References
Baas, J., Augustine, S., Marques, G.M. & Dorne, J.-L. (2018). Dynamic energy budget models in ecological risk assessment: From principles to applications. Science of The Total Environment , 628–629, 249–260.
Baruah, G., Clements, C.F., Guillaume, F. & Ozgul, A. (2019). When Do Shifts in Trait Dynamics Precede Population Declines? The American Naturalist , 193, 633–644.
Berger-Tal, O., Polak, T., Oron, A., Lubin, Y., Kotler, B.P. & Saltz, D. (2011). Integrating animal behavior and conservation biology: a conceptual framework. Behavioral Ecology , 22, 236–239.
Bjorndal, K.A., Bolten, A.B., Chaloupka, M., Saba, V.S., Bellini, C., Marcovaldi, M.A.G., et al. (2017). Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic.Global Change Biology , 23, 4556–4568.
Boggs, C.L. & Ross, C.L. (1993). The Effect of Adult Food Limitation on Life History Traits in Speyeria Mormonia (Lepidoptera: Nymphalidae).Ecology , 74, 433–441.
Burant, J.B., Park, C., Betini, G.S. & Norris, D.R. (2021). Early warning indicators of population collapse in a seasonal environment.Journal of Animal Ecology , 90, 1538–1549.
Bury, T.M., Sujith, R.I., Pavithran, I., Scheffer, M., Lenton, T.M., Anand, M., et al. (2021). Deep learning for early warning signals of tipping points. Proc Natl Acad Sci USA , 118, e2106140118.
Carter, M.I.D., Russell, D.J.F., Embling, C.B., Blight, C.J., Thompson, D., Hosegood, P.J., et al. (2017). Intrinsic and extrinsic factors drive ontogeny of early-life at-sea behaviour in a marine top predator. Sci Rep , 7, 15505.
Ceballos, G., Ehrlich, P.R., Barnosky, A.D., García, A., Pringle, R.M. & Palmer, T.M. (2015). Accelerated modern human–induced species losses: Entering the sixth mass extinction. Science Advances , 1, e1400253.
Chabot, D., Craik, S.R. & Bird, D.M. (2015). Population Census of a Large Common Tern Colony with a Small Unmanned Aircraft. PLOS ONE , 10, e0122588.
Chimienti, M., Beest, F.M. van, Beumer, L.T., Desforges, J.-P., Hansen, L.H., Stelvig, M., et al. (2021). Quantifying behavior and life-history events of an Arctic ungulate from year-long continuous accelerometer data. Ecosphere , 12, e03565.
Christiansen, F., Dujon, A.M., Sprogis, K.R., Arnould, J.P.Y. & Bejder, L. (2016). Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere , 7, e01468.
Clements, C.F., Blanchard, J.L., Nash, K.L., Hindell, M.A. & Ozgul, A. (2017). Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat Ecol Evol , 1, 0188.
Clements, C.F., Blanchard, J.L., Nash, K.L., Hindell, M.A. & Ozgul, A. (2018). Reply to ‘Whaling catch data are not reliable for analyses of body size shifts.’ Nat Ecol Evol , 2, 757–758.
Clements, C.F. & Ozgul, A. (2016). Including trait-based early warning signals helps predict population collapse. Nat Commun , 7, 10984.
Clements, C.F. & Ozgul, A. (2018). Indicators of transitions in biological systems. Ecol Lett , 21, 905–919.
Couvillon, M.J., Schürch, R. & Ratnieks, F.L.W. (2014). Waggle Dance Distances as Integrative Indicators of Seasonal Foraging Challenges.PLOS ONE , 9, e93495.
Deichmann, J.L., Hernández-Serna, A., Delgado C., J.A., Campos-Cerqueira, M. & Aide, T.M. (2017). Soundscape analysis and acoustic monitoring document impacts of natural gas exploration on biodiversity in a tropical forest. Ecological Indicators , 74, 39–48.
Dereniowska, M. & Meinard, Y. (2021). The unknownness of biodiversity: Its value and ethical significance for conservation action.Biological Conservation , 260, 109199.
Desjonquères, C., Gifford, T. & Linke, S. (2020). Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments. Freshwater Biology , 65, 7–19.
Durand, J., Legrand, A., Tort, M., Thiney, A., Michniewicz, R.J., Coulon, A., et al. (2012). Effects of geographic isolation on anti-snakes responses in the wall lizard, Podarcis muralis. Amphib Reptilia , 33, 199–206.
