More stories

  • in

    Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine

    Zhao, Q. G., Huang, G. Q. & Ma, Y. Q. The ecological environment conditions and construction of an ecological civilization in China. Acta Ecol. Sin. 36, 6328–6335 (2016).
    Google Scholar 
    Jiang, Y. China’s water scarcity. J. Environ. Manag. 90, 3185–3196 (2009).Article 

    Google Scholar 
    Jacob, D. J. & Winner, D. A. Effect of climate change on air quality. Atmos. Environ. 43, 51–63 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Shahmohamadi, P., Che-Ani, A. I., Ramly, A., Maulud, K. N. A. & Mohd-Nor, M. F. I. Reducing urban heat island effects: A systematic review to achieve energy consumption balance. Int. J. Phys. Sci. 5, 626–636 (2010).
    Google Scholar 
    Shan, W. et al. Ecological environment quality assessment based on remote sensing data for land consolidation. J. Clean. Prod. 239, 118126 (2019).Article 

    Google Scholar 
    Cheng, R. et al. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest. Biogeosciences 17, 4523–4544 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Ochoa-Gaona, S. et al. A multi-criterion index for the evaluation of local tropical forest conditions in Mexico. For. Ecol. Manag. 260, 618–627 (2010).Article 

    Google Scholar 
    Zuromski, L. M. et al. Solar-induced fluorescence detects interannual variation in gross primary production of coniferous forests in the western United States. Geophys. Res. Lett. 45, 7184–7193 (2018).Article 
    ADS 

    Google Scholar 
    Wingard, G. L. & Lorenz, J. J. Integrated conceptual ecological model and habitat indices for the southwest Florida coastal wetlands. Ecol. Ind. 44, 92–107 (2014).Article 

    Google Scholar 
    Zhou, X. H., Zhang, F., Zhang, H. W., Zhang, X. L. & Yuan, J. A study of soil salinity inversion based on multispectral remote sensing index in Ebinur lake wetland nature reserve. Spectrosc. Spectral Anal. 39, 1229–1235 (2019).CAS 

    Google Scholar 
    Jiang, M. Z., Chen, H. Y., Chen, Q. H., Wu, H. Y. & Chen, P. Wetland ecosystem integrity and its variation in an estuary using the EBLE index. Ecol. Ind. 48, 252–262 (2015).Article 

    Google Scholar 
    Lv, J. X. et al. Wetland loss identification and evaluation based on landscape and remote sensing indices in Xiong’an new area. Remote Sens. 11, 2834 (2019).Article 
    ADS 

    Google Scholar 
    Bi, X. et al. Assessment of spatio-temporal variation and driving mechanism of ecological environment quality in the arid regions of Central Asia, Xinjiang. Int. J. Environ. Res. Public Health 18, 7111 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leroux, L. et al. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. Eur. J. Agron. 108, 11–26 (2019).Article 

    Google Scholar 
    Liran, O., Shir, O. M., Levy, S., Grunfeld, A. & Shelly, Y. Novel remote sensing index of electron transport rate predicts primary production and crop health in L. sativa and Z. mays. Remote Sens. 12, 1718 (2020).Article 
    ADS 

    Google Scholar 
    Zang, Y. Z. et al. Remote sensing index for mapping canola flowers using MODIS data. Remote Sens. 12, 3912 (2020).Article 
    ADS 

    Google Scholar 
    Jia, T. X., Zhang, X. Q. & Dong, R. C. Long-term spatial and temporal monitoring of cyanobacteria blooms using MODIS on Google Earth Engine: A case study in Taihu lake. Remote Sens. 11, 2269 (2019).Article 
    ADS 

    Google Scholar 
    Bai, Y. Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data. Ecol. Ind. 129, 108029 (2021).Article 

    Google Scholar 
    Zhang, M., Lin, H., Long, X. R. & Cai, Y. T. Analyzing the spatiotemporal pattern and driving factors of wetland vegetation changes using 2000–2019 time-series Landsat data. Sci. Total Environ. 780, 146615 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Qu, C., Li, P. J. & Zhang, C. M. A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS J. Photogramm. Remote Sens. 175, 431–447 (2021).Article 
    ADS 

    Google Scholar 
    Fu, Y. C., Lu, X. Y., Zhao, Y. L., Zeng, X. T. & Xia, L. L. Assessment impacts of weather and land use/land cover (LULC) change on urban vegetation net primary productivity (NPP): A case study in Guangzhou, China. Remote Sens. 5, 4125–4144 (2013).Article 
    ADS 

    Google Scholar 
    Kulkarni, K. & Vijaya, P. NDBI based prediction of land use land cover change. J. Indian Soc. Remote Sens. 49, 2523–2537 (2021).Article 

    Google Scholar 
    Li, C. Y. & Zhang, N. Analysis of the daytime urban heat island mechanism in East China. J. Geophys. Res.-Atmos. 126, 2020 (2021).
    Google Scholar 
    Wang, Z. A. et al. Environmental and anthropogenic drivers of surface urban heat island intensity: A case-study in the Yangtze River Delta, China. Ecol. Indic. 128, 107845 (2021).Article 

    Google Scholar 
    Zhao, Y. J. et al. Impact of urban expansion on rain island effect in Jinan City, North China. Remote Sens. 13, 2989 (2021).Article 
    ADS 

    Google Scholar 
    Xu, H. Q. A remote sensing urban ecological index and its application. Acta Ecol. Sin. 33, 7853–7862 (2013).
    Google Scholar 
    Gou, R. K. & Zhao, J. Eco-environmental quality monitoring in Beijing, China, using an RSEI-based approach combined with random forest algorithms. IEEE Access 8, 196657–196666 (2020).Article 

    Google Scholar 
    Jing, Y. Q. et al. Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecol. Indic. 110, 107518 (2020).Article 

    Google Scholar 
    Airiken, M., Zhang, F., Chan, N. W. & Kung, H. T. Assessment of spatial and temporal ecological environment quality under land use change of urban agglomeration in the North Slope of Tianshan, China. Environ. Sci. Pollut. Res. 29, 12282–12299 (2022).Article 

    Google Scholar 
    Ji, J. W., Wang, S. X., Zhou, Y., Liu, W. L. & Wang, L. T. Studying the eco-environmental quality variations of Jing-Jin-Ji urban agglomeration and its driving factors in different ecosystem service regions from 2001 to 2015. IEEE Access 8, 154940–154952 (2020).Article 

    Google Scholar 
    Liu, Z. S., Wang, L. Y. & Li, B. Quality assessment of ecological environment based on Google Earth Engine: A case study of the Zhoushan Islands. Front. Ecol. Evol. 10, 918756 (2022).Article 

    Google Scholar 
    Xiong, Y. et al. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecol. Indic. 125, 107518 (2021).Article 

    Google Scholar 
    Zhang, Q. F. et al. Recent oasis dynamics and ecological security in the Tarim River Basin, Central Asia. Sustainability 14, 3372 (2022).Article 

    Google Scholar 
    Yuan, B. D. et al. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. J. Clean. Prod. 302, 126995 (2021).Article 

    Google Scholar 
    Gao, W. L., Zhang, S. W., Rao, X. Y., Lin, X. & Li, R. S. Landsat TM/OLI-based ecological and environmental quality survey of Yellow River Basin, Inner Mongolia section. Remote Sens. 13, 4477 (2021).Article 
    ADS 

    Google Scholar 
    Zhu, Q. et al. Relationship between ecological quality and ecosystem services in a red soil hilly watershed in southern China. Ecol. Ind. 121, 107119 (2021).Article 

    Google Scholar 
    Huang, H. P., Chen, W., Zhang, Y., Qiao, L. & Du, Y. Y. Analysis of ecological quality in Lhasa metropolitan area during 1990–2017 based on remote sensing and Google Earth Engine platform. J. Geogr. Sci. 31, 265–280 (2021).Article 

    Google Scholar 
    Fan, C., Gui, F., Wang, L. Z. & Zhao, S. Evaluation of environmental quality based on remote sensing data in the coastal lands of eastern China. J. Coastal Res. 36, 1229–1236 (2020).Article 

    Google Scholar 
    Phan, T. N., Kuch, V. & Lehnert, L. W. Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sens. 12, 2411 (2020).Article 
    ADS 

    Google Scholar 
    Binh, N. A. et al. Thirty-year dynamics of LULC at the Dong Thap Muoi area, southern Vietnam, using Google Earth Engine. ISPRS Int. J. Geo Inf. 10, 226 (2021).Article 

    Google Scholar 
    Yang, G. X. et al. AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. Int. J. Appl. Earth Obs. Geoinf. 102, 102446 (2021).Inman, V. L. & Lyons, M. B. Automated inundation mapping over large areas using Landsat data and Google Earth Engine. Remote Sens. 12, 1348 (2020).Article 
    ADS 

    Google Scholar 
    Long, X. R., Li, X. Y., Lin, H. & Zhang, M. Mapping the vegetation distribution and dynamics of a wetland using adaptive-stacking and Google Earth Engine based on multi-source remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 102, 102453 (2021).
    Google Scholar 
    Hu, Y. F., Dong, Y. & Nacun, B. An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS J. Photogram. Remote Sens. 146, 347–359 (2018).Article 
    ADS 

    Google Scholar 
    Mahdianpari, M. et al. A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: A case study in Newfoundland. Gisci. Remote Sens. 57, 1102–1124 (2020).Article 

    Google Scholar 
    Brovelli, M. A., Sun, Y. & Yordanov, V. Monitoring forest change in the Amazon using multi-temporal remote sensing data and machine learning classification on Google Earth Engine. ISPRS Int. J. Geo Inf. 9, 580 (2020).Article 

    Google Scholar 
    Yin, H. R. et al. Analysis of spatial heterogeneity and influencing factors of ecological environment quality in China’s north-south transitional zone. Int. J. Environ. Res. Public Health 19, 2236 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xinran, N., Zhenqi, H., Mengying, R., Qi, Z. & Huang, S. Remote-sensing evaluation and temporal and spatial change detection of ecological environment quality in coal-mining areas. Remote Sens. 14, 345 (2022).Article 

    Google Scholar 
    Li, H. et al. Exploring spatial distributions of increments in soil heavy metals and their relationships with environmental factors using GWR. Stoch. Environ. Res. Risk Assess. 35, 2173–2186 (2021).Article 

    Google Scholar 
    Wang, J. F. & Xu, C. D. Geodetector: Principle and prospective. Acta Geogr. Sin. 72, 116–134 (2017).
    Google Scholar 
    Peng, S., Ding, Y., Liu, W. & Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data. 11, 1931–1946 (2019).Article 
    ADS 

