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    Rapid weight loss in free ranging pygmy killer whales (Feresa attenuata) and the implications for anthropogenic disturbance of odontocetes

    1.Tyack, P. L. et al. Beaked whales respond to simulated and actual navy sonar. PLoS ONE 6, e17009 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Lusseau, D. Effects of tour boats on the behavior of bottlenose dolphins: using Markov chains to model anthropogenic impacts. Conserv. Biol. 17, 1785–1793 (2003).Article 

    Google Scholar 
    3.Currey, R. J. C. et al. Survival rates for a declining population of bottlenose dolphins in Doubtful Sound, New Zealand: an information theoretic approach to assessing the role of human impacts. Aquat. Conserv. Mar. Freshwat. Ecosyst. 19, 658–670 (2009).MathSciNet 
    Article 

    Google Scholar 
    4.Caswell, H., Fujiwara, M. & Brault, S. Declining survival probability threatens the North Atlantic right whale. Proc. Natl. Acad. Sci. 96, 3308–3313 (1999).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Bejder, L. et al. Decline in relative abundance of bottlenose dolphins exposed to long-term disturbance. Conserv. Biol. 20, 1791–1798 (2006).PubMed 
    Article 

    Google Scholar 
    6.Pirotta, E. et al. Understanding the population consequences of disturbance. Ecol. Evol. 8, 9934–9946 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.New, L. F., Moretti, D. J., Hooker, S. K., Costa, D. P. & Simmons, S. E. Using energetic models to investigate the survival and reproduction of beaked whales (family Ziphiidae). PLoS ONE 8, e68725 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Fleishman, E. et al. Monitoring population-level responses of marine mammals to human activities. Mar. Mamm. Sci. 32, 1004–1021 (2016).Article 

    Google Scholar 
    9.Pirotta, E., Merchant, N. D., Thompson, P. M., Barton, T. R. & Lusseau, D. Quantifying the effect of boat disturbance on bottlenose dolphin foraging activity. Biol. Cons. 181, 82–89 (2015).Article 

    Google Scholar 
    10.Benoit-Bird, K. J. Prey caloric value and predator energy needs: foraging predictions for wild spinner dolphins. Mar. Biol. 145, 435–444 (2004).Article 

    Google Scholar 
    11.Wisniewska, D. M. et al. Ultra-high foraging rates of harbor porpoises make them vulnerable to anthropogenic disturbance. Curr. Biol. 26, 1441–1446 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Farmer, N. A., Noren, D. P., Fougères, E. M., Machernis, A. & Baker, K. Resilience of the endangered sperm whale Physeter macrocephalus to foraging disturbance in the Gulf of Mexico, USA: a bioenergetics approach. Mar. Ecol. Prog. Ser. 589, 241–261 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    13.New, L. F. et al. Modelling the biological significance of behavioural change in coastal bottlenose dolphins in response to disturbance. Funct. Ecol. 27, 314–322 (2013).Article 

    Google Scholar 
    14.Christiansen, F. et al. Poor body condition associated with an unusual mortality event in gray whales. Mar. Ecol. Prog. Ser. 658, 237–252 (2021).ADS 
    Article 

    Google Scholar 
    15.Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y. & Bejder, L. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468 (2016).Article 

    Google Scholar 
    16.Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281 (2018).ADS 
    Article 

    Google Scholar 
    17.Kastelein, R. A., Helder-Hoek, L., Jennings, N., van Kester, R. & Huisman, R. Reduction in body mass and blubber thickness of harbor porpoises (Phocoena phocoena) due to near-fasting for 24 hours in four seasons. Aquat. Mamm. 45, 37–47 (2019).Article 

    Google Scholar 
    18.Marine mammal populations and ocean noise: determining when noise causes biologically significant effects. In (eds. National Research Council (U.S.) & National Academies Press (U.S.)) 142 (National Academies Press, 2005).19.Booth, C. G., Sinclair, R. R. & Harwood, J. Methods for monitoring for the population consequences of disturbance in marine mammals: a review. Front. Mar. Sci. 7, 115 (2020).ADS 
    Article 

    Google Scholar 
    20.Villegas-Amtmann, S., Schwarz, L. K., Sumich, J. L. & Costa, D. P. A bioenergetics model to evaluate demographic consequences of disturbance in marine mammals applied to gray whales. Ecosphere 6, art183 (2015).21.Wikelski, M. & Cooke, S. J. Conservation physiology. Trends Ecol. Evol. 21, 38–46 (2006).PubMed 
    Article 

    Google Scholar 
    22.Fearnbach, H., Durban, J., Ellifrit, D. & Balcomb, K. Using aerial photogrammetry to detect changes in body condition of endangered southern resident killer whales. Endanger. Spec. Res. 35, 175–180 (2018).Article 

    Google Scholar 
    23.Harwood, J. et al. Understanding the Population Consequences of Acoustic Disturbance for Marine Mammals. In The Effects of Noise on Aquatic Life II (eds. Popper, A. N. & Hawkins, A.) 417–423 (Springer, 2016). https://doi.org/10.1007/978-1-4939-2981-8_49.24.Christiansen, F., Rojano-Doñate, L., Madsen, P. T. & Bejder, L. Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine mammals. Front. Mar. Sci. 3, 277 (2016).Article 

    Google Scholar 
    25.Christiansen, F., Nielsen, M. L. K., Charlton, C., Bejder, L. & Madsen, P. T. Southern right whales show no behavioral response to low noise levels from a nearby unmanned aerial vehicle. Mar. Mam. Sci. mms.12699 (2020) https://doi.org/10.1111/mms.12699.26.Castrillon, J. & Nash, S. B. Evaluating cetacean body condition; a review of traditional approaches and new developments. Ecol. Evol. 10, 6144–6162 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Durban, J. W. et al. Photogrammetry of blue whales with an unmanned hexacopter. Mar. Mamm. Sci. 32, 1510–1515 (2016).Article 

    Google Scholar 
    28.Christiansen, F. et al. Estimating body mass of free‐living whales using aerial photogrammetry and 3D volumetrics. Methods Ecol. Evol. 2041–210X.13298 (2019). https://doi.org/10.1111/2041-210X.13298.29.Krause, D. J., Hinke, J. T., Perryman, W. L., Goebel, M. E. & LeRoi, D. J. An accurate and adaptable photogrammetric approach for estimating the mass and body condition of pinnipeds using an unmanned aerial system. PLoS ONE 12, e0187465 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    30.Fiori, L., Doshi, A., Martinez, E., Orams, M. B. & Bollard-Breen, B. The use of unmanned aerial systems in marine mammal research. Remote Sensing 9, 543 (2017).ADS 
    Article 

    Google Scholar 
    31.Lusseau, D. The hidden cost of tourism: detecting long-term effects of tourism using behavioral information. Ecol. Soc. 9, 2 (2004).Article 

    Google Scholar 
    32.Bejder, L., Samuels, A., Whitehead, H. & Gales, N. Interpreting short-term behavioural responses to disturbance within a longitudinal perspective. Anim. Behav. 72, 1149–1158 (2006).Article 

    Google Scholar 
    33.Baird, R. Odontocete cetaceans around the main Hawaiian Islands: habitat use and relative abundance from small-boat sighting surveys. Aquat. Mamm. 39, 253–269 (2013).Article 

    Google Scholar 
    34.Hawaii Tourism Authority. Monthly Visitor Statistics. https://www.hawaiitourismauthority.org/research/monthly-visitor-statistics/ (2020).35.Tyne, J. A., Johnston, D. W., Rankin, R., Loneragan, N. R. & Bejder, L. The importance of spinner dolphin (Stenella longirostris) resting habitat: implications for management. J. Appl. Ecol. 52, 621–630 (2015).Article 

    Google Scholar 
    36.Wiener, C., Bejder, L., Johnston, D., Fawcett, L. & Wilkinson, P. Cashing in on spinners: revenue estimates of wild Dolphin-Swim Tourism in the Hawaiian Islands. Front. Mar. Sci. 7, (2020).37.Stack, S. H. et al. Identifying spinner dolphin Stenella longirostris longirostris movement and behavioral patterns to inform conservation strategies in Maui Nui, Hawai‘i. Mar. Ecol. Prog. Ser. 644, 187–197 (2020).ADS 
    Article 

    Google Scholar 
    38.Tyne, J. A., Christiansen, F., Heenehan, H. L., Johnston, D. W. & Bejder, L. Chronic exposure of Hawaii Island spinner dolphins (Stenella longirostris) to human activities. R. Soc. Open Sci. 5, 171506 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Heenehan, H. et al. Using Ostrom’s common-pool resource theory to build toward an integrated ecosystem-based sustainable cetacean tourism system in Hawai`i. J. Sustain. Tour. 23, 536–556 (2015).Article 

    Google Scholar 
    40.Baird, R. W. et al. Movements of two satellite-tagged pygmy killer whales (Feresa attenuata) off the island of Hawai‘i. Mar. Mamm. Sci. 27, E332–E337 (2011).Article 

    Google Scholar 
    41.Baird, R. W. et al. Movements and Spatial Use of Odontocetes in the Western Main Hawaiian Islands: Results of a Three-Year Study Off O’ahu and Kaua’i: http://www.dtic.mil/docs/citations/ADA602078 (2013). https://doi.org/10.21236/ADA602078.42.Forrester, D. J., Odell, D. K., Thompson, N. P. & White, J. R. Morphometrics, parasites, and chlorinated hydrocarbon residues of pygmy killer whales from Florida. J. Mammal. 61, 356–360 (1980).Article 

    Google Scholar 
    43.Kastelein, R. A., Mosterd, J., Schooneman, N. M. & Wiepkema, P. R. Food consumption, growth, body dimensions, and respiration rates of captive false killer whales (Pseudorca crassidens). Aquat. Mamm. 26, 33–44 (2000).
    Google Scholar 
    44.Elorriaga-Verplancken, F. R. et al. First record of pygmy killer whales (Feresa attenuata) in the Gulf of California, Mexico: diet inferences and probable relation with warm conditions during 2014. Aquat. Mamm. 42, 20–26 (2016).Article 

    Google Scholar 
    45.Castrillon, J., Huston, W. & Nash, S. B. The blubber adipocyte index: a nondestructive biomarker of adiposity in humpback whales (Megaptera novaeangliae). Ecol. Evol. 7, 5131–5139 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Fahlman, A. et al. Field energetics and lung function in wild bottlenose dolphins, Tursiops truncatus, in Sarasota Bay Florida. R. Soc. Open Sci. 5, 171280 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Nowacek, D. P., Christiansen, F., Bejder, L., Goldbogen, J. A. & Friedlaender, A. S. Studying cetacean behaviour: new technological approaches and conservation applications. Anim. Behav. 120, 235–244 (2016).Article 

    Google Scholar 
    48.Adamczak, S. K., Pabst, A., McLellan, W. A. & Thorne, L. H. Using 3D models to improve estimates of marine mammal size and external morphology. Front. Mar. Sci. 6, 334 (2019).Article 

    Google Scholar 
    49.Lindstedt, S. L. & Boyce, M. S. Seasonality, fasting endurance, and body size in mammals. Am. Nat. 125, 873–878 (1985).Article 

    Google Scholar 
    50.Blueweiss, L. et al. Relationships between body size and some life history parameters. Oecologia 37, 257–272 (1978).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Senigaglia, V. et al. Meta-analyses of whale-watching impact studies: comparisons of cetacean responses to disturbance. Mar. Ecol. Prog. Ser. 542, 251–263 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    52.Sprogis, K. R., Christiansen, F., Wandres, M. & Bejder, L. E. Niño Southern Oscillation influences the abundance and movements of a marine top predator in coastal waters. Glob. Change Biol. 24, 1085–1096 (2018).ADS 
    Article 

    Google Scholar 
    53.Henderson, E. E. et al. Delphinid behavioral responses to incidental mid-frequency active sonar. J. Acoust. Soc. America 136, 2003–2014 (2014).ADS 
    Article 