Eshun-Wilson, F., Wolf, R., Andersen, T., Hessen, D.O. & Sperfeld, E. (2020). UV radiation affects antipredatory defense traits in Daphnia pulex. Ecology and Evolution , 10, 14082–14097.
Fagan, W.F. & Holmes, E.E. (2005). Quantifying the extinction vortex.Ecol Letters , 0, 051109031307004.
Fayet, A.L., Clucas, G.V., Anker‐Nilssen, T., Syposz, M. & Hansen, E.S. (2021). Local prey shortages drive foraging costs and breeding success in a declining seabird, the Atlantic puffin. J Anim Ecol , 1365-2656.13442.
Fleming, A.H., Clark, C.T., Calambokidis, J. & Barlow, J. (2016). Humpback whale diets respond to variance in ocean climate and ecosystem conditions in the California Current. Global Change Biology , 22, 1214–1224.
Fox, R.J., Donelson, J.M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J.D. (2019). Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philosophical Transactions of the Royal Society B: Biological Sciences , 374, 20180174.
Gardner, J.L., Peters, A., Kearney, M.R., Joseph, L. & Heinsohn, R. (2011). Declining body size: a third universal response to warming?Trends in Ecology & Evolution , 26, 285–291.
Gardner, T.A., Barlow, J., Araujo, I.S., Ávila-Pires, T.C., Bonaldo, A.B., Costa, J.E., et al. (2008). The cost-effectiveness of biodiversity surveys in tropical forests. Ecology Letters , 11, 139–150.
Gauzens, B., Rosenbaum, B., Kalinkat, G., Boy, T., Jochum, M., Kortsch, S., et al. (2021). Adaptive foraging behaviour increases vulnerability to climate change. bioRxiv , 2021.05.05.442768.
Gibb, R., Browning, E., Glover-Kapfer, P. & Jones, K.E. (2019). Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods in Ecology and Evolution , 10, 169–185.
Greggor, A.L., Berger-Tal, O., Blumstein, D.T., Angeloni, L., Bessa-Gomes, C., Blackwell, B.F., et al. (2016). Research Priorities from Animal Behaviour for Maximising Conservation Progress.Trends in Ecology & Evolution , 31, 953–964.
Guo, Q., Jin, S., Li, M., Yang, Q., Xu, K., Ju, Y., et al.(2020). Application of deep learning in ecological resource research: Theories, methods, and challenges. Sci. China Earth Sci. , 63, 1457–1474.
Hamilton, C.D., Lydersen, C., Ims, R.A. & Kovacs, K.M. (2015). Predictions replaced by facts: a keystone species’ behavioural responses to declining arctic sea-ice. Biol. Lett. , 11, 20150803.
Hansson, B. & Westerberg, L. (2002). On the correlation between heterozygosity and fitness in natural populations. Molecular Ecology , 11, 2467–2474.
Hertel, A.G., Leclerc, M., Warren, D., Pelletier, F., Zedrosser, A. & Mueller, T. (2019). Don’t poke the bear: using tracking data to quantify behavioural syndromes in elusive wildlife. Animal Behaviour , 147, 91–104.
Holmes, E., E., Ward, E., J. & Wills, K. (2012). MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data.The R Journal , 4, 11.
Holt, R.E. & Jørgensen, C. (2015). Climate change in fish: effects of respiratory constraints on optimal life history and behaviour.Biology Letters , 11, 20141032.
Ingram, D.J., Ferreira, G.B., Jones, K.E. & Mace, G.M. (2021). Targeting Conservation Actions at Species Threat Response Thresholds.Trends in Ecology & Evolution , 36, 216–226.
Jones, F.M., Arteta, C., Zisserman, A., Lempitsky, V., Lintott, C.J. & Hart, T. (2020). Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics. Sci Data , 7, 102.
Keith, S.A., Baird, A.H., Hobbs, J.-P.A., Woolsey, E.S., Hoey, A.S., Fadli, N., et al. (2018). Synchronous behavioural shifts in reef fishes linked to mass coral bleaching. Nature Clim Change , 8, 986–991.