    Google Scholar 
    Hu, X. S. & Xu, H. Q. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecol. Indic. 89, 11–21 (2018).Article 

    Google Scholar 
    Yu, G. Q., Yang, H. B., Tian, Z. Z. & Zhang, B. S. Eco-environment quality assessment of Miyun county based on RS and GIS. Proc. Environ. Sci. 10, 2601–2607 (2011).Article 

    Google Scholar 
    Chen, S. L., Zhu, Z. H., Liu, X. T. & Yang, L. Variation in vegetation and its driving force in the Pearl river delta region of China. Int. J. Environ. Res. Public Health 19, 10343 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhu, D. Y., Chen, T., Zhen, N. & Niu, R. Q. Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environ. Sci. Pollut. Res. 27, 15716–15728 (2020).Article 

    Google Scholar 
    Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A. & Skakun, S. Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Front. Earth Sci. 5, 1–10 (2017).Article 

    Google Scholar 
    Kumar, L. & Mutanga, O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 10, 1509 (2018).Article 
    ADS 

    Google Scholar 
    Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
    ADS 

    Google Scholar 
    Parastatidis, D., Mitraka, Z., Chrysoulakis, N. & Abrams, M. Online global land surface temperature estimation from Landsat. Remote Sens. 9, 1208 (2017).Article 
    ADS 

    Google Scholar 
    Kennedy, R. E. et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens. 10, 691 (2018).Article 
    ADS 

    Google Scholar 
    Huang, H. B. et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens. Environ. 202, 166–176 (2017).Article 
    ADS 

    Google Scholar 
    Ying, L. et al. Estimation of remote sensing based ecological index along the Grand Canal based on PCA-AHP-TOPSIS methodology. Ecol. Ind. 122, 107214 (2021).Article 

    Google Scholar 
    He, X., Li, M., Guo, H. & Tian, Z. Evaluation of ecological environment of Songshan scenic area based on GF-1 data. in IOP Conference Series: Materials Science and Engineering. Vol. 392. 042029 (2018).Yi, Z., Jiyun, S., Xiangren, L. & Meng, Z. Spatio-temporal evolution and driving factors of eco-environmental quality based on RSEI in Chang-Zhu-Tan metropolitan circle, central China. Ecol. Ind. 144, 109436 (2022).Article 

    Google Scholar 
    Wan, H. L., Huo, F., Niu, Y. F., Zhang, W. & Zhang, Q. R. Dynamic monitoring and analysis of ecological environment change in Cangzhou city based on RSEI model considering PM2.5 concentration. Prog. Geophys. 36, 953–960 (2021).
    Google Scholar 
    Wang, J., Ma, J. L., Xie, F. F. & Xu, X. J. Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example. Chin. J. Appl. Ecol. 31, 3795–3804 (2020).
    Google Scholar  More

  • in

    Newer roots for agriculture

    Annual grains, domesticated from wild species, have dominated agriculture since the Neolithic. A new study reports how turning to high-yield perennial rice crops could maintain key ecosystem functions while supporting livelihoods.The past several decades have seen modest but growing investments in the development of perennial grain crops, including perennial counterparts of wheat, rice and sorghum suitable for the USA, China, Europe and Africa. One technique involves domesticating wild perennial species through continual selection of desirable traits over multiple generations3. A recently developed perennial grain currently grown for niche markets in the USA, Kernza, was domesticated from Thinopyrum intermedium, a wild relative of wheat. While yields of Kernza remain low compared with those of annual wheat, they are increasing. As with the development of perennial rice, plant breeders can also cross perennial species with domesticated annual relatives to produce perennial hybrids with desirable traits derived from the annual parent3. More

  • in

    Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

    Data acquisition and preparationStructured datasetsWe used structured North American Breeding Bird Survey (BBS) data, which is conducted annually over  > 2500 routes across the United States and Canada11,12 during the peak of the breeding season (May and June). BBS routes were approximately 40 km long with 50 stops spaced 0.8 km apart. At each stop a 3-min point count was conducted, where all species seen or heard were recorded12. We downloaded the entire dataset, 1966–2019, to identify each observer’s first year and account for differences in survey experience. We created a binary variable for the observers’ first year, with 1 indicating the first year they provided data, and 0 indicating all subsequent years. We then subset the data to years 2010–2019 to align with available community science data. We zero-filled BBS data by adding zeros for each species on routes in which birds were not detected in each year.Semi-structured datasetWe used the eBird Basic Dataset as a semi-structured dataset. We used checklists within the US and Canada during June and July from 2010 to 2019. Data were filtered to impose structure on the observation process and minimize effects of unequal spatial and temporal sampling using the auk package in program R24,25,56,59,60. Data were filtered to only include complete checklists where observers recorded counts of all species detected to reduce effects of preferential species reporting61. We also filtered data based on observer effort to only include checklists  More

  • in

    Parasitic infection increases risk-taking in a social, intermediate host carnivore

    Dubey, J. P. Toxoplasmosis of animals and humans. (CRC Press, 2010).Robert-Gangneux, F. & Dardé, M. L. Epidemiology of and diagnostic strategies for toxoplasmosis. Clin. Microbiol Rev. 25, 264–296 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wong, S. & Remington, J. S. Toxoplasmosis in Pregnancy. Clin. Infect. Dis. 18, 853–861 (1994).Article 
    CAS 
    PubMed 

    Google Scholar 
    Arantes, T. P. et al. Toxoplasma gondii: Evidence for the transmission by semen in dogs. Exp. Parasitol. 123, 190–194 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stibbs, H. H. Changes in brain concentrations of catecholamines and indoleamines in Toxoplasma gondii infected mice. Ann. Trop. Med Parasitol. 79, 153–157 (1985).Article 
    CAS 
    PubMed 

    Google Scholar 
    McConkey, G. A., Martin, H. L., Bristow, G. C. & Webster, J. P. Toxoplasma gondii infection and behaviour – Location, location, location? J. Exp. Biol. 216, 113–119 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lim, A., Kumar, V., Hari Dass, S. A. & Vyas, A. Toxoplasma gondii infection enhances testicular steroidogenesis in rats. Mol. Ecol. 22, 102–110 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zouei, N., Shojaee, S., Mohebali, M. & Keshavarz, H. The association of latent toxoplasmosis and level of serum testosterone in humans. BMC Res Notes 11, 365 (2018).Arnott, M. A., Cassella, J. P., Aitken, P. P. & Hay, J. Social interactions of mice congenital Toxoplasma infection. Ann. Trop. Med Parasitol. 84, 149–156 (1990).Article 
    CAS 
    PubMed 

    Google Scholar 
    Coccaro, E. F. et al. Toxoplasma gondii infection: Relationship with aggression in psychiatric subjects. J. Clin. Psychiatry 77, 334–341 (2016).Article 
    PubMed 

    Google Scholar 
    Webster, J. P., Brunton, C. F. A. & Macdonald, D. W. Effect of Toxoplasma Gondii Upon Neophobic Behaviour in Wild Brown Rats, Rattus Norvegicus. Parasitology 109, 37–43 (1994).Article 
    PubMed 

    Google Scholar 
    Berdoy, M., Webster, J. P. & Mcdonald, D. W. Fatal attraction in rats infected with Toxoplasma gondii. Proc. R. Soc. B: Biol. Sci. 267, 1591–1594 (2000).Article 
    CAS 

    Google Scholar 
    Poirotte, C. et al. Morbid attraction to leopard urine in toxoplasma-infected chimpanzees. Curr. Biol. 26, R98–R99, https://doi.org/10.1016/j.cub.2015.12.020 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gering, E. et al. Toxoplasma gondii infections are associated with costly boldness toward felids in a wild host. Nat. Commun. 12, 3842 (2021).Smith, D. W., Stahler, D. R. & MacNulty, D. R. Yellowstone Wolves: Science and Discovery in the World’s First National Park. (University of Chicago Press, 2020).Ruth, T. K., Buotte, P. C., Hornocker, M., Murphy, K. M. & Smith, D. W. Patterns of Resource Use Prior to and during Wolf Restoration. in Yellowstone Cougars: Ecology Before And During Wolf Restoration (eds. Ruth, T. K., Buotte, P. C. & Hornocker, M.) 151–175 (University Press of Colorado, 2019).Brandell, E. E. et al. Patterns and processes of pathogen exposure in gray wolves across North America. Sci. Rep. 11, 3722 (2021).Watts, D. E. & Benson, A. M. Prevalence of antibodies for selected canine pathogens among wolves (Canis lupus) from the Alaska Peninsula, USA. J. Wildl. Dis. 52, 506–515 (2016).Article 
    PubMed 

    Google Scholar 
    Galván-Ramírez, M. D. L. L., Gutíerrez-Maldonado, A. F., Verduzco-Grijalva, F. & Judith Marcela, D. J. The role of hormones on toxoplasma gondii infection: A systematic review. Front. Microbiol. 5, 503 (2014).Kreeger, T. J. The Internal Wolf: Physiology, Pathology, and Pharmacology. in Wolves: Behavior, Ecology, and Conservation (eds. Mech, L. D. & Boitani, L.) 192–217 (University of Chicago Press, 2003).Sands, J. & Creel, S. Social dominance, aggression and faecal glucocorticoid levels in a wild population of wolves, Canis lupus. Anim. Behav. 67, 387–396 (2004).Article 

    Google Scholar 
    Cassidy, K. A., Mech, L. D., MacNulty, D. R., Stahler, D. R. & Smith, D. W. Sexually dimorphic aggression indicates male gray wolves specialize in pack defense against conspecific groups. Behavioural Process. 136, 64–72 (2017).Article 

    Google Scholar 
    Ganz, T. Defensins: Antimicrobial peptides of innate immunity. Nat. Rev. Immunol. 3, 710–720 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Anderson, T. M. et al. Molecular and evolutionary history of melanism in North American gray wolves. Science (1979) 323, 1339–1343 (2009).CAS 

    Google Scholar 
    Smith, D. W. et al. Population Dynamics and Demography. in Yellowstone Wolves: Science and Discovery in the World’s First National Park (eds. Smith, D. W., Stahler, D. R. & MacNulty, D. R.) 77–92 (University of Chicago Press, 2020).Geremia, C. et al. Integrating population- and individual-level information in a movement model of Yellowstone bison. Ecol. Appl. 24, 346–362 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Houston, D. B. Elk as Winter-Spring Food for Carnivores in Northern Yellowstone National Park. J. Appl. Ecol. 15, 653–661 (1978).Article 