    Google Scholar 
    54.Schwacke, L. H. et al. Quantifying injury to common bottlenose dolphins from the Deepwater Horizon oil spill using an age-, sex- and class-structured population model. Endanger. Spec. Res. 33, 265–279 (2017).Article 

    Google Scholar 
    55.Rolland, R. M. et al. Health of North Atlantic right whales Eubalaena glacialis over three decades: from individual health to demographic and population health trends. Mar. Ecol. Prog. Ser. 542, 265–282 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    56.Dawson, S., Bowmwn, H., Leunissen, E. & Sirguey, P. Inexpensive aerial photogrammetry for studies of whales and large marine animals. Front. Mar. Sci. 4, 366 (2017).Article 

    Google Scholar 
    57.Karns, B. L., Ewing, R. Y. & Schaefer, A. M. Evaluation of body mass index as a prognostic indicator from two rough-toothed dolphin (Steno bredanensis) mass strandings in Florida. Ecol. Evol. 9, 10544–10552 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Stevenson, R. D. & Woods, W. A. Condition indices for conservation: new uses for evolving tools. Integr. Comp. Biol. 46, 1169–1190 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Kershaw, J. L., Sherrill, M., Davison, N. J., Brownlow, A. & Hall, A. J. Evaluating morphometric and metabolic markers of body condition in a small cetacean, the harbor porpoise (Phocoena phocoena). Ecol. Evol. 7, 3494–3506 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Susceptibility of Drosophila suzukii larvae to the combined administration of the entomopathogens Bacillus thuringiensis and Steinernema carpocapsae

    Introduced through human activities, such as travels and transcontinental trade, and supported by climate change, several invasive species can settle in habitats where formerly they could not survive. The presence of allochthonous species can negatively affect native species reducing biodiversity, represent a threat to human health and, in the case of phytophagous insects, cause serious damage to the agroeconomic system29. Characterized by rapid spread and enormous impact, an invasion like that of D. suzukii has few precedents. This species is thus becoming a model of study in the biology of alien species and for the development of pest management techniques30.The protection of crops from D. suzukii invasion is mainly conducted by means of insecticides31 which, as known, are unselective, in many cases remain in the environment and, if persistent on fruit, are harmful to human and animal health32. In addition, their efficiency can be reduced by weather events such as rainfall, and their prolonged use may induce resistance phenomena in target insects. Moreover, the preference of D. suzukii for ripening fruit requires that any treatment be carried out close to the harvesting, which inevitably means that insecticide residues can remain on the marketed fruit33. Alternatively to synthetic chemicals, natural compounds that can act as repellents, toxicants by contact or ingestion, and overall deterrents, have been tested mainly against D. suzukii adults34. All these control procedures have positive aspects, such as low cost, sometimes good effectiveness, and ease of use for farmers, but, as mentioned, their massive use affects environment and organisms at various levels. Considering the limits of the chemical control, several studies have addressed the problem of D. suzukii management through the development and improvement of biological control methods. However, given the recent introduction of this insect in Europe and America specific projects in these areas are still limited. Until now, only the main entomopathogens commonly used in biological control, have been tested against D. suzukii35.Several serovars of Bt were tested at different concentrations on D. suzukii. Cossentine et al.27 described the results of the assays with 22 serotypes of Bt, and among them, only few are effective against the target dipteran. In particular, the serovars thuringiensis, kurstaki, thompsoni, pakistani, and bolivia show an effectiveness of more than 75% on first stage larvae. The mortality rates, however, are related to a dietary administration of at least 108 spores/mL, below this concentration the insecticidal activity is not relevant. Moreover, the efficacy on adult individuals is extremely low, only the serovar thuringiensis induces a mortality of about 44%. In the literature, data on the low effects of serovar israelensis on this fly are also reported36. The data obtained from our trials, carried out with the serovar kurstaki, agree with Cossentine et al.27, demonstrating that only high concentrations of the bacterium produce significant effects on larvae in a relatively short time (24–48 h).As regards the insecticidal activity of EPN, the literature includes several studies carried out in laboratory or field with applications on plants and soil. The data collected using different species of nematodes, such as S. carpocapsae (Sc), S. feltiae, S. kraussei, H. bacteriophora25,37,38,39 and the rare species Oscheius onirici40, are quite promising but, as observed for Bt, also for EPN high concentrations are required, and their effectiveness is very variable between laboratory and field studies. In accordance with previous results29, our current data indicate a good efficacy of Sc: treatments with an amount of 1.6 × 103 IJs (corresponding to 80 IJ/cm2) resulted in a larvae mortality above 98%, 48 h post-treatment.Since the reproduction of D. suzukii occurs by spawning in the mesocarp of the fruit, the technical limit of the application of bio-insecticides such as Bt or EPN cannot be disregarded. Larvae development is protected by the fruit pulp and this may be one of the causes of the low effectiveness of Bt in field applications. As known, to be effective, Bt must be ingested and should therefore reach the larva by penetrating inside the fruit; perhaps a timely spraying of the crops before oviposition could theoretically promote the penetration of microorganisms thanks to the drive of ovipositor. Based on these considerations, it is reasonable to assume that coupling Bt with EPN, which, being motile, can actively reach the targets, could significantly improve the effectiveness of D. suzukii control methods in the field.The simultaneous application of two control agents, with different modes of action, may result in additive or complementary effects. In particular, the tissue damage inflicted by Bt to the intestinal epithelium could facilitate the access of nematodes in the passage from the gut to the hemocoel (Fig. 9). When in the hemolymph, the nematode and its symbionts implement strategies of immunoevasion and immunodepression of host immune responses4,41,42, leading to a drastic and more rapid physiological alteration that results in the death of the target insect in a shorter time.Figure 9A possible model of the effects induced by the combined administration of B. thuringiensis (Bt) and S. carpocapsae (EPN) to D. suzukii larvae. The presence of B. thuringiensis, ingested during feeding by D. suzukii larvae, could facilitate and speed up the passage of S. carpocapsae from the intestine to the hemocoelic cavity. Bt toxins, produced as parasporal inclusions, are activated in the intestinal lumen of the larva. Active toxins interact with the membrane receptors of the intestinal epithelium and are responsible for the formation of pores that alter the physiology of the cells leading to their lysis. The resulting epithelial lesions could provide an easy access route for EPN to other body regions of the larvae.Full size imageAs previously reported, and supported by literature, it is desirable that studies on biological control of D. suzukii will be increased and possibly new strategies for the use of bio-insecticides will be tested. In this perspective, we started a project aimed at assessing whether the association of entomopathogenic organisms and microorganisms is a promising strategy, capable of making safer and faster measures to control the diffusion of insect pests. Besides this, a need to reduce Bt concentrations used in field application has arisen from recent observations on the possible insecticidal effect on non-target insect populations, caused by intensive use and consequent bioaccumulation of the bacterium in crop applications43. It is also known that high concentrations of Bt serovars which produce the cry1Ba1 and 1 beta-exotoxin toxins, are toxic not only to several dipterans but also to mammals44,45.Even when using EPN, limitations have been highlighted: such as with Bt, they are susceptible to runoff in case of rainfall, have a significant sensitivity to drying at high temperatures, and, depending on the species, are more or less effective in certain environmental conditions as well as susceptible to the presence of pesticides46,47.On the basis of these considerations and the relevant literature, and after excluding adverse effects on the nematode induced by the presence of Bt, we conducted the assays with combinations of Bt and Sc, administering them both simultaneously and time-shifted. According to our data, the mortality of D. suzukii larvae increases from  More

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    Combining genotypic and phenotypic variation in a geospatial framework to identify sources of mussels in northern New Zealand

    1.Pineda, J., Hare, J. & Sponaugle, S. Larval transport and dispersal in the Coastal Ocean and consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

    Google Scholar 
    2.Siegel, D. A. et al. The stochastic nature of larval connectivity among nearshore marine populations. Proc. Natl. Acad. Sci. U. S. A. 105, 8974–8979 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Taylor, M. L. & Roterman, C. N. Invertebrate population genetics across Earth’s largest habitat: the deep-sea floor. Mol. Ecol. 26, 1–25 (2017).Article 

    Google Scholar 
    4.Apte, S., Star, B. & Gardner, J. P. A. A comparison of genetic diversity between cultured and wild populations, and a test of genetic introgression in the New Zealand greenshell mussel, Perna canaliculus (Gmelin 1791). Aquaculture 219, 193–220 (2003).Article 

    Google Scholar 
    5.Shanks, A. L., Grantham, B. A. & Carr, M. H. Propagule dispersal distance and the size and spacing of Marine Reserves. Ecol. Appl. 13, S159–S169 (2003).Article 

    Google Scholar 
    6.Hilário, A. et al. Estimating dispersal distance in the deep sea: challenges and applications to marine reserves. Front. Mar. Sci. 2, 1–14 (2015).Article 

    Google Scholar 
    7.van Gennip, S. J. et al. Going with the flow: the role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23, 2602–2617 (2017).ADS 
    Article 

    Google Scholar 
    8.Silva, C. N. S., MacDonald, H. S., Hadfield, M., Cryer, M. & Gardner, J. P. A. Ocean currents predict fine-scale genetic structure and source-sink dynamics in a marine invertebrate coastal fishery. ICES J. Mar. Sci. 76, 1007–1018 (2019).Article 

    Google Scholar 
    9.Reiss, H., Hoarau, G., Dickey-Collas, M. & Wolff, W. J. Genetic population structure of marine fish: mismatch between biological and fisheries management units. Fish Fish. 10, 361–395 (2009).Article 

    Google Scholar 
    10.von der Heyden, S. et al. The application of genetics to marine management and conservation: examples from the Indo-Pacific. Bull. Mar. Sci. 90, 123–158 (2014).Article 

    Google Scholar 
    11.Johnson, M. S. & Black, R. Chaotic genetic patchiness in an intertidal Limpet, Siphonaria sp. Mar. Biol. 70, 157–164 (1982).Article 

    Google Scholar 
    12.Hedgecock, D. & Pudovkin, A. I. Sweepstakes reproductive success in highly fecund marine fish and shellfish: a review and commentary. Bull. Mar. Sci. 87, 971–1002 (2011).Article 

    Google Scholar 
    13.Reisser, C. M. O., Bell, J. J. & Gardner, J. P. A. Correlation between pelagic larval duration and realised dispersal: long-distance genetic connectivity between northern New Zealand and the Kermadec Islands archipelago. Mar. Biol. 161, 297–312 (2014).Article 

    Google Scholar 
    14.Gardner, J. P. A., Bell, J. J., Constable, H. B., Hannan, D. A., Ritchie, P. A. & Zuccarello, G. C. Multi-species coastal marine connectivity: a literature review with recommendations for further research. N. Z. Aquat. Environ. Biodivers. Rep. 58, 1–47. ISSN 1176-9440 (2010).15.White, C. et al. Ocean currents help explain population genetic structure. Proc. R. Soc. Lond. B 277, 1685–1694 (2010).
    Google Scholar 
    16.Hannan, D. A., Constable, H. B., Silva, C. N. S., Bell, J. J., Ritchie, P. A. & Gardner, J. P. A. Genetic population structure connectivity and barriers to gene flow amongst New Zealand’s open sandy shore and estuarine coastal taxa. N. Z. Aquat. Environ. Biodivers. Rep. 172, 1–97. ISSN 1179-6480 (2016).17.Thorrold, S. R. et al. Quantifying larval retention and connectivity in marine populations with artificial and natural markers. Bull. Mar. Sci. 70(Supplement 1), 291–308 (2002).
    Google Scholar 
    18.Elsdon, T. S. et al. Otolith chemistry to describe movements and life-history parameters of fishes: hypotheses, assumptions, limitations and inferences. Oceanogr. Mar. Biol. Annu. Rev. 46, 297–330 (2008).
    Google Scholar 
    19.Carson, H. S. et al. Temporal, spatial, and interspecific variation in geochemical signatures within fish otoliths, bivalve larval shells, and crustacean larvae. Mar. Ecol. Prog. Ser. 473, 133–148 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    20.Becker, B. J., Fodrie, F. J., McMillan, P. A. & Levin, L. A. Spatial and temporal variation in trace elemental fingerprints of mytilid mussel shells: a precursor to invertebrate larval tracking. Limnol. Oceanogr. 50, 48–61 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Dunphy, B., Millet, M.-A. & Jeffs, A. Elemental signatures in the shells of early juvenile green-lipped mussels (Perna canaliculus) and their potential use for larval tracking. Aquaculture 311, 187–192 (2011).Article 

