, D., Gordon, D. & Michael, K. Macrofaunal assemblages of benthic habitat of different complexity and the proposition of a model of biogenic reef habitat regeneration in Foveaux Strait, New Zealand. J. Sea Res. 52, 109–125 (2004).ADS 

Google Scholar 
Norling, P. & Kautsky, N. Structural and functional effects of Mytilus edulis on diversity of associated species and ecosystem functioning. Mar. Ecol. Prog. Ser. 351, 163–175 (2007).ADS 

Google Scholar 
Jaunatre, R. et al. New synthetic indicators to assess community resilience and restoration success. Ecol. Indicators 29, 468–477 (2013).
Google Scholar 
O’Meara, T. A., Hewitt, J. E., Thrush, S. F., Douglas, E. J. & Lohrer, A. M. Denitrification and the role of macrofauna across estuarine gradients in nutrient and sediment loading. Estuaries Coasts 43, 1394–1405. https://doi.org/10.1007/s12237-020-00728-x (2020).CAS 
Article 

Google Scholar 
McCann, L. D. Oligochaete influence on settlement, growth and reproduction in a surface-deposit-feeding polychaete. J. Exp. Mar. Biol. Ecol. 131, 233–253 (1989).
Google Scholar 
Hope, J. A., Paterson, D. M. & Thrush, S. F. The role of microphytobenthos in soft-sediment ecological networks and their contribution to the delivery of multiple ecosystem services. J. Ecol. 108, 815–830 (2020).
Google Scholar 
Christianen, M. J. et al. Benthic primary producers are key to sustain the Wadden Sea food web: Stable carbon isotope analysis at landscape scale. Ecology 98, 1498–1512 (2017).CAS 
PubMed 

Google Scholar 
Commito, J. A. & Dankers, N. M. Dynamics of spatial and temporal complexity in European and North American soft-bottom mussel beds. In Ecological Comparisons of Sedimentary Shores, 39–59 (Springer, Berlin, 2001).Arribas, L. P., Donnarumma, L., Palomo, M. G. & Scrosati, R. A. Intertidal mussels as ecosystem engineers: Their associated invertebrate biodiversity under contrasting wave exposures. Mar. Biodivers. 44, 203–211 (2014).
Google Scholar 
Walles, B., Salvador de Paiva, J., van Prooijen, B. C., Ysebaert, T. & Smaal, A. C. The ecosystem engineer Crassostrea gigas affects tidal flat morphology beyond the boundary of their reef structures. Estuaries Coasts 38, 941–950 (2015).
Google Scholar 
Tsuchiya, M. & Nishihira, M. Islands of Mytilus edulis as a habitat for small intertidal animals: Effect of Mytilus age structure on the species composition of the associated fauna and community organization. Mar. Ecol. Prog. Ser. 31, 171–178 (1986).ADS 

Google Scholar 
Craeymeersch, J. A. & Jansen, H. M. Bivalve assemblages as hotspots for biodiversity. In Goods and Services of Marine Bivalves, 275–294 (Springer, Cham, 2019).Buschbaum, C. et al. Mytilid mussels: Global habitat engineers in coastal sediments. Helgol. Mar. Res. 63, 47–58 (2009).ADS 

Google Scholar  More