    Google Scholar 
    White, P. J. et al. Migration of northern yellowstone elk: Implications of spatial structuring. J. Mammal. 91, 827–837 (2010).Article 

    Google Scholar 
    Jimenez, M. D. et al. Wolf dispersal in the Rocky Mountains, Western United States: 1993–2008. J. Wildl. Manag. 81, 581–592 (2017).Article 

    Google Scholar 
    Fuller, T. K., Mech, L. D. & Cochrane, J. F. Wolf population dynamics. in Wolves: Behavior, Ecology, and Conservation2 (eds. Mech, L. D. & Boitani, L.) 161–191 (University of Chicago Press, 2003).Clutton-Brock, T. Mammal Societies. (John Wiley & Sons, 2016).Dass, S. A. H. et al. Protozoan parasite Toxoplasma gondii manipulates mate choice in rats by enhancing attractiveness of males. PLoS One 6, 1–6 (2011).Article 

    Google Scholar 
    Packard, J. M. Wolf Behavior: Reproductive, Social and Intelligent. in Wolves: Behavior, Ecology, and Conservation (eds. Mech, L. D. & Boitani, L.) (University of Chicago Press, 2003).Stahler, D. R. et al. Ecology of Family Dynamics in Yellowstone Wolf Packs. in Yellowstone Wolves: Science and Discovery in the World’s First National Park (eds. Smith, D. W., Stahler, D. R. & MacNulty, D. R.) 42–60 (University of Chicago Press, 2020).Sikes, R. S. 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mammal. 97, 663–688 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Murphy, K. M. et al. Distribution of Canada lynx in Yellowstone National Park. Northwest Sci. 80, 199–206 (2006).
    Google Scholar 
    Murphy, K. M. The ecology of the cougar (Puma concolor) in the northern Yellowstone ecosystem: Interactions with prey, bears, and humans. (University of Idaho, Moscow, USA, 1998).Ruth, T. K., Buotte, P. C. & Quigley, H. B. Comparing Ground Telemetry and Global Positioning System Methods to Determine Cougar Kill Rates. J. Wildl. Manag. 74, 1122–1133 (2010).Article 

    Google Scholar 
    Anton, C. B. The demography and comparative ethology of top predators in a multi-carnivore system. 211 (2020).Cassidy, K. A. et al. Yellowstone Wolf Project Annual Report. (2021).Ruth, T. K., Buotte, P. C. & Hornocker, M. Spatial Responses of Cougars to Wolf Presence. in Yellowstone Cougars: Ecology Before And During Wolf Restoration (eds. Ruth, T. K., Buotte, P. C. & Hornocker, M.) 129–150 (University Press of Colorado, 2019).Sawaya, M. A. et al. Evaluation of noninvasive genetic sampling methods for cougars in Yellowstone National Park. J. Wildl. Manag. 75, 612–622 (2011).Article 

    Google Scholar 
    Metz, M. C. et al. Accounting for imperfect detection in observational studies: modeling wolf sightability in Yellowstone National Park. Ecosphere 11, e03152 (2020).Rothman, R. J. & Mech, L. D. Scent-marking in lone wolves and newly formed pairs. Anim. Behav. 27, 750–760 (1979).Article 

    Google Scholar 
    Liesenfeld, O., Nguyen, T. A., Pharke, C. & Suzuki, Y. Importance of gender and sex hormones in regulation of susceptibility of the small intestine to peroral infection with Toxoplasma gondii tissue cysts. J. Parasitol. 87, 1491–1493 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Molnar, B. et al. Environmental and intrinsic correlates of stress in free-ranging wolves. PLoS One 10, 1–25 (2015).Article 

    Google Scholar 
    Anton, C. B. et al. Gray wolf habitat use in response to visitor activity along roadways in Yellowstone National Park. Ecosphere 11, e03164 (2020). More

  • in

    Extensive range contraction predicted under climate warming for two endangered mountaintop frogs from the rainforests of subtropical Australia

    Beniston, M., Diaz, H. F. & Bradley, R. S. Climatic change at high elevation sites: An overview. Clim. Change 36, 233–251 (1997).Article 

    Google Scholar 
    Chape, S., Spalding, M. & Jenkins, M. The world’s protected areas: Status, values, and prospects in the twenty-first century. Bioscience 59(7), 623–624 (2009).
    Google Scholar 
    Körner, C. Mountain biodiversity, its causes and function. Ambio 33, 11–17 (2004).Article 

    Google Scholar 
    Körner, C. et al. A global inventory of mountains for bio-geographical applications. Alp. Bot. 127, 1–15 (2017).Article 

    Google Scholar 
    Forero-Medina, G., Joppa, L. & Pimm, S. L. Constraints to species’ elevational range shifts as climate changes. Conserv. Biol. 25, 163–171 (2011).Article 
    PubMed 

    Google Scholar 
    Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: Competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B 279, 2072–2080 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl. Acad. Sci. 115, 11982–11987 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024 (2011).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Lenoir, J. & Svenning, J. C. Climate-related range shifts: A global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

    Google Scholar 
    Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. 117, 4211–4217 (2020).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e200114 (2016).Article 

    Google Scholar 
    Orians, G. H. & Milewski, A. V. Ecology of Australia: The effects of nutrient-poor soils and intense fires. Biol. Rev. 82, 393–423 (2007).Article 
    PubMed 

    Google Scholar 
    Laurance, W. F. et al. The 10 Australian ecosystems most vulnerable to tipping points. Biol. Cons. 144, 1472–1480 (2011).Article 

    Google Scholar 
    Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: An impending environmental catastrophe. Proc. R. Soc. Lond. B 270, 1887–1892 (2003).Article 

    Google Scholar 
    Mahony, M.J. The amphibians. in Remnants of Gondwana: A Natural and Social History of the Gondwana Rainforests of Australia. (eds. Kitching, R.L., Braithwaite, R., & Cavanaugh, J.) (Surrey Beatty & Sons, 2010).Kooyman, R. M., Watson, J. & Wilf, P. Protect Australia’s gondwana rainforests. Science 367, 1083–1083 (2020).Article 
    PubMed 
    ADS 

    Google Scholar 
    Narsey, S. et al. (2020). Impact of climate change on cloud forests in the Gondwana Rainforests of Australia World Heritage Area. Earth Systems and Climate Change Hub Report.Newell, D. An update on frog declines from the forests of subtropical eastern Australia in Status of Conservation and Decline of Amphibians: Australia, New Zealand, and Pacific Islands (eds. Heatwole H. and Rowley J. L.) 29–37 (CSIRO, 2018).DAWE. Bushfire Impacts Vol. 2021 (Commonwealth Department of Agriculture Water and Environment, 2020).
    Google Scholar 
    Collins, L. et al. The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire. Environ. Res. Lett. 16, 044029 (2021).Article 
    ADS 

    Google Scholar 
    Filkov, A. I., Ngo, T., Matthews, S., Telfer, S. & Penman, T. D. Impact of Australia’s catastrophic 2019/20 bushfire season on communities and environment: Retrospective analysis and current trends. J. Saf. Sci. Resil. 1, 44–56 (2020).
    Google Scholar 
    Blunden, J. & Arndt, D. S. State of the climate in 2019. Bull. Am. Meteor. Soc. 101, S1–S429 (2020).Article 

    Google Scholar 
    Zhongming, Z., Linong, L., Wangqiang, Z. & Wei, L. AR6 Climate Change 2021: The Physical Science Basis (Springer, 2021).
    Google Scholar 
    Laidlaw, M. J., McDonald, W. J. F., Hunter, R. J., Putland, D. A. & Kitching, R. L. The potential impacts of climate change on Australian subtropical rainforest. Aust. J. Bot. 59, 440–449 (2011).Article 

    Google Scholar 
    Blaustein, A. R. et al. Direct and indirect effects of climate change on amphibian populations. Diversity 2, 281–313 (2010).Article 

    Google Scholar 
    Li, Y., Cohen, J. M. & Rohr, J. R. Review and synthesis of the effects of climate change on amphibians. Integr. Zool. 8, 145–161 (2013).Article 
    PubMed 

    Google Scholar 
    Carey, C. & Alexander, M. A. Climate change and amphibian declines: Is there a link?. Divers. Distrib. 9, 111–121 (2003).Article 

    Google Scholar 
    Cohen, J. M., Civitello, D. J., Venesky, M. D., McMahon, T. A. & Rohr, J. R. An interaction between climate change and infectious disease drove widespread amphibian declines. Glob. Change Biol. 25, 927–937 (2019).Article 
    ADS 

    Google Scholar 
    Geyle, H. M. et al. Red hot frogs: Identifying the Australian frogs most at risk of extinction. Pac. Conserv. Biol. 28, 211–223 (2021).Article 

    Google Scholar 
    Gillespie, G. R. et al. Status and priority conservation actions for Australian frog species. Biol. Conserv. 247, 108543 (2020).Article 

    Google Scholar 
    Almeida, A. M. et al. Prediction scenarios of past, present, and future environmental suitability for the Mediterranean species Arbutus unedo L. Sci. Rep. 12, 1–15 (2022).Article 

    Google Scholar 
    Lima, V. P. et al. Climate change threatens native potential agroforestry plant species in Brazil. Sci. Rep. 12, 1–14 (2022).Article 
    ADS 

    Google Scholar 
    Tiwari, S. et al. Modelling the potential risk zone of Lantana camara invasion and response to climate change in eastern India. Ecol. Process. 11(1), 1–13 (2022).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    Galante, P. J. et al. The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity. Ecography 41, 726–736 (2018).Article 

    Google Scholar 
    Li, J. et al. Climate refugia of snow leopards in High Asia. Biol. Conserv. 203, 188–196 (2016).Article 

    Google Scholar 
    Searcy, C. A. & Shaffer, B. H. Do ecological niche models accurately identify climatic determinants of species ranges?. Am. Nat. 187, 423–435 (2016).Article 
    PubMed 

    Google Scholar 
    Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108857 (2020).Article 

    Google Scholar 
    Anstis, M. Tadpoles and Frogs of Australia (New Holland Publishers Pty Limited, 2017).
    Google Scholar 
    Knowles, R., Mahony, M., Armstrong, J. & Donnellan, S. Systematics of sphagnum frogs of the Genus Philoria (Anura: Myobatrachidae) in Eastern Australia, with the description of two new species. Rec. Aust. Mus. 56, 57–74 (2004).Article 