    Google Scholar 
    22.Norrie, C. R., Dunphy, B. J., Ragg, N. L. & Lundquist, C. J. Comparative influence of genetics, ontogeny and the environment on elemental fingerprints in the shell of Perna canaliculus. Sci. Rep. 9, 8533 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    23.van Deurs, M. et al. Marine ecosystem connectivity mediated by migrant-resident interactions and the concomitant cross-system flux of lipids. Ecol. Evol. 6, 4076–4087. https://doi.org/10.1002/ece3.2167 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Huxham, M., Kimani, E., Newton, J. & Augley, J. Stable isotope records from otoliths as tracers of fish migration in a mangrove system. J. Fish Biol. 70, 1554–1567. https://doi.org/10.1111/j.1095-8649.2007.01443.x (2007).Article 

    Google Scholar 
    25.Phillips, D. L., Newsome, S. D. & Gregg, J. W. Combining sources in stable isotope mixing models: alternative methods. Oecologia 144, 520–527 (2005).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Phillips, D. L. IsoSource: stable isotope mixing model for partitioning an excess number of sources. http://www.epa.gov/wed/pages/models/stableIsotopes/isosource/isosource.htm (2008).27.Madigan, D. J., Baumann, Z. & Fisher, N. S. Pacific bluefin tuna transport Fukushima-derived radionuclides from Japan to California. Proc. Natl. Acad. Sci. U. S. A. 109, 9483–9486 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Aquaculture New Zealand. New Zealand aquaculture. A sector overview with key facts and statistics. https://www.aquaculture.org.nz/wp-content/uploads/2018/08/New-Zealand-Aquaculture-facts-2018.pdf (2018)29.Jeffs, A., Holland, R., Hooker, S. & Hayden, B. Overview and bibliography of research on the greenshell mussel, Perna canaliculus, from New Zealand waters. J. Shellfish Res. 18, 347–360 (1999).
    Google Scholar 
    30.Alfaro, A., Jeffs, A., Gardner, J. P. A., Breen, B. B. & Wilkin, J. Green-lipped mussels in GLM 9. N. Z. Fish. Assess. Rep. 48, 1–80 (2011).
    Google Scholar 
    31.Sutton, P. J. H. & Bowen, M. M. Currents off the west coast of Northland, New Zealand. N. Z. J. Mar. Freshwat. Res. 45, 609–624. https://doi.org/10.1080/00288330.2011.569729 (2011).Article 

    Google Scholar 
    32.Alfaro, A. C., McArdle, B. & Jeffs, A. G. Temporal patterns of arrival of beachcast green-lipped mussel (Perna canaliculus) spat harvested for aquaculture in New Zealand and its relationship with hydrodynamic and meteorological conditions. Aquaculture 302, 208–218 (2010).Article 

    Google Scholar 
    33.Dunphy, B. J., Silva, C. N. S. & Gardner, J. P. A. Testing techniques for tracing the provenance of green-lipped mussel spat washed up on Ninety Mile Beach, New Zealand. N. Z. Aquat. Environ. Biodivers. Rep. 164, 1–45. ISSN 1179-6480 (2015).34.Guillot, G., Renaud, S., Ledevin, R., Michaux, J. & Claude, J. A unifying model for the analysis of phenotypic, genetic and geographic data. Syst. Biol. 61, 897–911 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Forsman, A. Diversity promotes establishment. Proc. Natl. Acad. Sci. 111, 302–307. https://doi.org/10.1073/pnas.1317745111 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Castillo, J. M. et al. Low genetic diversity contrasts with high phenotypic variability in heptaploid Spartina densiflora populations invading the Pacific coast of North America. Ecol. Evol. 8, 4992–5007. https://doi.org/10.1002/ece3.4063 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Villellas, J., Berjano, R., Terrab, A. & García, M. B. Divergence between phenotypic and genetic variation within populations of a common herb across Europe. Ecosphere 5, 1–14 (2014).Article 

    Google Scholar 
    38.Tanner, S. E., Pérez, M., Presa, P., Thorrold, S. R. & Cabral, H. N. Integrating microsatellite DNA markers and otolith geochemistry to assess population structure of European hake (Merluccius merluccius). Estuar. Coast. Shelf Sci. 142, 68–75 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    39.Wei, K., Wood, A. R. & Gardner, J. P. A. Population genetic variation in the New Zealand greenshell mussel: locus-dependent conflicting signals of weak structure and high gene flow balanced against pronounced structure and high self-recruitment. Mar. Biol. 160, 931–949 (2013).Article 

    Google Scholar 
    40.Apte, S. & Gardner, J. P. A. Population genetic variation in the New Zealand greenshell mussel, Perna canaliculus: SSCP and RFLP analyses of mitochondrial DNA. Mol. Ecol. 11, 1617–1628 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Star, B., Apte, S. & Gardner, J. P. A. Genetic structuring among populations of the greenshell mussel Perna canaliculus (Gmelin 1791) revealed by analysis of Randomly Amplified Polymorphic DNA. Mar. Ecol. Prog. Ser. 249, 171–182 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    42.Norrie, C. R., Dunphy, B. J., Roughan, M., Weppe, S. & Lundquist, C. J. Spill-over from aquaculture may provide a larval subsidy for the restoration of mussel reefs. Aquac. Environ. Interact. 12, 231–249 (2020).Article 

    Google Scholar 
    43.Reis-Santos, P. et al. Reconciling differences in natural tags to infer demographic and genetic connectivity in marine fish populations. Sci. Rep. 8, 10343. https://doi.org/10.1038/s41598-018-28701-6 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Sorte, C. J. B., Etter, R. J., Spackman, R., Boyle, E. E. & Hannigan, R. E. Elemental fingerprinting of mussel shells to predict population sources and redistribution potential in the Gulf of Maine. PLoS ONE 8(11), e80868 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Gillanders, B., Sanchez-Jerez, P., Bayle-Sempere, J. & Ramos-Espla, A. Trace elements in otoliths of the two-banded bream from a coastal region in the south-west Mediterranean: are there differences among locations?. J. Fish Biol. 59, 350–363 (2001).CAS 
    Article 

    Google Scholar 
    46.Burton, J. D. Basic properties and processes in estuarine chemistry. In Estuarine Chemistry (eds Burton, J. D. & Liss, P. S.) 1–31 (Academic Press, 1976).
    Google Scholar 
    47.Gillespie, J. L. & Nelson, C. S. Distribution and control of mixed terrigenous-carbonate surficial sediment facies, Wanganui shelf, New Zealand. N. Z. J. Geol. Geophys. 39, 533–549 (1996).CAS 
    Article 

    Google Scholar 
    48.Churchman, G., Hunt, J., Glasby, G., Renner, R. & Griffiths, G. Input of river-derived sediment to the New Zealand continental shelf: II mineralogy and composition. Estuar. Coast. Shelf Sci. 27, 397–411 (1988).ADS 
    CAS 
    Article 

    Google Scholar 
    49.Nelson, C. S., Keane, S. L. & Head, P. S. Non-tropical carbonate deposits on the modern New Zealand shelf. Sed. Geol. 60, 71–94 (1988).CAS 
    Article 

    Google Scholar 
    50.Payne, D. S. Shelf-to-Slope Sedimentation on the North Kaipara Continental Margin, Northwestern North Island, New Zealand. MSc thesis held by the University of Waikato (2008).51.Ricardo, F., Pimentel, T., Génio, L. & Calado, R. Spatio-temporal variability of trace elements fingerprints in cockle (Cerastoderma edule) shells and its relevance for tracing geographic origin. Sci. Rep. 7, 3475 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Cathey, A. M., Miller, N. R. & Kimmel, D. G. Spatiotemporal stability of trace and minor elemental signatures in early larval shell of the Northern quahog (Hard Clam) Mercenaria mercenaria. J. Shellfish Res. 33, 247–255 (2014).Article 

    Google Scholar 
    53.Bennion, M. et al. Trace element fingerprinting of blue mussel (Mytilus edulis) shells and soft tissues successfully reveals harvesting locations. Sci. Total Environ. 685, 50–58. https://doi.org/10.1016/j.scitotenv.2019.05.233 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Aquaculture New Zealand. New Zealand Greenshell Mussel Spat Strategy, pp. 1–23. www.aquaculture.org.nz (2020)55.SPATnz. Newspaper Story (accessed 5 November 2019); http://www.scoop.co.nz/stories/BU1910/S00425/spatnz-reveals-200m-results-of-mussel-breeding-programme.htm (2019).56.New Zealand Government Aquaculture Strategy. https://www.fisheries.govt.nz/dmsdocument/15895-The-Governments-Aquaculture-Strategy-to-2025 (2019).57.Gardner, J. P. A., Wenne, R., Westfall, K. R. & Zbawicka, M. Invasive mussels threaten regional scale genetic diversity in mainland and remote offshore locations: the need for baseline data and enhanced protection in the Southern Ocean. Glob. Change Biol. 22, 3182–3195 (2016).ADS 
    Article 

    Google Scholar 
    58.Larraín, M. A., Zbawicka, M., Araneda, C., Gardner, J. P. A. & Wenne, R. Native and invasive taxa on the Pacific coast of South America: impacts on aquaculture, traceability and biodiversity of blue mussels (Mytilus spp.). Evol. Appl. 11, 298–311 (2018).Article 
    CAS 

    Google Scholar 
    59.Nowland, S. J., Silva, C. N. S., Southgate, P. C. & Strugnell, J. M. Mitochondrial and nuclear genetic analyses of the tropical black-lip rock oyster (Saccostrea echinata) reveals population subdivision and informs sustainable aquaculture development. BMC Genom. 20, 71 (2019).Article 
    CAS 

    Google Scholar 
    60.Hickman, R. Allometry and growth of the green-lipped mussel Perna canaliculus in New Zealand. Mar. Biol. 51, 311–327 (1979).Article 

    Google Scholar 
    61.MacAvoy, E. S., Wood, A. R. & Gardner, J. P. A. Development and evaluation of microsatellite markers for identification of individual Greenshell mussels (Perna canaliculus) in a selective breeding programme. Aquaculture 274, 41–48 (2008).CAS 
    Article 

    Google Scholar 
    62.Guichoux, E. et al. Current trends in microsatellite genotyping. Mol. Ecol. Resour. 11, 591–611 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 
    CAS 

    Google Scholar 
    64.Rousset, F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    66.Clarke, K. R. & Gorley, R. N. PRIMER V6: User Manual/Tutorial (PRIMER-E Ltd, 2006).
    Google Scholar 
    67.Piry, S. et al. GENECLASS2: a software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Paetkau, D., Slade, R., Burden, M. & Estoup, A. Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power. Mol. Ecol. 13, 55–65 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Strasser, C. A., Mullineaux, L. S. & Walther, B. D. Growth rate and age effects on Mya arenaria shell chemistry: Implications for biogeochemical studies. J. Exp. Mar. Biol. Ecol. 355, 153–163 (2008).CAS 
    Article 

    Google Scholar 
    70.Bello, A. On the performance of rank transform discriminant method in error-rate estimation. J. Stat. Comput. Simul. 48, 153–165 (1993).Article 

    Google Scholar 
    71.JMP 13.0 Software. SAS Institute.72.Team RC. R: A Language and Environment for Statistical Computing (Team RC, 2014).
    Google Scholar 
    73.Silva, C. N. S. & Gardner, J. P. A. Emerging patterns of genetic variation in the New Zealand endemic scallop Pecten novaezelandiae. Mol. Ecol. 24, 5379–5393 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    ENSO feedback drives variations in dieback at a marginal mangrove site