Kershaw, J.L., Ramp, C.A., Sears, R., Plourde, S., Brosset, P., Miller, P.J.O., et al. (2021). Declining reproductive success in the Gulf of St. Lawrence’s humpback whales (Megaptera novaeangliae) reflects ecosystem shifts on their feeding grounds. Global Change Biology , 27, 1027–1041.
Krause, D.J., Hinke, J.T., Perryman, W.L., Goebel, M.E. & LeRoi, D.J. (2017). An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLOS ONE , 12, e0187465.
Kunc, H.P. & Schmidt, R. (2021). Species sensitivities to a global pollutant: A meta-analysis on acoustic signals in response to anthropogenic noise. Global Change Biology , 27, 675–688.
Lai, G., Chang, W.-C., Yang, Y. & Liu, H. (2018). Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . Presented at the SIGIR ’18: The 41st International ACM SIGIR conference on research and development in Information Retrieval, ACM, Ann Arbor MI USA, pp. 95–104.
Lara-Benítez, P., Carranza-García, M. & Riquelme, J.C. (2021). An Experimental Review on Deep Learning Architectures for Time Series Forecasting. Int. J. Neur. Syst. , 31, 2130001.
Leary, R.F. & Allendorf, F.W. (1989). FluctuatinAgsymmetarsyanIndicator ofStressI:mplicatiofnosr ConservatiBoniology, 4, 4.
van de Leemput, I.A., Dakos, V., Scheffer, M. & van Nes, E.H. (2018). Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience. Ecosystems , 21, 141–152.
Leibold, M. & Tessier, A.J. (1991). Contrasting patterns of body size for Daphnia species that segregate by habitat. Oecologia , 86, 342–348.
Lenda, M., Witek, M., Skórka, P., Moroń, D. & Woyciechowski, M. (2013). Invasive alien plants affect grassland ant communities, colony size and foraging behaviour. Biol Invasions , 15, 2403–2414.
Lika, K., Kearney, M.R., Freitas, V., van der Veer, H.W., van der Meer, J., Wijsman, J.W.M., et al. (2011). The “covariation method” for estimating the parameters of the standard Dynamic Energy Budget model I: Philosophy and approach. Journal of Sea Research , The AquaDEB project (phase II): what we’ve learned from applying the Dynamic Energy Budget theory on aquatic organisms, 66, 270–277.
Linchant, J., Lisein, J., Semeki, J., Lejeune, P. & Vermeulen, C. (2015). Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review , 45, 239–252.
Lomolino, M.V. & Perault, D.R. (2007). Body Size Variation of Mammals in a Fragmented, Temperate Rainforest. Conservation Biology , 21, 1059–1069.
Lopez-Ricaurte, L., Vansteelant, W.M.G., Hernández-Pliego, J., García-Silveira, D., Bermejo-Bermejo, A., Casado, S., et al.(2021). Barrier crossings and winds shape daily travel schedules and speeds of a flight generalist. Sci Rep , 11, 1–12.
Maron, M., Mitchell, M.G.E., Runting, R.K., Rhodes, J.R., Mace, G.M., Keith, D.A., et al. (2017). Towards a Threat Assessment Framework for Ecosystem Services. Trends in Ecology & Evolution , 32, 240–248.
McClanahan, T.R., </b><b>, Darling, E.S., Maina, J.M., Muthiga, N.A., D’agata, S., et al.(2020). Highly variable taxa-specific coral bleaching responses to thermal stresses. Marine Ecology Progress Series , 648, 135–151.
McMahan, M.D. & Grabowski, J.H. (2019). Nonconsumptive effects of a range-expanding predator on juvenile lobster (Homarus americanus) population dynamics. Ecosphere , 10, e02867.
Measey, G.J., Stevenson, B.C., Scott, T., Altwegg, R. & Borchers, D.L. (2017). Counting chirps: acoustic monitoring of cryptic frogs.Journal of Applied Ecology , 54, 894–902.
Miller, B.S. & Miller, E.J. (2018). The seasonal occupancy and diel behaviour of Antarctic sperm whales revealed by acoustic monitoring.Sci Rep , 8, 5429.
Norouzzadeh, M.S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M.S., Packer, C., et al. (2018). Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.PNAS , 115, E5716–E5725.
Olden, J.D., Hogan, Z.S. & Zanden, M.J.V. (2007). Small fish, big fish, red fish, blue fish: size-biased extinction risk of the world’s freshwater and marine fishes. Global Ecology and Biogeography , 16, 694–701.