    Google Scholar 
    Mahony, M. J. et al. A new species of Philoria (Anura: Limnodynastidae) from the uplands of the Gondwana Rainforests world heritage area of eastern Australia. Zootaxa 5104, 209–241 (2022).Article 
    PubMed 

    Google Scholar 
    Bolitho, L. J., Rowley, J. J. L., Hines, H. B. & Newell, D. Occupancy modelling reveals a highly restricted and fragmented distribution in a threatened montane frog (Philoria kundagungan) in subtropical Australian rainforests. Aust. J. Zool. 67, 231–240 (2021).Article 

    Google Scholar 
    Heard, G. et al. Post-fire impact assessment for priority frogs: northern Philoria. (NESP Threatened Species Recovery Hub Project 8.1.3 report, Brisbane, 2021).Vanderwal, J. All Future Climate Layers for Australia: 1 km Resolution (James Cook University, 2012).
    Google Scholar 
    Torkkola, J. J., Chauvenet, A. L. M., Hines, H. & Oliver, P. M. Distributional modelling, megafires and data gaps highlight probable underestimation of climate change risk for two lizards from Australia’s montane rainforests. Austral Ecol. 47(2), 365–379 (2021).Article 

    Google Scholar 
    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 (2005).Article 

    Google Scholar 
    Geoscience, A. Digital Elevation Model (DEM) 25 Metre Grid of Australia derived from LiDAR. (Geoscience Australia, 2015).Thuiller, W., Georges, D., Engler, R. & Breiner, F. (2014). biomod2: Ensemble platform for species distribution modeling. R package version 3.1-64. http://CRANR-project.org/package=biomod2. Accessed Feb 2021.Feng, X., Park, D. S., Liang, Y., Pandey, R. & Papeş, M. Collinearity in ecological niche modeling: Confusions and challenges. Ecol. Evol. 9, 10365–10376 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thuiller, W. BIOMOD: Optimising predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol. 9, 1353–1362 (2003).Article 
    ADS 

    Google Scholar 
    MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonisation, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207 (2003).Article 

    Google Scholar 
    Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl. Acad. Sci. 117, 19656–19657 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
    CAS 
    PubMed 
    ADS 

    Google Scholar 
    Campos-Cerqueira, M. & Mitchell Aide, T. Lowland extirpation of anuran populations on a tropical mountain. PeerJ 2017, 1–10 (2017).
    Google Scholar 
    Pounds, J. A., Fogden, M. P. L. & Campbell, J. H. Biological response to climate change on a tropical mountain. Nature 398, 611–615 (1999).Article 
    CAS 
    ADS 

    Google Scholar 
    Raxworthy, C. J. et al. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: A preliminary appraisal for the highest massif in Madagascar. Glob. Change Biol. 14, 1703–1720 (2008).Article 
    ADS 

    Google Scholar 
    Fordham, D. A. et al. Extinction debt from climate change for frogs in the wet tropics. Biol. Lett. 12, 20160236 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoffmann, E. P., Williams, K., Hipsey, M. R. & Mitchell, N. J. Drying microclimates threaten persistence of natural and translocated populations of threatened frogs. Biodivers. Conserv. 30(1), 15–34 (2020).Article 

    Google Scholar 
    Scheele, B. C., Driscoll, D. A., Fischer, J. & Hunter, D. A. Decline of an endangered amphibian during an extreme climatic event. Ecosphere 3, 101 (2012).Article 

    Google Scholar 
    Legge, S. et al. Rapid assessment of the biodiversity impacts of the 2019–2020 Australian megafires to guide urgent management intervention and recovery and lessons for other regions. Divers. Distrib. 28, 571–591 (2022).Article 

    Google Scholar 
    Canadell, J. G. et al. Multi-decadal increase of forest burned area in Australia is linked to climate change. Nat. Commun. 12, 6921 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Hisano, M., Searle, E. B. & Chen, H. Y. H. Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems. Biol. Rev. 93, 439–456 (2018).Article 
    PubMed 

    Google Scholar 
    Holz, A., Wood, S. W., Veblen, T. T. & Bowman, D. M. J. S. Effects of high-severity fire drove the population collapse of the subalpine Tasmanian endemic conifer Athrotaxis cupressoides. Glob. Change Biol. 21, 445–458 (2015).Article 
    ADS 

    Google Scholar 
    Hutley, L. B., Doley, D., Yates, D. J. & Boonsaner, A. Water balance of an australian subtropical rainforest at altitude: The ecological and physiological significance of intercepted cloud and fog. Aust. J. Bot. 45, 311–329 (1997).Article 

    Google Scholar 
    Godfree, R. C. et al. Implications of the 2019–2020 megafires for the biogeography and conservation of Australian vegetation. Nat. Commun. 12, 1023 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Hennessy, K. et al. Climate Change Impacts on Fire-Weather in South-East Australia (Commonwealth Scientific and Industrial Research Organisation, 2005).
    Google Scholar 
    Moriondo, M. et al. Potential impact of climate change on fire risk in the Mediterranean area. Clim. Res. 31, 85–95 (2006).Article 

    Google Scholar 
    Pitman, A. J., Narisma, G. T. & McAneney, J. The impact of climate change on the risk of forest and grassland fires in Australia. Clim. Change 84, 383–401 (2007).Article 
    ADS 

    Google Scholar 
    Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Article 

    Google Scholar 
    Scheele, B. C. et al. Conservation translocations for amphibian species threatened by chytrid fungus: A review, conceptual framework, and recommendations. Conserv. Sci. Pract. 3, e524 (2021).
    Google Scholar 
    Rudin-Bitterli, T. S., Evans, J. P. & Mitchell, N. J. Geographic variation in adult and embryonic desiccation tolerance in a terrestrial-breeding frog. Evolution 74, 1186–1199 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 37, 1407–1413 (2010).
    Google Scholar 
    Keppel, G. et al. Refugia: Identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).Article 

    Google Scholar 
    Selwood, K. E. & Zimmer, H. C. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

    Google Scholar  More

  • in

    Speciated mechanism in Quaternary cervids (Cervus and Capreolus) on both sides of the Pyrenees: a multidisciplinary approach

    Petronio, C. Les cervidés endémiques des îles méditerranéennes. Quaternaire 3–4, 259–264 (1990).
    Google Scholar 
    Liouville, M. Variabilité du Cerf élaphe (Cervus elaphus LINNE 1758) au cours du pléistocène moyen et supérieur en Europe occidentale : Approches morphométrique, paléoécologique et cynégétique (Museum National d’Histoire Naturelle, Paris, 2007).
    Google Scholar 
    van der Made, J., Stefaniak, K. & Marciszak, A. The polish fossil record of the wolf canis and the deer alces, capreolus, megaloceros, dama and cervus in an evolutionary perspective. Quatern. Int. 326–327, 406–430 (2014).
    Google Scholar 
    Guadelli, J.-L. Contribution à l’étude des zoocénoses préhistoriques en Aquitaine (Würm ancien et interstade würmiem. Universite de Bordeaux, Talence, 1987).
    Google Scholar 
    Guadelli, J.-L. Les cerfs du würm ancien en Aquitaine. Paléo 8, 99–108 (1996).
    Google Scholar 
    Defleur, A. et al. Le niveau moustérien de la grotte de l’Adaouste (Jouques, Bouches-du-Rhône): Approche culturelle et paléoenvironnements. Bull. Mus. anthropol. préhist. Monaco 37, 11–48 (1994).
    Google Scholar 
    Tournepiche, J.-F. Les grands mammifères pléistocènes de Poitou-Charente. Paléo 8, 109–141 (1996).
    Google Scholar 
    Delagnes, A. et al. Le gisement Pléistocène moyen et supérieur d’artenac (Saint-Mary, Charente): Premier bilan interdisciplinaire. Bull. Soc. Prehist. Fr. 96, 469–496 (1999).
    Google Scholar 
    Valensi, P., Psathi, E. & Lacombat, F. Le cerf élaphe dans les sites du Paléolithique moyen du Sud-Est de la France et de la Ligurie. Intérêts biostratigraphique, environnemental et taphonomique. In Acts of the XIVth UISPP Congress, Session 3: Paleoecology, General Sessions and Posters, 2–8 september 2001 97–105 (BAR International Series, 2004).Steele, T. E. Variation in mortality profiles of red deer (Cervus elaphus) in middle palaeolithic assemblages from western Europe. Int. J. Osteoarchaeol. 14, 307–320 (2004).
    Google Scholar 
    Croitor, R. A new form of wapiti cervus canadensis Erxleben, 1777 (Cervidae, Mammalia) from the late pleistocene of France. Palaeoworld 29, 789–806 (2020).
    Google Scholar 
    Meiri, M. et al. Subspecies dynamics in space and time: A study of the red deer complex using ancient and modern DNA and morphology. J. Biogeogr. 45, 367–380 (2018).
    Google Scholar 
    Queirós, J. et al. Red deer in Iberia: Molecular ecological studies in a southern refugium and inferences on European postglacial colonization history. PLoS ONE 14, e0210282 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Carranza, J., Salinas, M., de Andrés, D. & Pérez-González, J. Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol. Evol. 6, 905–922 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Rey-Iglesia, A., Grandal-d’Anglade, A., Campos, P. F. & Hansen, A. J. Mitochondrial DNA of pre-last glacial maximum red deer from NW Spain suggests a more complex phylogeographical history for the species. Ecol. Evol. 7, 10690–10700 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Geist, V. Deer of the world: Their evolution, behaviour, and ecology (Stackpole Books, Pennsylvania, 1998).
    Google Scholar 
    Rivals, F. & Lister, A. M. Dietary flexibility and niche partitioning of large herbivores through the pleistocene of Britain. Quatern. Sci. Rev. 146, 116–133 (2016).ADS 

    Google Scholar 
    Berlioz, E. Ecologie alimentaire et paléoenvironnements des cervidés européens du Pleistocène inférieur: le message des texutures de micro-usure dentaire (University of Poitiers, Poitiers, 2017).
    Google Scholar 
    Saarinen, J., Eronen, J., Fortelius, M., Seppä, H. & Lister, A. M. Patterns of diet and body mass of large ungulates from the pleistocene of Western Europe, and their relation to vegetation. Palaeontol. Electron. 19.3.32A, 1–58 (2016).
    Google Scholar 
    Stefano, G. D., Pandolfi, L., Petronio, C. & Salari, L. The morphometry and the occurrence of cervus elaphus (Mammalia, Cervidae) from the late Pleistocene of the Italian peninsula. Riv. Ital. Paleontol. Stratigr. 121, 103–120 (2015).
    Google Scholar 
    Terada, C., Tatsuzawa, S. & Saitoh, T. Ecological correlates and determinants in the geographical variation of deer morphology. Oecologia 169, 981–994 (2012).PubMed 
    ADS 