    1.McPhaden, M. J. & Busalacchi, A. J. The tropical ocean-global atmosphere observing system: A Decade of progress research. Oceans. https://doi.org/10.1029/97JC02906 (1998).2.Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87(2), 341–359 (2017).Article 

    Google Scholar 
    3.Adame, M. F. et al. Mangroves in arid regions: Ecology, threats, and opportunities. Estuar. Coast. Shelf Sci. 1, 106796 (2020).
    Google Scholar 
    4.Asbridge, E. F. et al. Assessing the distribution and drivers of mangrove dieback in Kakadu National Park, Northern Australia. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

    Google Scholar 
    5.Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15(5), 257–265 (2017).Article 

    Google Scholar 
    6.Spalding, M. D. et al. The role of ecosystems in coastal protection: adapting to climate change and coastal hazards. Ocean. Coast. Manag. 90, 50–57 (2014).7.Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Mangrove mortality in a changing climate: An overview. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).ADS 
    Article 

    Google Scholar 
    8.Mafi-Gholami, D., Zenner, E. K., & Jaafari, A. Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century. Ecol. Indic. 110, 105972 (2020).Article 

    Google Scholar 
    9.Jump, A. S., & Penuelas J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8(9), 1010–1020 (2005).Article 

    Google Scholar 
    10.Jimenez, J. A., Lugo, A. E. & Cintron, G. Tree mortality in mangrove forests. Biotropica 17, 177–185 (1985).Article 

    Google Scholar 
    11.Xie, S.-P. et al. Indo-western pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci. 33(4), 411–432 (2016).Article 

    Google Scholar 
    12.Hamlington, B. D. et al. An ongoing shift in Pacific Ocean sea level. J. Geophys/ Res. Oceans 121, 5084–5097 (2016).ADS 
    Article 

    Google Scholar 
    13.Merrifield, M. A., Thompson, P. R. & Lander, M. Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys. Res. Lett. 39, 2–6 (2012).Article 

    Google Scholar 
    14.Godfrey, J. S. & Ridgway, K. R. The large-scale environment of the poleward-flowing Leeuwin Current, Western Australia: Longshore steric height gradients, wind stresses and geostrophic flow. J. Phys. Oceanogr. 15, 481–495 (1985).ADS 
    Article 

    Google Scholar 
    15.Drexler, J. Z. & Ewel, K. C. Wetland complex linked references are available on JSTOR for this article: Effect of the 1997–1998 ENSO-related drought on hydrology and salinity in a Micronesian wetland complex. Estuaries 24, 347–356 (2001).Article 

    Google Scholar 
    16.Duke, N. C. et al. Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: A severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res. 68(10), 1816–1829 (2017).Article 

    Google Scholar 
    17.Cai, W. et al. More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488, 365–369 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Wilson, S. G., Taylor, J. G., & Pearce, A. F. The Seasonal Aggregation of Whale Sharks at Ningaloo Reef, Western Australia: Currents, Migrations and the El Niño/Southern Oscillation. Environmental Biology of Fishes. https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1023/A:1011069914753&casa_token=55v4NHJmcDcAAAAA:owpASeBazqNzQzH7Z9xJI0BOtHzNZMvjTiJHRjLGIFCWzhyiWwMvYJUU8cloH46JDWCSZ7XOhu_CZuzZ0w. (2001).19.Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 1, 1–8 (2017).
    Google Scholar 
    20.Giri, C. Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remot. Sens. 8, 783 (2016).ADS 
    Article 

    Google Scholar 
    21.Fatoyinbo, T. E., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove, forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci. 113, 1–13 (2008).Article 

    Google Scholar 
    22.Rodriguez, W., Feller, I. C. & Cavanaugh, K. C. Spatio-temporal changes of a mangrove saltmarsh ecotone in the northeastern coast of Florida, USA. Glob. Ecol. Conserv. 7, 245–261 (2016).Article 

    Google Scholar 
    23.Bureau of Meteorology. Record-Breaking La Niña Events. Australian Government. http://www.bom.gov.au/climate/enso/history/La-Nina-2010-12.pdf (2012).24.Jensen, J. R. et al. The measurement of mangrove characteristics in southwest Florida using spot multispectral data. Geocarto Int. 6, 13–21 (1991).Article 

    Google Scholar 
    25.Eslami-Andargoli, L., Dale, P., Sipe, N. & Chaseling, J. Mangrove expansion and rainfall patterns in Moreton Bay, Southeast Queensland, Australia. Estuar. Coast. Shelf Sci. 85, 292–298 (2009).ADS 
    Article 

    Google Scholar 
    26.Hicks, W., Fitzpatrick, R. W., & Bowman, G. (2003) Managing coastal acid sulfate soils: the East Trinity example. in Advances in regolith: Proceedings of the CRC LEME regional regolith symposia. CRC LEME, Bentley 174–177.27.Harris, N. L. et al. Using spatial statistics to identify emerging hot spots of forest loss. Environ. Res. Lett. 12, 024012 (2017).ADS 
    Article 

    Google Scholar 
    28.Bryan-Brown, D. N. et al. Global trends in mangrove forest fragmentation. Sci. Rep. 10(1), 7117 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    29.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the anthropocene. Science 359(6371), 80–83 (2018).30.Wang, H. J., Zhang, R. H., Cole, J. & Chavez, F. El Niño and the related phenomenon southern oscillation (ENSO): The largest signal in interannual climate variation. Proc. Natl. Acad. Sci. USA. 96(20), 11071–11072 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    31.Berg, A. et al. Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Chang. 6, 869–874 (2016).ADS 
    Article 

    Google Scholar 
    32.Perry, S. J., McGregor, S., Gupta, A. S. & England, M. H. Future changes to El Niño-southern oscillation temperature and precipitation teleconnections. Geophys. Res. Lett. 44(20), 10608–10616 (2017).ADS 
    Article 

    Google Scholar 
    33.Osland, M. J. et al. Beyond just sea-level rise: Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change. Glob. Change Biol. 22, 1–11 (2016).ADS 
    Article 

    Google Scholar 
    34.Jentsch, A. & Beierkuhnlein, C. Research frontiers in climate change: Effects of extreme meteorological events on ecosystems. C.R. Geosci. 340, 621–628 (2008).ADS 
    Article 

    Google Scholar 
    35.Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).ADS 
    Article 

    Google Scholar 
    36.Landsat 7 (L7) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1927_L7_Data_Users_Handbook-v2.pdf. (2009).37.Landsat 8 (L8) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf. (2009).38.Story, M. & Congalton, R. G. Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote. Sens. 52, 397–399 (1986).
    Google Scholar 
    39.Moore, C. et al. Improving essential fish habitat designation to support sustainable ecosystem-based fisheries management. Mar. Policy 69, 32–41 (2016).40.Burnham, K. P., & Anderson, R. A practical information-theoretic approach. in Model Selection and Multimodel Inference 2. http://sutlib2.sut.ac.th/sut_contents/H79182.pdf.41.Burnham, K. P., & Anderson, D. R. Practical use of the information-theoretic approach. in Model Selection and Inference: A Practical Information-Theoretic Approach (eds. Burnham K. P. & Anderson D. R.) 75–117 (New York, NY, Springer, 1998).42.Cornforth, W. A., Fatoyinbo, T. E., Freemantle, T. P. & Pettorelli, N. Advanced land observing satellite phased array type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remot. Sens. 5, 224–237 (2013).ADS 
    Article 

    Google Scholar 
    43.Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73, 91–100 (2007).ADS 
    Article 

    Google Scholar 
    44.Long, J., Giri, C., Primavera, J. & Trivedi, M. Damage and recovery assessment of the Philippines ’ mangroves following Super Typhoon Haiyan. MPB 109, 734–743 (2016).CAS 

    Google Scholar 
    45.Satyanarayana, B., Mohamad, K. A., Idris, I. F., Husain, M.-L. & Dahdouh-Guebas, F. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int. J. Remot. Sens. 32, 1635–1650 (2011).Article 

    Google Scholar 
    46.Almahasheer, H., Aljowair, A., Duarte, C. M. & Irigoien, X. Decadal stability of red sea mangroves. Estuar. Coast. Shelf Sci. 169, 164–172 (2016).ADS 
    Article 

    Google Scholar 
    47.Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).Article 

    Google Scholar  More

  • in

    Climate change and specialty coffee potential in Ethiopia

    1.Agovino, M., Casaccia, M., Ciommi, M., Ferrara, M. & Marchesano, K. Agriculture, climate change and sustainability: The case of EU-28. Ecol. Ind. 105, 525–543 (2019).Article 

    Google Scholar 
    2.Vegro, C. L. R. & de Almeida, L. F. in Coffee Consumption and Industry Strategies in Brazil 3–19 (Elsevier, 2020).3.Bunn, C., Läderach, P., Jimenez, J. G. P., Montagnon, C. & Schilling, T. Multiclass classification of agro-ecological zones for Arabica coffee: An improved understanding of the impacts of climate change. PLoS ONE 10, e0140490 (2015).Article 

    Google Scholar 
    4.Bunn, C., Läderach, P., Rivera, O. O. & Kirschke, D. A bitter cup: climate change profile of global production of Arabica and Robusta coffee. Clim. Change 129, 89–101 (2015).ADS 
    Article 

    Google Scholar 
    5.Pham, Y., Reardon-Smith, K., Mushtaq, S. & Cockfield, G. The impact of climate change and variability on coffee production: A systematic review. Clim. Change 156, 609–630 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Chemura, A., Kutywayo, D., Chidoko, P. & Mahoya, C. Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Reg. Environ. Change 16, 473–485 (2016).Article 

    Google Scholar 
    7.Laderach, P. et al. in The economic, social and political elements of climate change 703–723 (Springer, 2011).8.Baker, P. & Haggar, J. Global warming: Effects on global coffee (SCAA Conference Handout, Long Beach, 2007).9.Craparo, A., Van Asten, P. J., Läderach, P., Jassogne, L. T. & Grab, S. Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric. For. Meteorol. 207, 1–10 (2015).ADS 
    Article 

    Google Scholar 
    10.Alves, M. C., Carvalho, L. G., Pozza, E. A., Sanches, L. & Maia, J. Ecological zoning of soybean rust, coffee rust and banana sigatoka based on Brazilian climate changes. Earth Syst. Sci. Global Change Clim. People 6, 35–46. https://doi.org/10.1016/j.proenv.2011.05.005 (2011).Article 

    Google Scholar 
    11.Jaramillo, J., Muchugu, E., Vega, F. E., Davis, A. & Borgemesister, C. Some like it hot: The influence and implications of climate change on coffee berry borer (Hypothenemus hampei) and coffee production in East Africa. PLoS ONE 6, e24528. https://doi.org/10.1371/journal.pone.0024528 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Kutywayo, D., Chemura, A., Kusena, W., Chidoko, P. & Mahoya, C. The impact of climate change on the potential distribution of agricultural pests: The case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe. Plos One 8, e73432. https://doi.org/10.1371/journal.pone.0073432 (2013).13.Läderach, P. et al. Climate change adaptation of coffee production in space and time. Clim. Change 141, 47–62 (2017).Article 

    Google Scholar 
    14.Scholz, M. B. d. S., Kitzberger, C. S. G., Prudencio, S. H. & Silva, R. S. d. S. F. d. The typicity of coffees from different terroirs determined by groups of physico-chemical and sensory variables and multiple factor analysis. Food Res. Int. 114, 72–80. https://doi.org/10.1016/j.foodres.2018.07.058 (2018).15.Bertrand, B. et al. Comparison of the effectiveness of fatty acids, chlorogenic acids, and elements for the chemometric discrimination of coffee (Coffea arabica L.) varieties and growing origins. J. Agric. Food Chem. 56, 2273–2280 (2008).16.Cheng, B., Furtado, A., Smyth, H. E. & Henry, R. J. Influence of genotype and environment on coffee quality. Trends Food Sci. Technol. 57, 20–30 (2016).CAS 
    Article 

    Google Scholar 
    17.Bote, A. D. & Vos, J. Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality. NJAS-Wageningen J. Life Sci. 83, 39–46 (2017).18.de Carvalho, A. M. et al. Relationship between the sensory attributes and the quality of coffee in different environments. Afr. J. Agric. Res. 11, 3607–3614 (2016).Article 