Oram, E. & Spitze, K. (2013). Depth selection by Daphnia pulex in response to Chaoborus kairomone. Freshwater Biology , 58, 409–415.
Ortega, Z., Mencía, A. & Pérez-Mellado, V. (2017). Rapid acquisition of antipredatory responses to new predators by an insular lizard.Behav Ecol Sociobiol , 71, 1.
Patrick, S.C., Martin, J.G.A., Ummenhofer, C.C., Corbeau, A. & Weimerskirch, H. (2021). Albatrosses respond adaptively to climate variability by changing variance in a foraging trait. Global Change Biology , 27, 4564–4574.
Pellegrini, A.Y., Romeu, B., Ingram, S.N. & Daura-Jorge, F.G. (2021). Boat disturbance affects the acoustic behaviour of dolphins engaged in a rare foraging cooperation with fishers. Animal Conservation , 24, 613–625.
Pierce, C.L. (1988). Predator avoidance, microhabitat shift, and risk-sensitive foraging in larval dragonflies. Oecologia , 77, 81–90.
Pigeon, G., Ezard, T.H.G., Festa-Bianchet, M., Coltman, D.W. & Pelletier, F. (2017). Fluctuating effects of genetic and plastic changes in body mass on population dynamics in a large herbivore.Ecology , 98, 2456–2467.
Pimm, S.L., Alibhai, S., Bergl, R., Dehgan, A., Giri, C., Jewell, Z.,et al. (2015). Emerging Technologies to Conserve Biodiversity.Trends in Ecology & Evolution , 30, 685–696.
Portugal, S.J. & White, C.R. (2018). Miniaturization of biologgers is not alleviating the 5% rule. Methods in Ecology and Evolution , 9, 1662–1666.
Rebecchi, L., Boschetti, C. & Nelson, D.R. (2020). Extreme-tolerance mechanisms in meiofaunal organisms: a case study with tardigrades, rotifers and nematodes. Hydrobiologia , 847, 2779–2799.
Reside, A.E., Critchell, K., Crayn, D.M., Goosem, M., Goosem, S., Hoskin, C.J., et al. (2019). Beyond the model: expert knowledge improves predictions of species’ fates under climate change.Ecological Applications , 29, e01824.
Seber, G.A.F. & Schofield, M.R. (2019). Capture-Recapture: Parameter Estimation for Open Animal Populations . Statistics for Biology and Health. Springer International Publishing, Cham.
Sequeira, A.M.M., O’Toole, M., Keates, T.R., McDonnell, L.H., Braun, C.D., Hoenner, X., et al. (2021). A standardisation framework for bio-logging data to advance ecological research and conservation.Methods in Ecology and Evolution , 12, 996–1007.
Shaffer, M.L. (1991). Population Viability Analysis. In:Challenges in the Conservation of Biological Resources . Routledge.
Sheridan, J.A. & Bickford, D. (2011). Shrinking body size as an ecological response to climate change. Nature Climate Change , 1, 401–406.
Shimada, T., Thums, M., Hamann, M., Limpus, C.J., Hays, G.C., FitzSimmons, N.N., et al. (2021). Optimising sample sizes for animal distribution analysis using tracking data. Methods in Ecology and Evolution , 12, 288–297.
Sih, A. (2013). Understanding variation in behavioural responses to human-induced rapid environmental change: a conceptual overview.Animal Behaviour , 85, 1077–1088.
Simbula, G., Vignoli, L., Carretero, M.A. & Kaliontzopoulou, A. (2021). Fluctuating asymmetry as biomarker of pesticides exposure in the Italian wall lizards (Podarcis siculus). Zoology , 147, 125928.
Singh, R., Prathibha, P. & Jain, M. (2020). Effect of temperature on life-history traits and mating calls of a field cricket, Acanthogryllus asiaticus. Journal of Thermal Biology , 93, 102740.
Sousa-Lima, R.S., Engel, M.H., Sábato, V., Lima, B.R., Queiróz, T.S.M., Brito, M.R.M., et al. (2018). Acoustic ecology of humpback whales in Brazilian waters investigated with basic and sophisticated passive acoustic technologies over 17 years. Western Indian Ocean Journal of Marine Science , 23–40.