    Google Scholar 
    Sommer, R. S., Fahlke, J. M., Schmölcke, U., Benecke, N. & Zachos, F. E. Quaternary history of the European roe deer capreolus capreolus. Mammal Rev. 39, 1–16 (2009).
    Google Scholar 
    Lorenzini, R. et al. European Roe Deer Capreolus capreolus (Linnaeus, 1758). In Handbook of the Mammals of Europe (eds Hackländer, F. & Zachos, F. E.) 1–32 (Springer, Cham, 2022).
    Google Scholar 
    Lorenzini, R., Garofalo, L., Qin, X., Voloshina, I. & Lovari, S. Global phylogeography of the genus capreolus (Artiodactyla: Cervidae), a palaearctic meso-mammal. Zool. J. Linn. Soc. 170, 209–221 (2014).
    Google Scholar 
    Tixier, H. & Duncan, P. Are European roe deer browsers? A review of variations in the composition of their diets. Rev. Ecol. 51, 3–17 (1996).
    Google Scholar 
    Merceron, G., Viriot, L. & Blondel, C. Tooth microwear pattern in roe deer (Capreolus capreolus L.) from Chizé (Western France) and relation to food composition. Small Rumin. Res. 53, 125–132 (2004).
    Google Scholar 
    Delibes, J. R. Ecología y comportamiento del corzo (Capreolus capreolus L. 1758) en la Sierra de Grazalema (Cádiz) (Universidad Complutense, Complutense, 1996).
    Google Scholar 
    Hewitt, G. M. The genetic legacy of the quaternary ice ages. Nature 405, 907–913 (2000).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. Lond. B. Biol. Sci. 277, 661–671 (2010).
    Google Scholar 
    Álvarez-Lao, D. J. & García, N. Geographical distribution of pleistocene cold-adapted large mammal faunas in the Iberian peninsula. Quatern. Int. 233, 159–170 (2011).
    Google Scholar 
    Lumley, H. de. Le Paléolithique inférieur et moyen du Midi méditerranéen dans son cadre géologique. Tome I. Ligurie—Provence. Gall. Préhist. 5, (1969).Texier, P.-J. L’industrie moustérienne de l’abri pié-lombard (Tourettes-sur-Loup, Alpes-Maritimes). Bull. Soc. Préhist. Fr. 71, 429–448 (1974).
    Google Scholar 
    Texier, P.-J. et al. L’abri pié lombard à tourrettes-sur-loup (Alpes-Maritimes): Anciennes fouilles (1971–1985), nouvelles données. Bull. Mus.Anthropol.e Préhistor. Monaco 51, 19–49 (2011).
    Google Scholar 
    Tomasso, A. Territoires, systèmes de mobilité et systèmes de production : La fin du Paléolithique supérieur dans l’arc liguro-provençal (University of Nice Sophia Antipolis Nice, and University of Pisa, 2014).
    Google Scholar 
    Pelletier, M., Desclaux, E., Brugal, J.-P. & Texier, P.-J. The exploitation of rabbits for food and pelts by last interglacial neandertals. Quatern. Sci. Rev. 224, 105972 (2019).
    Google Scholar 
    Valladas, H. et al. Datations par la thermoluminescence de gisements moustériens du sud de la France. L’Anthropologie 91, 211–226 (1987).
    Google Scholar 
    Yokoyama, Y. et al. ESR dating of stalagmites of the Caune de l’Arago, the Grotte du Lazaret, the Grotte du Vallonnet and the abri Pié Lombard : a comparison with the U-Th method. In Third Specialist Seminar on TL and ESR Dating (eds. Hackens, T., Mejdahl, V., Bowman, S. G. E., Wintle, A. G. & Aitken, M. J.) 381–389 (1983).Romero, A. J., Fernández-Lomana, J. C. D. & Brugal, J.-P. Aves de caza. Estudio tafonómico y zooarqueológico de los restos avianos de los niveles musterienses de pié lombard (Alpes-Maritimes, Francia). Munibe Antropol. Arkeol. 68, 73–84 (2017).
    Google Scholar 
    Lumley (de), M.-A. Les néandertaliens dans le midi méditerranéen. In La Préhistoire française vol. T. 1 (Editions du CNRS, 1976).Porraz, G. En marge du milieu alpin. Dynamiques de formation des ensembles lithiques et modes d’occupation des territoires au paléolithique moyen (Université de Provence, Marseille, 2005).
    Google Scholar 
    Porraz, G. Middle Paleolithic mobile toolkits in shor-tterm human occupations: Two case studies. Eur. Prehist. 6, 33–55 (2009).
    Google Scholar 
    Roussel, A., Gourichon, L., Valensi, P. & Brugal, J.-P. Homme, gibier et environnement au Paléolithique moyen. Regards sur la gestion territoriale de l’espace semi-montagnard du Midi de la France. In Biodiversités, environnements et sociétés depuis la Préhistoire : nouveaux marqueurs et approches intégrées 87–99 (Éditions APDCA, 2021).Renault-Miskovsky, J. & Texier, J. Intérêt de l’analyse pollinique détaillée dans les concrétions de grotte .Application à l’abri pié-lombard (Tourettes-sur-Loup, Alpes maritimes). Quaternaire 17, 129–134 (1980).
    Google Scholar 
    Rosell, J. et al. A resilient landscape at teixoneres cave (MIS 3; Moià, Barcelona, Spain): The Neanderthals as disrupting agent. Quatern. Int. 435, 195–210 (2017).
    Google Scholar 
    Rosell, J. et al. Mossegades i Levallois: les noves intervencionsa la cova de les teixoneres (Moià, Bages). Trib d’Arqueologia 29–43 (2008).Rosell, J. et al. Los ocupaciones en la Cova de les Teixoneres (Moià, Barcelona): relaciones espaciales y grado de competencia entre hienas, osos y neandertales durante el Pleistoceno Superior. In Actas de la 1a Reunión de Científicos sobre Cubiles de Hiena (y Otros Grandes Carnívoros) en los Yacimientos Arqueológicos de la Península Ibérica (392–402) (eds Arriaza, M. C. et al.) (Museo Arqueológico Regional, 2010).
    Google Scholar 
    Rosell, J. et al. A stop along the way: The role of Neanderthal groups at level III of teixoneres cave (Moià, Barcelona, Spain). Quaternaire 21, 139–154 (2010).
    Google Scholar 
    Rosell, J. et al. Cova del toll y cova de les Teixoneres (Moià, Barcelona). In Los cazadores recolectores del Pleistoceno y del Holoceno en Iberia y el estrecho de Gibraltar (eds. Sala, R., Carbonell, E., Bermudez de Castro, J. M. & Arsuaga, J. L.) 302–307 (2014).Zilio, L. et al. Examining Neanderthal and carnivore occupations of teixoneres cave (Moià, Barcelona, Spain) using archaeostratigraphic and intra-site spatial analysis. Sci. Rep. 11, 4339 (2021).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    Tissoux, H. et al. Datation par les séries de l’uranium des occupations moustériennes de la grotte de teixoneres (Moia, Province de Barcelone, Espagne). Quaternaire 17, 27–33 (2006).
    Google Scholar 
    Talamo, S. et al. The radiocarbon approach to Neanderthals in a carnivore den site: A well-defined chronology for teixoneres cave (Moià, Barcelona, Spain). Radiocarbon 58, 247–265 (2016).CAS 

    Google Scholar 
    Álvarez-Lao, D. J., Rivals, F., Sánchez-Hernández, C., Blasco, R. & Rosell, J. Ungulates from teixoneres cave (Moià, Barcelona, Spain): Presence of cold-adapted elements in NE Iberia during the MIS 3. Palaeogeogr. Palaeoclimatol. Palaeoecol. 466, 287–302 (2017).
    Google Scholar 
    Rufà, A., Blasco, R., Rivals, F. & Rosell, J. Leporids as a potential resource for predators (hominins, mammalian carnivores, raptors): An example of mixed contribution from level III of teixoneres cave (MIS 3, Barcelona, Spain). C.R. Palevol. 13, 665–680 (2014).
    Google Scholar 
    Rufà, A., Blasco, R., Rivals, F. & Rosell, J. Who eats whom? Taphonomic analysis of the avian record from the middle paleolithic site of teixoneres cave (Moià, Barcelona, Spain). Quatern. Int. 421, 103–115 (2016).
    Google Scholar 
    Sánchez-Hernández, C., Rivals, F., Blasco, R. & Rosell, J. Short, but repeated Neanderthal visits to teixoneres cave (MIS 3, Barcelona, Spain): A combined analysis of tooth microwear patterns and seasonality. J. Archaeol. Sci. 49, 317–325 (2014).
    Google Scholar 
    Sánchez-Hernández, C., Rivals, F., Blasco, R. & Rosell, J. Tale of two timescales: Combining tooth wear methods with different temporal resolutions to detect seasonality of Palaeolithic hominin occupational patterns. J. Archaeol. Sci. Rep. 6, 790–797 (2016).
    Google Scholar 
    Picin, A. et al. Neanderthal mobile toolkit in short-term occupations at teixoneres cave (Moia, Spain). J. Archaeol. Sci. Rep. 29, 102165 (2020).
    Google Scholar 
    Fernández-García, M. et al. New insights in Neanderthal palaeoecology using stable oxygen isotopes preserved in small mammals as palaeoclimatic tracers in teixoneres cave (Moià, northeastern Iberia). Archaeol. Anthropol. Sci. 14, 106 (2022).
    Google Scholar 
    Ochando, J. et al. Neanderthals in a highly diverse, mediterranean-Eurosiberian forest ecotone: The pleistocene pollen record of teixoneres cave, Northeastern Spain. Quatern. Sci. Rev. 241, 106429 (2020).
    Google Scholar 
    López-García, J. M. et al. A multidisciplinary approach to reconstructing the chronology and environment of Southwestern European Neanderthals: The contribution of teixoneres cave (Moià, Barcelona, Spain). Quatern. Sci. Rev. 43, 33–44 (2012).ADS 