    Google Scholar 
    19.Sberveglieri, V. et al. in AIP Conference Proceedings. 86–87 (American Institute of Physics).20.Bertrand, B. et al. Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality. Food Chem. 135, 2575–2583 (2012).CAS 
    Article 

    Google Scholar 
    21.International Trade Centre. The Coffee Exporter’s Guide (World Trade Organization and the United Nations, 2011).
    Google Scholar 
    22.Lambot, C. et al. in The Craft and Science of Coffee (ed Britta Folmer) 17–49 (Academic Press, 2017).23.Ahmed, S. & Stepp, J. R. Beyond yields: Climate effects on specialty crop quality and agroecological management. Element. Sci. Anthropocene 4, 92 (2016).24.Purba, P., Sukartiko, A. & Ainuri, M. in IOP Conference Series: Earth and Environmental Science. 012021 (IOP Publishing).25.Traore, T. M., Wilson, N. L. & Fields, D. What explains specialty coffee quality scores and prices: A case study from the cup of excellence program. J. Agric. Appl. Econ. 50, 349–368 (2018).Article 

    Google Scholar 
    26.Barjolle, D., Quiñones-Ruiz, X. F., Bagal, M. & Comoé, H. The role of the state for geographical indications of coffee: Case studies from Colombia and Kenya. World Dev. 98, 105–119 (2017).Article 

    Google Scholar 
    27.Oguamanam, C. & Dagne, T. Geographical indication (GI) options for Ethiopian coffee and Ghanaian cocoa. Innovation and intellectual property: Collaborative dynamics in Africa, 77–108 (2014).28.Boaventura, P. S. M., Abdalla, C. C., Araujo, C. L. & Arakelian, J. S. Value co-creation in the specialty coffee value chain: The third-wave coffee movement. Revista de Administração de Empresas 58, 254–266 (2018).Article 

    Google Scholar 
    29.Lannigan, J. Making a space for taste: Context and discourse in the specialty coffee scene. Int. J. Inf. Manage. 51, 101987 (2020).Article 

    Google Scholar 
    30.Masters, G., Baker, P. & Flood, J. Climate change and agricultural commodities. CABI Work. Pap. 2, 1–38 (2010).
    Google Scholar 
    31.Rahman, S., Gross, M., Battiste, M. & Gacioch, M. Specialty Coffee Farmers’ Climate Change Concern and Perceived Ability to Adapt. (2016).32.Srinivasan, R., Giannikas, V., Kumar, M., Guyot, R. & McFarlane, D. Modelling food sourcing decisions under climate change: A data-driven approach. Comput. Ind. Eng. 128, 911–919 (2019).Article 

    Google Scholar 
    33.Chemura, A., Schauberger, B. & Gornott, C. Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLoS ONE 15, e0229881 (2020).CAS 
    Article 

    Google Scholar 
    34.FAO. (Food Agriculture Organization of the United Nations, Roma, 2012).35.Hirons, M. et al. Pursuing climate resilient coffee in Ethiopia: A critical review. Geoforum 91, 108–116 (2018).Article 

    Google Scholar 
    36.Central Statistical Agency (CSA). Agricultural Sample Survey 2018/19. (2019).37.Murken, L. et al. Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Ethiopia’s Agricultural Sector. (2020).38.Ridley, F. The past and future climatic suitability of arabica coffee (Coffea arabica L.) in East Africa, Durham University, (2011).39.Putri, S. P., Irifune, T. & Fukusaki, E. GC/MS based metabolite profiling of Indonesian specialty coffee from different species and geographical origin. Metabolomics 15, 126 (2019).Article 

    Google Scholar 
    40.Mengistie, G. in Extending the Protection of Geographical Indications: Case studies of Agricultural Products of Africa Vol. 15 (eds M Blakeney, T Coulet, Getachew Mengistie, & M.T Mahop) 150 (Routledge, 2011).41.Kufa, T., Ayano, A., Yilma, A., Kumela, T. & Tefera, W. The contribution of coffee research for coffee seed development in Ethiopia. J. Agric. Res. Dev. 1, 009–016 (2011).
    Google Scholar 
    42.Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).Article 

    Google Scholar 
    43.Moat, J., Gole, T. W. & Davis, A. P. Least Concern to Endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Glob. Change Biol. 25, 390–403 (2019).ADS 
    Article 

    Google Scholar 
    44.Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): Predicting future trends and identifying priorities. PLoS ONE 7, e47981. https://doi.org/10.1371/journal.pone.0047981 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.CIAT. Future Climate Scenarios for Tanzania’s Arabica Coffee Growing Areas. 27 (International Center for Tropical Agriculture, Cali, Colombia: , 2012).46.Laderach, P., Jarvis, A. & Ramirez, J. The impact of climate change in coffee-growing regions: The case of 10 municipalities in Nicaragua. 4 (CafeDirect/GTZ, 2006).47.Gomes, L. C. et al. Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agr. Ecosyst. Environ. 294, 106858. https://doi.org/10.1016/j.agee.2020.106858 (2020).Article 

    Google Scholar 
    48.Brown, N. in Daily Coffee News (Roast Magazine, 2018).49.Labouisse, J.-P., Bellachew, B., Kotecha, S. & Bertrand, B. Current status of coffee (Coffea arabica L.) genetic resources in Ethiopia: implications for conservation. Genet. Resour. Crop Evol. 55, 1079 (2008).50.MFA. Coffee production in Ethiopia. The 4th World Coffee Conference in Addis Ababa, Ministry of Foreign Affairs of Ethiopia, Addis Ababa, Ethiopia (2016).51.Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857 (2017).52.Chemura, A., Mahoya, C., Chidoko, P. & Kutywayo, D. Effect of soil moisture deficit stress on biomass accumulation of four coffee (Coffea arabica) varieties in Zimbabwe. ISRN Agron. 1–10, 2014. https://doi.org/10.1155/2014/767312 (2014).Article 

    Google Scholar 
    53.Hannah, L. et al. Climate change, wine, and conservation. Proc. Natl. Acad. Sci. 110, 6907–6912 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    54.Impact on variety and origin chemometric determination. Villarreal, D. et al. Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile. J. Agric. Food Chem. 57, 11321–11327 (2009).Article 

    Google Scholar 
    55.Sisay, B. T. in Sustainable agriculture reviews 33 99–113 (Springer, 2018).56.DaMatta, F. b. M., Avila, R. T., Cardoso, A. A., Martins, S. C. & Ramalho, J. C. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. J. Agric. Food Chem. 66, 5264–5274 (2018).57.CABI. (2015).58.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

    Google Scholar 
    59.Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: Considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).Article 

    Google Scholar 
    60.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).Article 

    Google Scholar 
    61.R Core Team. R: A language and environment for statistical computing. (2019).62.Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PloS one 9 (2014).63.Nair, K. P. P. The Agronomy and Economy of Important Tree Crops of the Developing World. 368 (Elservier, 2010).64.Coste, J. Coffee: The plant and the product. (Longman, 1992).65.Lin, F.-J. Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual. Quant. 42, 417–426 (2008).Article 

    Google Scholar 
    66.Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

    Google Scholar 
    67.Breiman, L. Random forests machine learning. 45: 5–32. View Article PubMed/NCBI Google Scholar (2001).68.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
    Article 

    Google Scholar 
    69.Li, X. & Wang, Y. Applying various algorithms for species distribution modelling. Integr. Zool. 8, 124–135 (2013).Article 

    Google Scholar 
    70.Gobeyn, S. et al. Evolutionary algorithms for species distribution modelling: A review in the context of machine learning. Ecol. Model. 392, 179–195 (2019).Article 

    Google Scholar 
    71.Vapnik, V. The nature of statistical learning theory. (Springer science & business media, 2013).72.Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F. & Kløve, B. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Sci. Total Environ. 615, 272–281 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    73.Pourghasemi, H. R., Yousefi, S., Kornejady, A. & Cerdà, A. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764–775 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    74.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

    Google Scholar 
    75.Chang, Y. & Bourque, C.P.-A. Relating modelled habitat suitability for Abies balsamea to on-the-ground species structural characteristics in naturally growing forests. Ecol. Ind. 111, 105981 (2020).Article 

    Google Scholar 
    76.Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

    Google Scholar 
    77.Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (2020).78.ArcGIS Desktop v. 10.2 (Environmental Systems Research Institute, Redlands, CA, Redlands, 2012).79.WorldClim. Global climate and weather data. https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html ( 2020).80.Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).Article 

    Google Scholar 
    81.van Vuuren, D. P. et al. A new scenario framework for Climate Change Research: scenario matrix architecture. Clim. Change 122, 373–386. https://doi.org/10.1007/s10584-013-0906-1 (2014).Article 

    Google Scholar 
    82.Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang. 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002 (2017).Article 

    Google Scholar 
    83.O’Neill, B. C. et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 42, 169–180 (2017).Article 

    Google Scholar 
    84.Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang. 48, 119–135 (2018).Article 

    Google Scholar  More

  • in

    Topography modulates near-ground microclimate in the Mediterranean Fagus sylvatica treeline

    1.Jones, C. G., Lawton, J. H., & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management 130–147 (Springer, 1994).2.Alvarez-Uria, P. & Körner, C. Low temperature limits of root growth in deciduous and evergreen temperate tree species. Funct. Ecol. 21, 211–218 (2007).Article 

    Google Scholar 
    3.Rossi, S. et al. Pattern of xylem phenology in conifers of cold ecosystems at the Northern Hemisphere. Glob. Chang. Biol. 22, 3804–3813 (2016).PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    4.Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).Article 

    Google Scholar 
    5.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    6.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    7.Albrich, K., Rammer, W. & Seidl, R. Climate change causes critical transitions and irreversible alterations of mountain forests. Glob. Change Biol. 26, 4013–4027 (2020).Article 
    ADS 

    Google Scholar 
    8.De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. PNAS 110, 18561–18565 (2013).PubMed 
    Article 
    ADS 
    CAS 
    PubMed Central 

    Google Scholar 
    9.Maclean, I. M. D. et al. Microclimates buffer the responses of plant communities to climate change. Glob. Ecol. Biogeogr. 24, 1340–1350 (2015).Article 

    Google Scholar 
    10.Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    11.Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory vegetation is more related to soil than to climate towards the cold distribution margin of European beech. J. Veg Sci. 30, 746–755 (2019).Article 

    Google Scholar 
    12.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    13.Dozier, J. & Outcalt, S. I. An approach toward energy balance simulation over rugged terrain. Geogr. Anal. 11, 65–85 (1979).Article 

    Google Scholar 
    14.Rorison, I. H., Sutton, F. & Hunt, R. Local climate, topography and plant growth in Lathkill Dale NNR. I. A twelve-year summary of solar radiation and temperature. Plant Cell Environ. 9, 49–56 (1986).
    Google Scholar 
    15.Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

    Google Scholar 
    16.Baldocchi, D. D. & Xu, L. What limits evaporation from Mediterranean oak woodlands—The supply of moisture in the soil, physiological control by plants or the demand by the atmosphere?. Adv. Water Resour. 30, 2113–2122 (2007).Article 
    ADS 

    Google Scholar 
    17.Komatsu, H. Forest categorization according to dry-canopy evaporation rates in the growing season: Comparison of the Priestley-Taylor coefficient values from various observation sites. Hydrol. Process. 19, 3873–3896 (2005).Article 
    ADS 

    Google Scholar 
    18.Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Chang. Biol. 19, 1470–1481 (2013).PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    19.Aussenac, G. Interactions between forest stands and microclimate: Ecophysiological aspects and consequences for silviculture. Ann. For. Sci. 57, 287–301 (2000).Article 

    Google Scholar 
    20.von Arx, G., Dobbertin, M. & Rebetez, M. Spatio-temporal effects of forest canopy on understory microclimate in a long-term experiment in Switzerland. Agric. For. Meteorol. 166, 144–155 (2012).Article 
    ADS 