Spanbauer, T.L., Allen, C.R., Angeler, D.G., Eason, T., Fritz, S.C., Garmestani, A.S., et al. (2016). Body size distributions signal a regime shift in a lake ecosystem. Proceedings of the Royal Society B: Biological Sciences , 283, 20160249.
Stirling, I. & Derocher, A.E. (2012). Effects of climate warming on polar bears: a review of the evidence. Global Change Biology , 18, 2694–2706.
Thawley, C.J., Goldy-Brown, M., McCormick, G.L., Graham, S.P. & Langkilde, T. (2019). Presence of an invasive species reverses latitudinal clines of multiple traits in a native species. Global Change Biology , 25, 620–628.
Thompson, W. (2013). Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters . Island Press.
Thoral, E., Queiros, Q., Roussel, D., Dutto, G., Gasset, E., McKenzie, D.J., et al. (2021). Changes in foraging mode caused by a decline in prey size have major bioenergetic consequences for a small pelagic fish. Journal of Animal Ecology , 90, 2289–2301.
Tilman, D., Clark, M., Williams, D.R., Kimmel, K., Polasky, S. & Packer, C. (2017). Future threats to biodiversity and pathways to their prevention. Nature , 546, 73–81.
Tini, M., Bardiani, M., Chiari, S., Campanaro, A., Maurizi, E., Toni, I., et al. (2018). Use of space and dispersal ability of a flagship saproxylic insect: a telemetric study of the stag beetle (Lucanus cervus) in a relict lowland forest. Insect Conservation and Diversity , 11, 116–129.
Trites, A.W. & Donnelly, C.P. (2003). The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutritional stress hypothesis. Mammal Review , 33, 3–28.
Tuneu-Corral, C., Puig-Montserrat, X., Flaquer, C., Mas, M., Budinski, I. & López-Baucells, A. (2020). Ecological indices in long-term acoustic bat surveys for assessing and monitoring bats’ responses to climatic and land-cover changes. Ecological Indicators , 110, 105849.
Tuomainen, U. & Candolin, U. (2011). Behavioural responses to human-induced environmental change. Biological Reviews , 86, 640–657.
Vilhunen, S., Hirvonen, H. & Laakkonen, M.V.-M. (2005). Less is more: social learning of predator recognition requires a low demonstrator to observer ratio in Arctic charr (Salvelinus alpinus). Behav Ecol Sociobiol , 57, 275–282.
Ward, R.J., Griffiths, R.A., Wilkinson, J.W. & Cornish, N. (2017). Optimising monitoring efforts for secretive snakes: a comparison of occupancy and N-mixture models for assessment of population status.Sci Rep , 7, 18074.
Wauchope, H.S., Amano, T., Geldmann, J., Johnston, A., Simmons, B.I., Sutherland, W.J., et al. (2021). Evaluating Impact Using Time-Series Data. Trends in Ecology & Evolution , 36, 196–205.
Wei, W.W.S. (2018). Multivariate Time Series Analysis and Applications . John Wiley & Sons.
Williams, H.J., Taylor, L.A., Benhamou, S., Bijleveld, A.I., Clay, T.A., Grissac, S. de, et al. (2020). Optimizing the use of biologgers for movement ecology research. Journal of Animal Ecology , 89, 186–206.
Williams, N.F., McRae, L., Freeman, R., Capdevila, P. & Clements, C.F. (2021). Scaling the extinction vortex: Body size as a predictor of population dynamics close to extinction events. Ecology and Evolution , 11, 7069–7079.
Yang, Z., Wang, T., Skidmore, A.K., Leeuw, J. de, Said, M.Y. & Freer, J. (2014). Spotting East African Mammals in Open Savannah from Space.PLOS ONE , 9, e115989.
Yates, M.C., Fraser, D.J. & Derry, A.M. (2019). Meta-analysis supports further refinement of eDNA for monitoring aquatic species-specific abundance in nature. Environmental DNA , 1, 5–13.
Zhang, H., Hollander, J. & Hansson, L.-A. (2017). Bi-directional plasticity: Rotifer prey adjust spine length to different predator regimes. Sci Rep , 7, 10254.
Zhu, M., Yamakawa, T. & Sakai, T. (2018). Combined use of trawl fishery and research vessel survey data in a multivariate autoregressive state-space (MARSS) model to improve the accuracy of abundance index estimates. Fish Sci , 84, 437–451.
Table1 . Overview on data acquisition methods usable to collect the signals of the timeline to collapse.