    Google Scholar 
    Sánchez-Hernández, C. et al. Dietary traits of ungulates in northeastern Iberian Peninsula: Did these Neanderthal preys show adaptive behaviour to local habitats during the middle palaeolithic?. Quatern. Int. 557, 47–62 (2020).
    Google Scholar 
    Fortelius, M. & Solounias, N. Functional characterization of ungulate molars using the abrasion-attrition wear gradient: A new method for reconstructing paleodiets. Am. Mus. Novit. 3301, 1–36 (2000).
    Google Scholar 
    Rivals, F., Solounias, N. & Mihlbachler, M. C. Evidence for geographic variation in the diets of late pleistocene and early holocene bison in North America, and differences from the diets of recent bison. Quatern. Res. 68, 338–346 (2007).ADS 

    Google Scholar 
    King, T., Andrews, P. & Boz, B. Effect of taphonomic processes on dental microwear. Am. J. Phys. Anthropol. 108, 359–373 (1999).CAS 
    PubMed 

    Google Scholar 
    Uzunidis, A. et al. The impact of sediment abrasion on tooth microwear analysis: An experimental study. Archaeol. Anthropol. Sci. 13, 134 (2021).
    Google Scholar 
    Kaiser, T. M. & Solounias, N. Extending the tooth mesowear method to extinct and extant equids. Geodiversitas 25, 321–345 (2003).
    Google Scholar 
    Xafis, A., Nagel, D. & Bastl, K. Which tooth to sample? A methodological study of the utility of premolar/non-carnassial teeth in the microwear analysis of mammals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 229–240 (2017).
    Google Scholar 
    Meadow, R. H. Early animal domestication in South Asia a first report of the faunal remains from mehrgarh Pakistan. In South Asian Archaeology (ed. Härtel, H.) 143–179 (Dietrich Reimer, Berlin, 1979).
    Google Scholar 
    Meadow, R. H. The use of size index scaling techniques for research on archaeozoological collections from the Middle East. In Historici Animalium ex. Ossibus Festschrift Angela Von Den Driesch Zum 65 Geburtstag (eds Becker, C. et al.) 285–300 (Verlag Marie Leidorf, Rahden, 1999).
    Google Scholar 
    Simpson, G. G. Large pleistocene felines of North America. Pleistocene felines North Am. 1136, 1–28 (1941).
    Google Scholar 
    Valli, A. M. F. & Guérin, C. L. gisement pléistocène supérieur de la grotte de Jaurens à Nespouls, Corrèze, France: Les cervidae (Mammalia, Artiodactyla). Publ. mus. Conflu. 1, 41–81 (2000).
    Google Scholar 
    Janis, C. M. The correlation between diet and dental wear in herbivorous mammals and its relationship to the determination of diets of extinct species. In Evolutionary Paleobiology of Behavior and Coevolution (ed. Boucot, A. J.) 241–259 (Elsevier, Amsterdam, 1990).
    Google Scholar 
    Heintz, E. Les Cervidés villafranchiens de France et d’Espagne (Museum National d’Histoire Naturelle, Parise, 1970).
    Google Scholar 
    Magniez, P. Etude paléontologique des artiodactyles de la grotte Tournal (Bize-Minervois, Aude, France) étude taphonomique, archéozoologique et paléoécologique des grands Mammifères dans leur cadre biostratigraphique et paléoenvironnemental (Université de Perpignan, Perpignan, 2010).
    Google Scholar 
    Cucchi, T., Hulme-Beaman, A., Yuan, J. & Dobney, K. Early neolithic pig domestication at Jiahu, Henan Province, China: clues from molar shape analyses using geometric morphometric approaches. J. Archaeol. Sci. 38, 11–22 (2011).
    Google Scholar 
    Evin, A. et al. The long and winding road: Identifying pig domestication through molar size and shape. J. Archaeol. Sci. 40, 735–743 (2013).
    Google Scholar 
    Pelletier, M., Kotiaho, A., Niinimäki, S. & Salmi, A.-K. Identifying early stages of reindeer domestication in the archaeological record: A 3D morphological investigation on forelimb bones of modern populations from Fennoscandia. Archaeol. Anthropol. Sci. 12, 169 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Bignon, O., Baylac, M., Vigne, J.-D. & Eisenmann, V. Geometric morphometrics and the population diversity of late glacial horses in Western Europe (Equus caballus arcelini): Phylogeographic and anthropological implications. J. Archaeol. Sci. 32, 375–391 (2005).
    Google Scholar 
    Pelletier, M. Morphological diversity, evolution and biogeography of early pleistocene rabbits (Genus Oryctolagus). Palaeontology 64, 817–838 (2021).
    Google Scholar 
    Curran, S. C. Expanding ecomorphological methods: Geometric morphometric analysis of cervidae post-crania. J. Archaeol. Sci. 39, 1172–1182 (2012).
    Google Scholar 
    Curran, S. C. Exploring eucladoceros ecomorphology using geometric morphometrics. Anat. Rec. 298, 291–313 (2015).
    Google Scholar 
    Cucchi, T. et al. Taxonomic and phylogenetic signals in bovini cheek teeth: Towards new biosystematic markers to explore the history of wild and domestic cattle. J. Archaeol. Sci. 109, 104993 (2019).
    Google Scholar 
    Jeanjean, M. et al. Sorting the flock: Quantitative identification of sheep and goat from isolated third lower molars and mandibles through geometric morphometrics. J. Archaeol. Sci. 141, 105580 (2022).
    Google Scholar 
    Herrera, P. L. Différences entre les dents jugales deciduales du cerf elaphe (Cervus Elaphus L.) et du boeuf domestique (Bos Taurus L.). Rev. Paléobiol. 8, 77 (1989).
    Google Scholar 
    Pfeiffer, T. Die stellung von dama (Cervidae, Mammalia) im system plesiometacarpaler hirsche des pleistozäns. Phylogenetische reconstruktion-metrische analyse. Cour Forsch. Senckenberg. 211, 1–218 (1999).
    Google Scholar 
    Rohlf, F. J. TPSDig, version 2.17 (Stony Brook, NY: Department of Ecology and Evolution, State University of New York, 2013).Bookstein, F. L. Morphometric Tools for Landmark Data: Geometry and Biology (Cambridge University Press, Cambridge, 1992).MATH 

    Google Scholar 
    Schlager, S. Morpho: Calculations and visualizations related to geometric morphometrics. (2013).Bookstein, F. L. Size and shape spaces for landmark data in two dimensions. Stat. Sci. 1, 181–222 (1986).MATH 

    Google Scholar 
    Kaiser, T. M. & Schulz, E. Tooth wear gradients in zebras as an environmental proxy—a pilot study. Mitt. Hambg. Zool. Mus. Inst. 103, 187–210 (2006).
    Google Scholar 
    Louys, J., Ditchfield, P., Meloro, C., Elton, S. & Bishop, L. C. Stable isotopes provide independent support for the use of mesowear variables for inferring diets in African antelopes. Proc. R. Soc. B. Biol. Sci. 279, 4441–4446 (2012).CAS 

    Google Scholar 
    Schulz, E. & Kaiser, T. M. Historical distribution, habitat requirements and feeding ecology of the genus equus (Perissodactyla). Mammal Rev. 43, 111–123 (2013).
    Google Scholar 
    Ulbricht, A., Maul, L. C. & Schulz, E. Can mesowear analysis be applied to small mammals? A pilot-study on leporines and murines. Mamm. Biol. 80, 14–20 (2015).
    Google Scholar 
    Danowitz, M., Hou, S., Mihlbachler, M., Hastings, V. & Solounias, N. A combined-mesowear analysis of late miocene giraffids from North Chinese and Greek localities of the pikermian biome. Palaeogeogr. Palaeoclimatol. Palaeoecol. 449, 194–204 (2016).
    Google Scholar 
    Marom, N., Garfinkel, Y. & Bar-Oz, G. Times in between: A zooarchaeological analysis of ritual in Neolithic Sha’ar Hagolan. Quatern. Int. 464, 216–225 (2018).
    Google Scholar 
    Ackermans, N. L. et al. Mesowear represents a lifetime signal in sheep (Ovis aries) within a long-term feeding experiment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 553, 109793 (2020).
    Google Scholar 
    Mihlbachler, M. C., Rivals, F., Solounias, N. & Semprebon, G. M. Dietary change and evolution of horses in North America. Science 331, 1178–1181 (2011).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Rivals, F., Rindel, D. & Belardi, J. B. Dietary ecology of extant guanaco (Lama guanicoe) from Southern Patagonia: Seasonal leaf browsing and its archaeological implications. J. Archaeol. Sci. 40, 2971–2980 (2013).
    Google Scholar 
    Rivals, F., Uzunidis, A., Sanz, M. & Daura, J. Faunal dietary response to the heinrich event 4 in southwestern Europe. Palaeogeogr. Palaeoclimatol. Palaeoecol. 473, 123–130 (2017).
    Google Scholar 
    Uzunidis, A., Rivals, F. & Brugal, J.-P. Relation between morphology and dietary traits in horse jugal upper teeth during the middle pleistocene in Southern France. Quat. Rev. Assoc. franc. l’étude Quat. 28, 303–312 (2017).
    Google Scholar 
    Uzunidis, A. Dental wear analyses of middle pleistocene site of Lunel-Viel (Hérault, France): Did equus and bos live in a wetland?. Quatern. Int. 557, 39–46 (2020).
    Google Scholar 
    Solounias, N. & Semprebon, G. Advances in the reconstruction of ungulate ecomorphology with application to early fossil equids. Am. Mus. Novit. 3366, 49 (2002).
    Google Scholar 
    Semprebon, G., Godfrey, L. R., Solounias, N., Sutherland, M. R. & Jungers, W. L. Can low-magnification stereomicroscopy reveal diet?. J. Hum. Evol. 47, 115–144 (2004).PubMed 

    Google Scholar 
    Grine, F. E. Dental evidence for dietary differences in australopithecus and paranthropus: A quantitative analysis of permanent molar microwear. J. Hum. Evol. 15, 783–822 (1986).
    Google Scholar 
    Teaford, M. F. & Oyen, O. J. In vivo and in vitro turnover in dental microwear. Am. J. Phys. Anthropol. 80, 447–460 (1989).CAS 
    PubMed 

    Google Scholar 
    Winkler, D. E. et al. The turnover of dental microwear texture: Testing the” last supper” effect in small mammals in a controlled feeding experiment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 557, 109930 (2020).
    Google Scholar 
    Walker, A., Hoeck, H. N. & Perez, L. Microwear of mammalian teeth as an indicator of diet. Science 201, 908–910 (1978).CAS 
    PubMed 
    ADS 