    Google Scholar 
    21.Gaudio, N. et al. Impact of tree canopy on thermal and radiative microclimates in a mixed temperate forest: A new statistical method to analyse hourly temporal dynamics. Agric. For. Meteorol. 237, 71–79 (2017).Article 
    ADS 

    Google Scholar 
    22.Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).Article 

    Google Scholar 
    23.Breshears, D. D., Myers, O. B. & Barnes, F. J. Horizontal heterogeneity in the frequency of plant-available water with woodland intercanopy-canopy vegetation patch type rivals that occurring vertically by soil depth. Ecohydrology 2, 503–519 (2009).Article 

    Google Scholar 
    24.Zou, C. B., Barron-Gafford, G. A. & Breshears, D. D. Effects of topography and woody plant canopy cover on near-ground solar radiation: Relevant energy inputs for ecohydrology and hydropedology. Geophys. Res. Lett. 34, L24S21 (2007).Article 

    Google Scholar 
    25.Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol. 105, 119–127 (2011).Article 
    ADS 

    Google Scholar 
    26.De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Harsch, M. A. & Bader, M. Y. Treeline form—A potential key to understanding treeline dynamics. Glob. Ecol. Biogeogr. 20, 582–596 (2011).Article 

    Google Scholar 
    28.Körner, C. et al. Where, why and how? Explaining the low-temperature range limits of temperate tree species. J. Ecol. 104, 1076–1088 (2016).Article 
    CAS 

    Google Scholar 
    29.Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: Implications for species redistribution. Ecography 40, 253–266 (2017).Article 

    Google Scholar 
    30.Bonanomi, G. et al. Anthropogenic and environmental factors affect the tree line position of Fagus sylvatica along the Apennines (Italy). J. Biogeogr. 45, 2595–2608 (2018).Article 

    Google Scholar 
    31.Bonanomi, G. et al. Climatic and anthropogenic factors explain the variability of Fagus sylvatica treeline elevation in fifteen mountain groups across the Apennines. For. Ecosyst. 7, 5 (2020).Article 

    Google Scholar 
    32.Driessen, P., Deckers, J., Spaargaren, O. & Nachtergaele, F. (Eds.). Lecture notes on the major soils of the world. In World Soil Resources Report; No. 94. (Food and Agricultural Organization of the United Nations, 2001).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    34.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2019).35.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).
    Google Scholar 
    36.Davis, K. T., Dobrowski, S. Z., Holden, Z. A., Higuera, P. E. & Abatzoglou, J. T. Microclimatic buffering in forests of the future: The role of local water balance. Ecography 42, 1–11 (2019).Article 

    Google Scholar 
    37.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.15. https://CRAN.R-project.org/package=MuMIn (2019).38.Geiger, R., Aron, R. H. & Todhunter, P. The Climate near the Ground (Rowman & Littlefield Publishers, 2003).
    Google Scholar 
    39.Bader, M., Rietkerk, M. & Bregt, A. Vegetation structure and temperature regimes of tropical alpine treelines. Arct. Antarct. Alp. Res. 39, 353–364 (2007).Article 

    Google Scholar 
    40.Potter, B. E., Teclaw, R. M. & Zasada, J. C. The impact of forest structure on near-ground temperatures during two years of contrasting temperature extremes. Agric. For. Meteorol. 106, 331–336 (2001).Article 
    ADS 

    Google Scholar 
    41.von Arx, G., Pannatier, E. G., Thimonier, A. & Rebetez, M. Microclimate in forests with varying leaf area index and soil moisture: Potential implications for seedling establishment in a changing climate. J. Ecol. 101, 1201–1213 (2013).Article 

    Google Scholar 
    42.Frey, B. R. et al. An analysis of sucker regeneration of trembling aspen. Can. J. For. Res. 33, 1169–1179 (2003).Article 

    Google Scholar 
    43.Lenz, A., Hoch, G. & Vitasse, Y. Fast acclimation of freezing resistance suggests no influence of winter minimum temperature on the range limit of European beech. Tree Physiol. 36, 490–501 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Keitel, C. et al. Carbon and oxygen isotope composition of organic compounds in the phloem sap provides a short-term measure for stomatal conductance of European beech (Fagus sylvatica L.). Plant Cell Environ. 26, 1157–1168 (2003).CAS 
    Article 

    Google Scholar 
    45.van der Maaten, E., Bouriaud, O., van der Maaten-Theunissen, M., Mayer, H. & Spiecker, H. Meteorological forcing of day-to-day stem radius variations of beech is highly synchronic on opposing aspects of a valley. Agric. For. Meteorol. 181, 85–93 (2013).Article 
    ADS 

    Google Scholar 
    46.Smith, D. L. & Johnson, L. Vegetation-mediated changes in microclimate reduce soil respiration as woodlands expand into grasslands. Ecology 85, 3348–3361 (2004).Article 

    Google Scholar 
    47.Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J. & Hungate, B. A. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation. Glob. Change Biol. 17, 927–942 (2011).Article 
    ADS 

    Google Scholar 
    48.Gehlhausen, S. M., Schwartz, M. W. & Augspurger, C. K. Vegetation and microclimatic edge effects in two mixed-mesophytic forest fragments. Plant Ecol. 147, 21–35 (2000).Article 

    Google Scholar 
    49.Hofmeister, J. et al. Microclimate edge effect in small fragments of temperate forests in the context of climate change. For. Ecol. Manag. 448, 48–56 (2019).Article 

    Google Scholar 
    50.Treml, V. & Banaš, M. The effect of exposure on alpine treeline position: A case study from the High Sudetes, Czech Republic. Arct. Antarct. Alp. Res. 40, 751–760 (2008).Article 

    Google Scholar 
    51.Zellweger, F. et al. Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Glob. Ecol. Biogeogr. 28, 1774–1786 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Frey, S. J. et al. Spatial models reveal the microclimatic buffering capacity of old-growth forests. Sci. Adv. 2, e1501392 (2016).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    53.Ashcroft, M. B. & Gollan, J. R. Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia. Agric. For. Meteorol. 176, 77–89 (2013).Article 
    ADS 

    Google Scholar 
    54.Holden, Z. A., Klene, A. E., Keefe, R. F. & Moisen, G. G. Design and evaluation of an inexpensive radiation shield for monitoring surface air temperatures. Agric. For. Meteorol. 180, 281–286 (2013).Article 
    ADS 

    Google Scholar 
    55.Maher, E. L., Germino, M. J. & Hasselquist, N. J. Interactive effects of tree and herb cover on survivorship, physiology, and microclimate of conifer seedlings at the alpine tree-line ecotone. Can. J. For. Res. 35, 567–574 (2005).Article 

    Google Scholar 
    56.Maher, E. L. & Germino, M. J. Microsite differentiation among conifer species during seedling establishment at alpine treeline. Ecoscience 13, 334–341 (2006).Article 

    Google Scholar 
    57.Mayor, J. R. et al. Elevation alters ecosystem properties across temperate treelines globally. Nature 542, 91–95 (2017).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    58.Allevato, E. et al. Canopy damage by spring frost in European beech along the Apennines: Effect of latitude, altitude and aspect. Remote Sens. Environ. 225, 431–440 (2019).Article 
    ADS 

    Google Scholar 
    59.Nolè, A., Rita, A., Ferrara, A. M. S. & Borghetti, M. Effects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI. Ann. For. Sci. 75, 83 (2018).Article 

    Google Scholar 
    60.Müller, M. et al. Soil temperature and soil moisture patterns in a Himalayan alpine treeline ecotone. Arct. Antarct. Alp. Res. 48, 501–521 (2016).Article 

    Google Scholar 
    61.Liechty, H. O., Holmes, M. J., Reed, D. D. & Mroz, G. D. Changes in microclimate after stand conversion in two northern hardwood stands. For. Ecol. Manag. 50, 253–264 (1992).Article 

    Google Scholar 
    62.Peterson, D. W. & Peterson, D. L. Mountain hemlock growth responds to climatic variability at annual and decadal time scales. Ecology 82, 3330–3345 (2001).Article 

    Google Scholar 
    63.Jarvis, P. et al. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: The “Birch effect”. Tree Physiol. 27, 929–940 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Binkley, D. & Fisher, R. F. Ecology and Management of Forest Soils (Wiley-Blackwell, 2013).
    Google Scholar  More

  • in

    Long-term patterns of cave-exiting activity of hibernating bats in western North America

    1.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 569–578. https://doi.org/10.1007/s00360-011-0631-x (2012).Article 

    Google Scholar 
    2.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).Article 
    PubMed 

    Google Scholar 
    3.Reynolds, D. S., Shoemaker, K., von Oettingen, S. & Najjar, S. High rates of winter activity and arousals in two New England bat species: Implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208 (2017).Article 

    Google Scholar 
    4.Kunz, T. H. & Martin, R. A. Plecotus townsendii. Mamm. Species 175, 1–6 (1982).
    Google Scholar 
    5.Twente, J. W. Aspects of a population study of cavern-dwelling bats. J. Mamm. 36, 379–390 (1955).Article 

    Google Scholar 
    6.Humphrey, S. R. & Kunz, T. H. Ecology of a Pleistocene relict, the western big-eared bat (Plecotus townsendii), in the southern Great Plains. J. Mamm. 57, 470–494. https://doi.org/10.2307/1379297 (1976).Article 

    Google Scholar 
    7.Czenze, Z. J., Park, A. D. & Willis, C. K. R. Staying cold through dinner: Cold-climate bats rewarm with conspecifics but not sunset during hibernation. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 183, 859–866. https://doi.org/10.1007/s00360-013-0753-4 (2013).Article 

    Google Scholar 
    8.Pearson, O. P., Koford, M. R. & Pearson, A. K. Reproduction of the lump-nosed bat (Corynorhinus rafinesquei) in California. J. Mamm. 33, 273–320 (1952).Article 

    Google Scholar 
    9.Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Lausen, C. L. & Barclay, R. M. R. Winter bat activity in the Canadian prairies. Can. J. Zool.-Rev. Can. Zool. 84, 1079–1086. https://doi.org/10.1139/z06-093 (2006).Article 

    Google Scholar 
    11.Thomas, D. W. & Cloutier, D. Evaporative water-loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

    Google Scholar 
    12.Ben-Hamo, M., Munoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577. https://doi.org/10.1242/jeb.078790 (2013).Article 
    PubMed 

    Google Scholar 
    13.Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

    Google Scholar 
    14.Choate, J. R. & Anderson, J. M. Bats of jewel cave national monument, South Dakota. Prairie Nat. 29, 39–47 (1997).
    Google Scholar 
    15.Klüg-Baerwald, B. J., Gower, L. E., Lausen, C. L. & Brigham, R. M. Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada. Can. J. Zool. 94, 829–836. https://doi.org/10.1139/cjz-2016-0055 (2016).Article 

    Google Scholar 
    16.Johnson, J. S. et al. Migratory and winter activity of bats in Yellowstone National Park. J. Mamm. 98, 211–221. https://doi.org/10.1093/jmammal/gyw175 (2017).Article 

    Google Scholar 
    17.Norquay, K. & Willis, C. Hibernation phenology of Myotis lucifugus. J. Zool. 294, 85–92 (2014).Article 

    Google Scholar 
    18.Barclay, R. M. et al. Variation in the reproductive rate of bats. Can. J. Zool. 82, 688–693 (2004).Article 

    Google Scholar 
    19.Jonasson, K. A. & Willis, C. K. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061. https://doi.org/10.1371/journal.pone.0021061 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).Article 

    Google Scholar 
    21.Reeder, D. M., Field, K. A. & Slater, M. H. Balancing the costs of wildlife research with the benefits of understanding a panzootic disease, white-nose syndrome. ILAR J. 56, 275–282. https://doi.org/10.1093/ilar/ilv035 (2015).CAS 
    Article 

    Google Scholar 
    22.Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

    Google Scholar 
    23.Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mamm. 76, 940–946. https://doi.org/10.2307/1382764 (1995).Article 

    Google Scholar 
    24.Furey, N. M. & Racey, P. A. Bats in the Anthropocene: Conservation of Bats in a Changing World 463–500 (Springer, 2016).
    Google Scholar 
    25.Sheffield, S. R., Shaw, J. H., Heidt, G. A. & McClenaghan, L. R. Guidelines for the protection of bat roosts. J. Mamm. 73, 707–710 (1992).
    Google Scholar 
    26.Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R. & Racey, P. A. Carpe noctem: The importance of bats as bioindicators. Endang. Species Res. 8, 93–115 (2009).Article 

    Google Scholar 
    27.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    28.Foley, J., Clifford, D., Castle, K., Cryan, P. & Ostfeld, R. S. Investigating and managing the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231. https://doi.org/10.1111/j.1523-1739.2010.01638.x (2011).Article 
    PubMed 

    Google Scholar 
    29.Ingersoll, T. E., Sewall, B. J. & Amelon, S. K. Effects of white-nose syndrome on regional population patterns of 3 hibernating bat species. Conserv. Biol. 30, 1048–1059. https://doi.org/10.1111/cobi.12690 (2016).Article 
    PubMed 

    Google Scholar 
    30.Minnis, A. M. & Lindner, D. L. Phylogenetic evaluation of Geomyces and allies reveals no close relatives of Pseudogymnoascus destructans, comb. nov., in bat hibernacula of eastern North America. Fungal Biol. 117, 638–649. https://doi.org/10.1016/j.funbio.2013.07.001 (2013).Article 
    PubMed 

    Google Scholar 
    31.Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    32.Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

    Google Scholar 
    33.Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. U.S.A. 109, 6999–7003. https://doi.org/10.1073/pnas.1200374109 (2012).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Lilley, T. M. et al. White-nose syndrome survivors do not exhibit frequent arousals associated with Pseudogymnoascus destructans infection. Front. Zool. https://doi.org/10.1186/s12983-016-0143-3 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 313, R680–R686. https://doi.org/10.1152/ajpregu.00058.2017 (2017).CAS 
    Article 

    Google Scholar 
    36.Knudsen, G. R., Dixon, R. D. & Amelon, S. K. Potential spread of white-nose syndrome of bats to the Northwest: Epidemiological considerations. Northwest Sci. 87, 292–306. https://doi.org/10.3955/046.087.0401 (2013).Article 

    Google Scholar 
    37.Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Cheng, T. L. et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. J. Anim. Ecol. 88, 591–600 (2019).Article 

    Google Scholar 
    39.Turner, J. M. et al. Conspecific disturbance contributes to altered hibernation patterns in bats with white-nose syndrome. Physiol. Behav. 140, 71–78 (2015).CAS 
    Article 

    Google Scholar 
    40.Blazek, J. et al. Numerous cold arousals and rare arousal cascades as a hibernation strategy in European Myotis bats. J. Therm. Biol 82, 150–156. https://doi.org/10.1016/j.jtherbio.2019.04.002 (2019).Article 
    PubMed 

    Google Scholar 
    41.Lorch, J. M. et al. First detection of bat white-nose syndrome in Western North America. mSphere 1(4), e00148. https://doi.org/10.1128/mSphere.00148-16 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS ONE https://doi.org/10.1371/journal.pone.0205647 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Whiting, J. C. et al. Bat hibernacula in caves of southern Idaho: Implications for monitoring and management. West. N. Am. Nat. 78, 165–173 (2018).Article 

    Google Scholar 
    44.Whiting, J. C. et al. Long-term bat abundance in sagebrush steppe. Sci. Rep. 8, 12288 (2018).ADS 
    Article 

    Google Scholar 
    45.Call, R. S. et al. Maternity roosts of Townsend’s big-eared bats in lava tube caves of southern Idaho. Northwest Sci. 92, 158–165 (2018).ADS 
    Article 

    Google Scholar 
    46.Clark, B. S., Clark, B. K. & Leslie, D. M. Seasonal variation in activity patterns of the endangered Ozark big-eared bat (Corynorhinus townsendii ingens). J. Mamm. 83, 590–598. https://doi.org/10.1644/1545-1542(2002)083%3c0590:sviapo%3e2.0.co;2 (2002).Article 

    Google Scholar 
    47.French, A. R. The patterns of mammalian hibernation. Am. Sci. 76, 568–575 (1988).ADS 

    Google Scholar 
    48.Reynolds, T. D., Connelly, J. W., Halford, D. K. & Arthur, W. J. Vertebrate fauna of the Idaho National Environmental Research Park. Gt. Basin Nat. 46, 513–527 (1986).
    Google Scholar 
    49.Genter, D. L. Wintering bats of the upper Snake River Plain: Occurrence in lava-tube caves. Gt. Basin Nat. 46, 241–244 (1986).
    Google Scholar 
    50.Gillies, K. E., Murphy, P. J. & Matocq, M. D. Hibernacula characteristics of Townsend’s big-eared bats in southeastern Idaho. Nat. Areas J. 34, 24–30 (2014).Article 

    Google Scholar 
    51.Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97(663–688), 2016. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

    Google Scholar 
    52.Schwab, N. A. & Mabee, T. J. Winter acoustic activity of bats in Montana. Northwest. Nat. 95, 13–27 (2014).Article 

    Google Scholar 
    53.Britzke, E. R., Slack, B. A., Armstrong, M. P. & Loeb, S. C. Effects of orientation and weatherproofing on the detection of bat echolocation calls. J. Fish Wildl. Manage. 1, 136–141. https://doi.org/10.3996/072010-jfwm-025 (2010).Article 

    Google Scholar 
    54.Skalak, S. L., Sherwin, R. E. & Brigham, R. M. Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods Ecol. Evol. 3, 490–502. https://doi.org/10.1111/j.2041-210X.2011.00177.x (2012).Article 

    Google Scholar 
    55.Miller, B. W. A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring. Acta Chiropt. 3, 93–105 (2001).
    Google Scholar 
    56.Nocera, T., Ford, W. M., Silvis, A. & Dobony, C. A. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort drum army installation, New York. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00633 (2019).Article 

    Google Scholar 
    57.Britzke, E. R., Gillam, E. H. & Murray, K. L. Current state of understanding of ultrasonic detectors for the study of bat ecology. Acta Theriol. 58, 109–117. https://doi.org/10.1007/s13364-013-0131-3 (2013).Article 

    Google Scholar 
    58.O’Farrell, M. J., Miller, B. W. & Gannon, W. L. Qualitative identification of free-flying bats using the Anabat detector. J. Mamm. 80, 11–23. https://doi.org/10.2307/1383203 (1999).Article 

    Google Scholar 
    59.Whiting, J. C., Doering, B. & Pennock, D. Acoustic surveys for local, free-flying bats in zoos: An engaging approach for bat education and conservation. J. Bat Res. Conserv. 12, 94–99. https://doi.org/10.14709/BarbJ.12.1.2019.12 (2019).Article 

    Google Scholar 
    60.O’Farrell, M. J. & Gannon, W. L. A comparison of acoustic versus capture techniques for the inventory of bats. J. Mamm. 80, 24–30. https://doi.org/10.2307/1383204 (1999).Article 

    Google Scholar 
    61.Stahlschmidt, P. & Bruhl, C. A. Bats as bioindicators—The need of a standardized method for acoustic bat activity surveys. Methods Ecol. Evol. 3, 503–508. https://doi.org/10.1111/j.2041-210X.2012.00188.x (2012).Article 

    Google Scholar 
    62.Avery, M. I. Winter activity of pipistrelle bats. J. Anim. Ecol. 54, 721–738. https://doi.org/10.2307/4374 (1985).Article 

    Google Scholar 
    63.McCulloch, C. E. & Neuhaus, J. M. Generalized linear mixed models. In Encyclopedia of Biostatistics (eds Armitage, P. & Colton, T.) (Wiley, 2005).
    Google Scholar 
    64.Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (Gen.) 135, 370–384 (1972).Article 

    Google Scholar 
    65.Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, 2007).
    Google Scholar 
    66.Consul, P. & Famoye, F. Generalized Poisson regression model. Commun. Stat. Theory Methods 21, 89–109 (1992).Article 

    Google Scholar 
    67.Aho, K. A. Foundational and Applied Statistics for Biologists using R (CRC Press, 2013).
    Google Scholar 
    68.Akaike, H. Selected Papers of Hirotugu Akaike 199–213 (Springer, 1998).
    Google Scholar 
    69.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A practical Information-Theoretic Approach 2nd edn. (Springer, 2002).
    Google Scholar 
    70.RCoreTeam. R: A Language and Environment for Statistical Computing (2020).71.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer, 2013).
    Google Scholar 
    72.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

    Google Scholar 
    73.Perkins, J. M., Barss, J. M. & Peterson, J. Winter records of bats in Oregon and Washington. Northwest. Nat. 71, 59–62. https://doi.org/10.2307/3536594 (1990).Article 

    Google Scholar 
    74.Nagorsen, D. W. et al. Winter bat records for British Columbia. Northwest Nat. 74, 61–66 (1993).Article 

    Google Scholar 
    75.Hayman, D. T., Cryan, P. M., Fricker, P. D. & Dannemiller, N. G. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol. Evol. 8, 1813–1821 (2017).Article 

    Google Scholar 
    76.Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

    Google Scholar 
    77.Speakman, J. R. & Racey, P. A. Hibernal ecology of the pipistrelle bat: Energy expenditure, water requirements and mass-loss, implications for survial and the function of winter emergence flights. J. Anim. Ecol. 58, 797–813. https://doi.org/10.2307/5125 (1989).Article 

    Google Scholar 
    78.Lawrence, B. D. & Simmons, J. A. Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats. J. Acoust. Soc. Am. 71, 585–590 (1982).ADS 
    CAS 
    Article 

    Google Scholar 
    79.Dunbar, M. B. & Tomasi, T. E. Arousal patterns, metabolic rate, and an energy budget of eastern red bats (Lasiurus borealis) in winter. J. Mamm. 87, 1096–1102. https://doi.org/10.1644/05-mamm-a-254r3.1 (2006).Article 

    Google Scholar 
    80.Ford, W. M., Britzke, E. R., Dobony, C. A., Rodrigue, J. L. & Johnson, J. B. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. J. Fish Wildl. Manage. 2, 125–134. https://doi.org/10.3996/042011-jfwm-027 (2011).Article 

    Google Scholar 
    81.Bernard, R. F., Foster, J. T., Willcox, E. V., Parise, K. L. & McCracken, G. F. Molecular detection of the causative agent of white-nose syndrome on Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and two species of migratory bats in the southeastern USA. J. Wildl. Dis. 51, 519–522. https://doi.org/10.7589/2014-08-202 (2015).Article 
    PubMed 

    Google Scholar 
    82.Dzal, Y., McGuire, L. P., Veselka, N. & Fenton, M. B. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biol. Lett. 7, 392–394 (2010).Article 

    Google Scholar 
    83.Brooks, R. T. Declines in summer bat activity in central New England 4 years following the initial detection of white-nose syndrome. Biodivers. Conserv. 20, 2537–2541. https://doi.org/10.1007/s10531-011-9996-0 (2011).Article 

    Google Scholar 
    84.Holloway, G. L. & Barclay, R. M. R. Myotis ciliolabrum. Mamm. Species 670, 1–5. https://doi.org/10.1644/1545-1410(2001)670%3c0001:mc%3e2.0.co;2 (2001).Article 

    Google Scholar 
    85.Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big brown bats during winter in a building hibernaculum. J. Mamm. 93, 497–503 (2012).Article 

    Google Scholar 
    86.Paige, K. N. Bats and barometric pressure: conserving limited energy and tracking insects from the roost. Funct. Ecol. 9, 463–467 (1995).Article 

    Google Scholar 
    87.Frick, W. F. Acoustic monitoring of bats, considerations of options for long-term monitoring. Therya 4, 69–78 (2013).ADS 
    Article 

    Google Scholar 
    88.Whitaker, J. O. & Rissler, L. J. Winter activity of bats at a mine entrance in Vermillion County, Indiana. Am. Midl. Nat. 127, 52–59. https://doi.org/10.2307/2426321 (1992).Article 