    Google Scholar 
    Janis, C. M. & Lister, A. M. The morphology of the lower fourth premolaras a taxonomic character in the ruminantia (Mammalia; Artiodactyla), and the systematic position of triceromeryx. J. Paleontol. 59, 405–410 (1985).
    Google Scholar 
    Croitor, R. Animal husbandry and hunting. Bone material use ineconomic activities. In Kravchenko, E. A. (eds.) From Bronze to Iron: Pale-oeconomy of the Habitants of the Inkerman Valley (According the Materialof Excavations in Uch-Bash and Saharnaya Golovka Settlements). 191–222 (Institute of Archaeology of National Academy of Sciences of Ukraine, 2016).Geist, V. & Bayer, M. Sexual dimorphism in the cervidae and its relation to habitat. J. Zool. 214, 45–53 (1988).
    Google Scholar 
    Fichant, R. Le cerf: Biologie, comportement, gestion (Gerfaut Editions, 2003).
    Google Scholar 
    Arellano-Moullé, A. Les cervidés des niveaux moustériens de la grotte du Prince (Grimaldi, Vintimille, Italie) Etude paléontologique. Bull. Mus. Anthropol. Préhist. Monaco 39, 53–58 (1997).
    Google Scholar 
    Brugal, J. .-P. . La. faune des grands mammifères de l’abri des Canalettes – matériel 1980–1986. In L’abri des Canalettes Un habitat moustérien sur les grands Causses Nant Aveyron, 89–137 (ed. Meignen, L.) (CNRS Éditions, Paris, 1993).
    Google Scholar 
    La Gerber, J. P. faune des grands mammifères du Würm ancien dans le sud-est de la France (Université de Provence, Marseille, 1973).
    Google Scholar 
    Alonso, D. A. Analisis paleobiologico de los ungulados del pleistoceno superior de la meseta norte (Universidad de Salamanca, Salamanca, 2015).
    Google Scholar 
    Sanchez, B. La fauna de mamíferos del pleistoceno superior del Abric Romani (Capellades, Barcelona). Adas de Paleontol. 331–347 (1989).Clot, A. Le chevreuil, Capreolus capreolus (L.) (Ceervidae, Artiodactyla) dans le pléistocène de Ge$$rde (H.-P.) et des pyrénées. Bull. Soc. Hist. Nat. Toulouse 125, 83–86 (1989).
    Google Scholar 
    Vanpé, C. Mating systems and sexual selection in ungulates. New insights from a territorial species with low sexual size dimorphism: the European roe deer (Capreolus capreolus). (Université Paul Sabatier, Toulouse III and Swedish University of Agricultural Sciences, 2007).Horcajada-Sánchez, F. & Barja, I. Local ecotypes of roe deer populations (Capreolus capreolus L.) in relation to morphometric features and fur colouration in the centre of the Iberian Peninsula. Pol. J Ecol. 64, 113–124 (2016).
    Google Scholar 
    Semprebon, G. M., Sise, P. J. & Coombs, M. C. Potential bark and fruit browsing as revealed by Stereomicrowear analysis of the peculiar clawed herbivores known as Chalicotheres (Perissodactyla, Chalicotherioidea). J. Mammal. Evol. 18, 33–55 (2011).
    Google Scholar 
    Rivals, F. et al. Palaeoecology of the mammoth steppe fauna from the late pleistocene of the North Sea and Alaska: Separating species preferences from geographic influence in paleoecological dental wear analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 286, 42–54 (2010).
    Google Scholar 
    Rivals, F., Takatsuki, S., Albert, R. M. & Macià, L. Bamboo feeding and tooth wear of three sika deer (Cervus nippon) populations from northern Japan. J. Mammal. 95, 1043–1053 (2014).
    Google Scholar 
    Lister, A. M. Evolutionary and ecological origins of British deer. Proc. R. Soc. Edinb. Sect. B. Biol. Sci. 82, 205–229 (1984).
    Google Scholar 
    Coulson, T., Guinness, F., Pemberton, J. & Clutton-Brock, T. The demographic consequences of releasing a population of red deer from culling. Ecology 85, 411–422 (2004).
    Google Scholar 
    Nussey, D. H., Clutton-Brock, T. H., Elston, D. A., Albon, S. D. & Kruuk, L. E. B. Phenotypic plasticity in a maternal trait in red deer. J. Anim. Ecol. 74, 387–396 (2005).
    Google Scholar 
    Frevert, W. Rominten (BLV Bayerischer Landwirtschaftsverlag, 1977).
    Google Scholar 
    Clutton-Brock, T. H. & Albon, S. D. Winter mortality in red deer (Cervus elaphus). J. Zool. 198, 515–519 (1982).
    Google Scholar 
    Loison, A. & Langvatn, R. Short- and long-term effects of winter and spring weather on growth and survival of red deer in Norway. Oecologia 116, 489–500 (1998).PubMed 
    ADS 

    Google Scholar 
    Torres-Porras, J., Carranza, J. & Pérez-González, J. Combined effects of drought and density on body and antler size of male iberian red deer cervus elaphus hispanicus: Climate change implications. Wildl. Biol. 15, 213–221 (2009).
    Google Scholar 
    Bugalho, M. N., Milne, J. A. & Racey, P. A. The foraging ecology of red deer (Cervus elaphus) in a mediterranean environment: Is a larger body size advantageous?. J. Zool. 255, 285–289 (2001).
    Google Scholar 
    Köhler, M. Skeleton and habitat of recent and fossil ruminants (F. Pfeil, Germany, 1993).
    Google Scholar 
    Boessneck, J. Zur grosse des mitteleuropaischen Rehes Capreolus capreolus L. in alluvial-vorgeschichtlicher und friiher historischer zeit. Z. f. Siiugetierkunde 21, 121–131 (1958).
    Google Scholar 
    Jensen, P. Body size trends of roe deer (Capreolus capreolus) from danish mesolithic sites. J. Dan. Archaeol. 10, 51–58 (1991).
    Google Scholar 
    Braza, F., San José, C., Aragon, S. & Delibes, J. R. El corzo andaluz. (Junta de Andalucía, 1994).Fandos, P. Skull biometry of spanish roe deer (Capreolus capreolus). Folia Zool. 43, 11–20 (1994).
    Google Scholar 
    Costa, L. First data on the size of north-Iberian roe bucks (Capreolus capreolus). Mammalia 59, 447–451 (1995).
    Google Scholar 
    Klein, D. R. & Strandgaard, H. Factors affecting growth and body size of roe deer. J. Wildl. Manag. 36, 64–79 (1972).
    Google Scholar  More

  • in

    Community succession and functional prediction of microbial consortium with straw degradation during subculture at low temperature

    Changes of straw degradation characteristics at different culture stagesCorn straw degradation ratioCorn straw weight loss in M44 at F1 reached 35.90% at 15 ℃ for 21 days, which was greater than that at F5, F8, and F11 by 2.33%, 3.01%, and 3.35%, respectively. There were no significant differences between F8 and F11(Fig. 1).Figure 1Corn straw degradation ratio was measured at different culture stages. The same small letter means there was no significant difference, and different small letters indicate significant differences at p  More

  • in

    Response of Canola productivity to integration between mineral nitrogen with yeast extract under poor fertility sandy soil condition