    Google Scholar  More

  • in

    Stock delineation of striped snakehead, Channa striata using multivariate generalised linear models with otolith shape and chemistry data

    1.Carlson, A. K., Phelps, Q. E. & Graeb, B. D. S. Chemistry to conservation: Using otoliths to advance recreational and commercial fisheries management. J. Fish Biol. 90, 505–527 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Ward, R. D. Genetics in fisheries management. Hydrobiologia 420, 191–201 (2000).CAS 
    Article 

    Google Scholar 
    3.Tracey, S. R., Lyle, J. M. & Duhamel, G. Application of elliptical Fourier analysis of otolith form as a tool for stock identification. Fish. Res. 77, 138–147 (2006).Article 

    Google Scholar 
    4.Ferguson, G. J., Ward, T. M. & Gillanders, B. M. Otolith shape and elemental composition: Complementary tools for stock discrimination of mulloway (Argyrosomus japonicus) in southern Australia. Fish. Res. 110, 75–83 (2011).Article 

    Google Scholar 
    5.Campana, S. E. & Casselman, J. M. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50(5), 1062-1083 (1993).Article 

    Google Scholar 
    6.Begg, G. A., Overholtz, W. J. & Munroe, N. J. The use of internal otolith morphometrics for identification of haddock (Melanogrammus aeglefinus) stocks on Georges Bank. Fish. Bull. 99, 1–1 (2001).
    Google Scholar 
    7.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Ansari, N. G. Truss morphometry and otolith microchemistry reveal stock discrimination in Clarias batrachus (Linnaeus, 1758) inhabiting the Gangetic river system. Fish. Res. 173, 294–302 (2016).Article 

    Google Scholar 
    8.Nazir, A. & Khan, M. A. Spatial and temporal variation in otolith chemistry and its relationship with water chemistry: Stock discrimination of Sperata aor. Ecol. Freshw. Fish 28, 499–511 (2019).Article 

    Google Scholar 
    9.Bird, J. L., Eppler, D. T. & Checkley, D. M. Jr. Comparisons of herring otoliths using Fourier series shape analysis. Can. J. Fish. Aquat. Sci. 43(6), 1228-1234 (1986).Article 

    Google Scholar 
    10.Castonguay, M., Simard, P. & Gagnon, P. Usefulness of Fourier analysis of otolith shape for Atlantic Mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48(2), 296-302 (1991).Article 

    Google Scholar 
    11.Friedland, K. D. & Reddin, D. G. Use of otolith morphology in stock discriminations of Atlantic Salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 51(1), 91-98 (1994).Article 

    Google Scholar 
    12.Vignon, M. & Morat, F. Environmental and genetic determinant of otolith shape revealed by a non-indigenous tropical fish. Mar. Ecol. Prog. Ser. 411, 231–241 (2010).ADS 
    Article 

    Google Scholar 
    13.Campana, S. E., Chouinard, G. A., Hanson, J. M., Fréchet, A. & Brattey, J. Otolith elemental fingerprints as biological tracers of fish stocks. Fish. Res. 46, 343–357 (2000).Article 

    Google Scholar 
    14.Elsdon, T. S. & Gillanders, B. M. Reconstructing migratory patterns of fish based on environmental influences on otolith chemistry. Rev. Fish Biol. Fish. 13, 217–235 (2003).Article 

    Google Scholar 
    15.Stransky, C. Geographic variation of golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella) in the North Atlantic based on otolith shape analysis. ICES J. Mar. Sci. 62, 1691–1698 (2005).Article 

    Google Scholar 
    16.Grammer, G. L. et al. Coupling biogeochemical tracers with fish growth reveals physiological and environmental controls on otolith chemistry. Ecol. Monogr. 87, 487–507 (2017).Article 

    Google Scholar 
    17.Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454 (2018).Article 

    Google Scholar 
    18.Elsdon, T. S. & Gillanders, B. M. Fish otolith chemistry influenced by exposure to multiple environmental variables. J. Exp. Mar. Biol. Ecol. 313, 269–284 (2004).CAS 
    Article 

    Google Scholar 
    19.Khan, M. A., Miyan, K., Khan, S., Patel, D. K. & Ansari, G. Studies on the elemental profile of otoliths and truss network analysis for stock discrimination of the threatened stinging catfish Heteropneustes fossilis (Bloch 1794) from the Ganga river and its tributaries. Zool. Stud. 51, 1195–1206 (2012).
    Google Scholar 
    20.Miyan, K., Khan, M. A. & Khan, S. Stock structure delineation using variation in otolith chemistry of snakehead, Channa punctata (Bloch, 1793), from three Indian rivers. J. Appl. Ichthyol. 30, 881–886 (2014).CAS 
    Article 

    Google Scholar 
    21.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Prasad, S. Otolith fingerprints reveal stock discrimination of Sperata seenghala inhabiting the Gangetic river system. Ichthyol. Res. 63, 294–301 (2016).Article 

    Google Scholar 
    22.Fowler, A. M., Macreadie, P. I., Bishop, D. P. & Booth, D. J. Using otolith microchemistry and shape to assess the habitat value of oil structures for reef fish. Mar. Environ. Res. 106, 103–113 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Schilling, H. T. et al. Evaluating estuarine nursery use and life history patterns of Pomatomus saltatrix in eastern Australia. Mar. Ecol. Prog. Ser. 598, 187–199 (2018).ADS 
    Article 

    Google Scholar 
    24.Biolé, F. G. et al. Fish stocks of Urophycis brasiliensis revealed by otolith fingerprint and shape in the Southwestern Atlantic Ocean. Estuar. Coast. Shelf Sci. 229, 106406 (2019).Article 
    CAS 

    Google Scholar 
    25.Maguffee, A. C., Reilly, R., Clark, R. & Jones, M. L. Examining the potential of otolith chemistry to determine natal origins of wild Lake Michigan Chinook salmon. Can. J. Fish. Aquat. Sci. 76(11), 2035-2044 (2019).Article 

    Google Scholar 
    26.Tanner, S. E., Vasconcelos, R. P., Cabral, H. N. & Thorrold, S. R. Testing an otolith geochemistry approach to determine population structure and movements of European hake in the northeast Atlantic Ocean and Mediterranean Sea. Fish. Res. 125–126, 198–205 (2012).Article 

    Google Scholar 
    27.Andrade, H. et al. Ontogenetic movements of cod in Arctic fjords and the Barents Sea as revealed by otolith microchemistry. Polar Biol. 43, 409–421 (2020).Article 

    Google Scholar 
    28.Warton, D. I. Why you cannot transform your way out of trouble for small counts. Biometrics 74, 362–368 (2018).MathSciNet 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    29.Foster, S. D. & Bravington, M. V. A Poisson-Gamma model for analysis of ecological non-negative continuous data. Environ. Ecol. Stat. 20, 533–552 (2013).MathSciNet 
    Article 

    Google Scholar 
    30.Taylor, L. R. Aggregation, variance and the mean. Nature 189, 732–735 (1961).ADS 
    Article 

    Google Scholar 
    31.Kendal, R. L., Coolen, I. & Laland, K. N. The role of conformity in foraging when personal and social information conflict. Behav. Ecol. 15, 269–277 (2004).Article 

    Google Scholar 
    32.Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).Article 

    Google Scholar 
    33.Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J. & Dunstan, P. K. Model-based thinking for community ecology. Plant Ecol. 216, 669–682 (2015).Article 

    Google Scholar 
    34.Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund– an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).Article 

    Google Scholar 
    35.Niku, J., Warton, D. I., Hui, F. K. C. & Taskinen, S. Generalized linear latent variable models for multivariate count and biomass data in ecology. J. Agric. Biol. Environ. Stat. 22, 498–522 (2017).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    36.Dunn, P. K. & Smyth, G. K. Randomized quantile residuals. J. Comput. Graph. Stat. 5, 236–244 (1996).
    Google Scholar 
    37.Dunn, P. K. & Smyth, G. K. Chapter 8: generalized linear models: Diagnostics. In Generalized Linear Models With Examples in R (eds. Dunn, P. K. & Smyth, G. K.) 297–331 (Springer, 2018). https://doi.org/10.1007/978-1-4419-0118-7_8.38.Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D. & Warton, D. I. Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411 (2015).Article 

    Google Scholar 
    39.Hui, F. K. C. Boral–Bayesian ordination and regression analysis of multivariate abundance Data in r. Methods Ecol. Evol. 7, 744–750 (2016).Article 

    Google Scholar 
    40.Popovic, G. C., Warton, D. I., Thomson, F. J., Hui, F. K. C. & Moles, A. T. Untangling direct species associations from indirect mediator species effects with graphical models. Methods Ecol. Evol. 10, 1571–1583 (2019).Article 

    Google Scholar 
    41.Jones, C. M., Palmer, M. & Schaffler, J. J. Beyond Zar: The use and abuse of classification statistics for otolith chemistry. J. Fish Biol. 90, 492–504 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Rahman, M. A. & Awal, S. Development of captive breeding, seed production and culture techniques of snakehead fish for species conservation and sustainable aquaculture. Int. J. Adv. Agric. Environ. Eng. 3, 117–120 (2016).
    Google Scholar 
    43.Khan, M. A., Khan, S. & Miyan, K. Stock identification of the Channa striata inhabiting the Gangetic River System using Truss Morphometry. Russ. J. Ecol. 50, 391–396 (2019).Article 

    Google Scholar 
    44.Phen, C., Thang, T. B., Baran, E. & Vann, L. S. Biological reviews of important Cambodian fish species, based on FishBase 2004. Volume 1: Channa striata; Channa micropeltes; Barbonymus altus; Barbonymus gonionotus; Cyclocheilichthys apogon; Cyclocheilichthys enoplos; Henicorhynchus lineatus; Henicorhynchus siamensis; Pangasius hypophthalmus; Pangasius djambal. (WorldFish Center and Inland Fisheries Research and Development Institute, 2005).45.War, M. & Haniffa, M. A. Growth and survival of larval snakehead Channa striatus (Bloch, 1793) fed different live feed organisms. Turk. J. Fish. Aquat. Sci. 11, 523–528 (2011).
    Google Scholar 
    46.Cagauan, A. G. Exotic aquatic species introduction in the Philippines for aquaculture—A threat to biodiversity or a boon to the economy?. J. Environ. Sci. Manag. 10, 48–62 (2007).
    Google Scholar 
    47.Jayaram, K. C. The Freshwater Fishes of the Indian Region (Narendra Publishing House, 1999).
    Google Scholar 
    48.Talwar, P. K. & Jhingran, A. G. Inland fishes of India and adjacent countries Vol. 2 (CRC Press, 1991).
    Google Scholar 
    49.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).50.Libungan, L. A. & Pálsson, S. ShapeR: An R package to study otolith shape variation among fish populations. PLoS ONE 10, e0121102 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).Article 

    Google Scholar 
    52.Turan, C. The use of otolith shape and chemistry to determine stock structure of Mediterranean horse mackerel Trachurus mediterraneus (Steindachner). J. Fish Biol. 69, 165–180 (2006).CAS 
    Article 

    Google Scholar 
    53.Oksanen, J. vegan: Community Ecology Package. (2019).54.Venables, W. N. & Ripley, B. D. Modern applied statistics with S-PLUS (Springer Science & Business Media, 2013).
    Google Scholar 
    55.Warton, D. I. Raw data graphing: An informative but under-utilized tool for the analysis of multivariate abundances. Austral. Ecol. 33, 290–300 (2008).Article 

    Google Scholar 
    56.Begg, G. A., Friedland, K. D. & Pearce, J. B. Stock identification and its role in stock assessment and fisheries management: An overview. Fish. Res. 43, 1–8 (1999).Article 

    Google Scholar 
    57.Sengupta, B. Water Quality Status of Yamuna River (1999-2005), Assessment and Development of River Basin Series: ADSORBS/41/2006-07. Cent. Pollut. Control Board Delhi (2006).58.Bhardwaj, R., Gupta, A. & Garg, J. K. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Sci. 31, 52–66 (2017).Article 

    Google Scholar  More