    Photosynthetic pigmentsBased on the analysis of variance, data of Photosynthetic pigments as presented in Table 1 indicate that photosynthetic pigments as chlorophyll a (Chl. a) had non-significant for three Canola genotypes AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), but chlorophyll b (Chl. b) and chlorophyll a/b ratio (Chl. a/b) had significant difference for three genotypes. Chl. a, Chl. b and Chl. a/b were positively responded to different N application i.e. without nitrogen fertilization (control F0), 95 kg N ha−1 (F1), 120 kg N ha−1 (F2) and 142 kg N ha−1 (F3) (without yeast); and integrated between nitrogen fertilization and yeast extract (YE) treatments as follows: 95 kg N ha−1 + YE (F4), 120 kg N ha−1 + YE (F5) and 142 kg N ha−1 (F6) (with yeast), data indicated that F5 and F6 gave the highest values of Chl. a and Chl. a/b ratio and lowest values of Chl. b Table 1. Interaction data showed that three Canola genotypes that were fertilized with N without yeast or with yeast had a slight difference with statistically significant in chl. a. The highest values of Chl. an obtained by G2 under F5 treatment followed by G1 under F6 treatments. In respect to Chl. a/b ratio, statistical analysis showed that Interaction between Canola genotypes treated with N applications without or with yeast had a significant difference whereas the highest values were recorded when Canola genotypes G3 and G2 fertilized with F6 and F5 with slight differences. While the interaction was significant between N treatments and Canola genotypes for Chl. b. and Canola genotype (G1) gave the highest value when treated with F1. Generally, F6 and F5 improve the contents of chl. a and chl. a/b ratio for three Canola genotypes Table 1. Chl. contents were increased in plants grown under middle and high N conditions as compared with plants grown under low N conditions, which significantly affected photochemical processes20. N is a fundamental element for leaf plants, insufficient N supply lead to decreased photosynthetic rate in plants21, this occurs to many factors such as a decrease in pigment degradation22, reduction in stomatal conductance23 and a decline in the light and dark reaction of photosynthesis. Canola is a nitrophilous plant, wherein a high concentration of NO3 in the culture media results in higher Chl. contents in the plant leave compared with controls20. The Chl. a/b ratio can be a valuable indicator of N element within a leaf because this ratio must be positively related to the ratio of PSII cores to light-harvesting chlorophyll-protein complex (LHCII)24. LHCII contains the majority of Chl. b, consequently it has a lower Chl a/b ratio than other Chl. binding proteins associated with PSII25. Thus, Chl. a/b ratios should increase with decreasing N availability, especially under high light conditions26, the Chl. a/b ratio and the ratio of PSII to Chl. are independent of N availability for spinach27, and lower Chl. a/b ratios were noticed when plants were subjected to low N28, while Kitajima and Hogan29 revealed that the Chl. a/b ratio increased when Chl. content decreased in response to N restriction in photosynthetic cotyledons in leaves of seedlings of four tropical woody species in the Bignoniaceae, and Bungard et al.30 demonstrated that there is a tiny response in Chl. a/b ratios to light or N. The yeast includes bio-regulators i.e. plant growth regulators and endogenous plant hormones, which enhance photosynthesis, also it produces 5-Aminolevulinic acid which is vital to tetrapyrrole biosynthesis and biochemical processes in plants, including heme and Chl. biosynthesis25.Table 1 Photosynthetic pigments for the three Canola genotypes under different N applications without and with yeast extract.Full size tableYield and its attributesComparing of mean data through the Duncan Multiple Range Test in the probability level of 5%, data showed significant differences among the Canola genotypes for the highest plant (cm), branches number/plant, and pods number/plant. On contrary, there wasn’t a significant difference for seed number/pods, seed yield (t ha−1), biological yield (t ha−1), and harvest index, wherein G2 gave the highest value for the highest plant (cm). In the same trend, G2 gave the highest values of branches No./plant and pods No./plant followed by G3 for the previous two treats Table 2. All examined N without or with yeast caused a significant difference in yield and its attributes, wherein F6 positively affected on abovementioned traits and gave the highest values on the highest plant (cm), branches No./plant, pods No./plant, seed No./pods, seed yield (t ha−1), and harvest index. While the highest values of biological yield (t ha−1) were obtained with F3, F6, and F5, respectively Table 2.Table 2 Growth, yield and its attributes for the three Canola genotypes under different N applications without and with yeast extract.Full size tableThe interaction between the Canola genotype and different N rates without or with yeast extract as shown in Table 2, demonstrated a significant difference. Data showed that the highest values of plant height and pods No./plant were recorded by G2 under F6 and the highest values of branches No./plant, seed No./pods, and seed yield (t ha−1) got by G3 and G2 under F6. There was a slight difference with statistically significant biological yield (t ha−1) and highest values established by G1 under F3 and F6; and G2 and G3 under F3, F5, and F6 respectively; and the highest values of harvest index recorded by G1, G2 and G3. under F6. Generally, data proved that 142 kg N/ h−1 + YE (F6) was enhanced the yield and its components of three Canola genotypes i.e. AD201 (G1), Topaz (G2), and SemuDNK 234/84 (G3). Many researchers reported that there are significant differences among Canola varieties and growth and yield traits are significantly increased by increasing N rates11. Increasing N fertilizer rates significantly increased most of the yield and its components31, N enhances metabolites synthesized by the plant which leads to more transformation of photosynthesis to reproductive parts, and induces different physiological mechanisms to access the nutrient32. Yeast extract as bio-fertilizer had a significant and positive effect on plant height and yield traits of Canola. The role of bread yeast in increasing the growth and yield traits; may be due to the content of yeast to many important nutrients elements i.e. N, Mg, Ca, Zn, Cu, and Fe, and the production of some growth regulators such as Auxin and Gibberellin and cytokinin which is necessary for plant biological processers especially photosynthesis and cell division and elongation33. Also, Yeast extract had stimulatory effects on cell division and enlargement, protein and nucleic acid synthesis, and chlorophyll formation34, in addition to its content of cryoprotective agent, i.e. sugars, protein, amino acids, and also several vitamins35. Consequently, it improves growth, flowering, and fruit set and formation and increases yield34.Correlation of Canola seed yield and chlorophyll a/b ratioPartial correlation coefficients of Canola seed yield and Chl. a/b ratio is given in Fig. 1. This result showed that seed yield was positively correlated with Chl. a/b ratio when the amount of N applied without or with yeast extract is increased. Chl. a/b ratio can be an important indicator of N within a leaf, this ratio must be positively related to photosynthesis and biological processers which reflect on seed yield.Figure 1Correlation of Canola seed yield (t/h) and chlorophyll a/b ratio as affected by different nitrogen rates without and with yeast extract.Full size imageCorrelation of Canola seed yield and its attributesCorrelations of seed yield and yield components of Canola are a function of the plant height, number of branches/plant, number of pods/plant, and number of seeds/pod as shown in Fig. 2a–d. These results proved that grain yield was strongly positively correlated with some of the abovementioned traits when N fertilization increased without or with yeast extract. Sufficient N contributes to enhance physiological processes, improves growth, flowering, seed formation, and the seed yield finally.Figure 2(a) Correlation of Canola seed yield (t/h) and plant height (cm) as affected by different nitrogen rates without and with yeast extract, (b) Correlation of Canola seed yield (t/h) and branch No/plant as affected by different nitrogen rates without and with yeast extract, (c) Correlation of Canola seed yield (t/h) and pods No/ plant as affected by different nitrogen rates without and with yeast extract, and (d) Correlation of Canola seed yield (t/h) and seeds No/ pod as affected by different nitrogen rates without and with yeast extract.Full size imageChemical propertiesRegarding results of the oil yield (t ha−1), seed oil %, protein %, N % in seed, and N% in straw as presented in Table 3, data showed significant differences among three Canola genotypes; AD201 (G1), Topaz (G2) and SemuDNK 234/84 (G3), excepted oil yield had non-significant difference. G1 was surpassed in oil %; G2, G3 surpassed in protein % and N % in seed, and G3 surpassed in N% in straw. Different N fertilization applies without or with yeast extract had a significant effect on the abovementioned traits, wherein F6 treatment gave the highest oil yield, protein %, N % in seed, and N% in straw, while seed oil % significantly increased with F1 and F4 treatments. There was significant interaction concerning with abovementioned traits, Table 3, as well as the highest values of seed oil yield (t ha−1), protein % in seeds, and nitrogen % in seeds were obtained with G1, G2, and G3 when treated with F6. Wherein the highest values of oil % were obtained by G1 under F1 and F4 treatments. Concerning N% in straw was increased by increasing the rate of N fertilizer application and the highest value was recorded by adding F6 to G336. Seed oil percentage was decreased by increasing nitrogen rates; the effect of interaction between Canola cultivars and nitrogen fertilization treatments was significant on seed oil. % High rates of N led to decreases in seed oil % and increase in protein concentrations in Canola seed37, the increase in seed protein % because N is an integral part of protein and the protein of Canola.Table 3 Effect of different N applications without and with yeast extract on oil yield, oil %, protein %, N % in seed and N% in straw for the three Canola genotypes.Full size tableCorrelation of Canola seed yield and seed oil percentageA strong negative correlation was detected between seed oil percentage as shown in Fig. 3. The result indicates that seed oil percentage decreases with increasing in different N fertilization rates without or with yeast extract. That’s a negative correlation between seed yield and seed oil %; it might be due to N application which results in delaying maturity leading to poor seed filling and a greater proportion of green seed38.Figure 3Correlation of Canola seed yield (t h−1) and oil % as affected by different nitrogen rates without and with yeast extract.Full size imagePhysico-chemical properties of Canola oilThe effects of different N application rates without or with yeast extract on Canola genotypes on physico-chemical properties i.e. Acid value (mg g−1), saponification number (mg g−1) and peroxide value (mg kg−1) were shown in Table 4. Data of chemical properties of Canola oil showed significant differences among Canola genotypes, the highest acid value and peroxide value were obtained from G2 followed by G1 and G3, respectively, while the highest saponification number was obtained by G3 followed by G1 and G2, respectively.Table 4 Oil properties for three Canola genotypes under different N applications without and with yeast extract.Full size tableData had significant differences among different N application rates without or with yeast extract, by increasing the N rated from F0 to F6 caused decreases in Acid value, Saponification number, and peroxide value. Also, data showed a significant interaction between Canola genotypes and different N application rates without or with yeast extract for all abovementioned traits, wherein the highest values of saponification number were obtained by G1 and G3 under F0 treatment. In addition, the highest values of peroxide value and the acid value were obtained by G2 with F0. The acid value is a physicochemical indicator38, wherein oils which have higher acid value posse poor quality39, on another hand, Low acid value of Canola genotype shows their higher oil quality. The peroxide value varied between 7.1 and 9.06 meq. O2/kg indicates that the tested vegetable oils are fresh, and the lowest initial peroxide value is suitable for consumption40. High saponification value indicated that Canola oil possesses normal triglycerides and may be useful in the production of liquid soap and shampoo41. Saponification number was significantly different among genotypes and a higher nitrogen rate resulted in an increase in the unsaponifiable matter and led to a decrease in oil acid value and saponification value42.Fatty acids composition percentages in Canola oilThe main values of fatty acids composition percentages in Canola oil were determined and calculated in the second season Table 5. Gas–liquid chromatographic analysis showed that, saturated fatty acids (Palmitic, 16:0, Stearic, 18:0, Arachidic, 20:0, and Behenic, 22:0) represent about 9.1 of the total fatty acids. Palmitic was the dominant acid among the saturated ones. In respect of unsaturated fatty acids i.e., Oleic acid (18:1), Linoleic (18:2), Linolenic (18:3), and Erucic (22:1), they all represent about 90.9% of total fatty acids. Therefore, Oleic acid (18:1) was the major fatty acid in Canola oil (59.43%) followed by Linoleic (20.80%) and Linolenic (9.02%). Erucic acid was less than 2%.Table 5 Saturated and unsaturated fatty acids (%) in seeds of the three Canola genotypes and different N applications without and with yeast extract.Full size tableData in Table 5, showed slight differences in saturated fatty acids between Canola varieties. AD201(G1) variety contained more amount of Palmitic (4.78%) and Stearic (1.52%) acids followed by Topaz (G2) for Palmitic and SemuDNK 234/84 (V3) for Stearic. However, Behenic acid (1.20%) was higher in G3 than G2 (1.17%), while G2 was the highest in Arachidic acid than G3 variety. These results are in line with those obtained by El Habbasha et al.43. They reported that AD 201, Silvo, and Topas (G2) were different in their oil contents of saturated and unsaturated fatty acids. Canola varieties were also slightly differed in their content of the unsaturated fatty acids Table 5, G3 variety contained more amounts of Oleic (60.36%) acid followed by the G2 variety. G1 recorded the lowest amount of Oleic acid (58.36%) in comparison with the other two varieties. On the other hand, G1 showed a high increment in Linoleic and Linolenic acids followed by G3 for Linoleic and Linolenic acids. The second oil quality breeding objective is to reduce the percentage of Linolenic acid from the percent 8–10% to less than 3% while maintaining or increasing the level of Linoleic acid44. Lower Linolenic acid is desired to improve the storage characteristics of the oil, while higher Linolenic acid content may be nutritionally desirable. Similar observations were reported by Ref.45. Topaz variety recorded the highest value for Erucic acid (1.77%) followed by AD201 variety, whereas Semu DNK gave the lowest value (1.45%). The increase in Erucic acid content in the Topaz variety may be due to the decrease in Oleic acid content46. Stated that the concentrations of Oleic and Erucic acids were negatively correlated and a high Oleic acid concentration ( > 50%) was always associated with a low Erucic acid concentration ( More