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    Effects of landscape structure on restoration success in tropical premontane forest

    Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Chazdon, R. L. Landscape restoration, natural regeneration, and the forests of the future. mobt 102, 251–257 (2017).
    Google Scholar 
    Crouzeilles, R., Lorini, M. L. & Grelle, C. Applying graph theory to design networks of protected areas: using inter-patch distance for regional conservation planning. Natureza Conservaçao Rev. Brasileira de Conservaçao da Natureza 9, 219–224 (2011).
    Google Scholar 
    Crouzeilles, R., Lorini, M. L. & Grelle, C. E. V. The importance of using sustainable use protected areas for functional connectivity. Biol. Cons. 159, 450–457 (2013).Article 

    Google Scholar 
    Arroyo-Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).PubMed 
    Article 

    Google Scholar 
    O’Farrell, P. J. & Anderson, P. M. Sustainable multifunctional landscapes: a review to implementation. Curr Opin Environ. Sustain. 2, 59–65 (2010).Article 

    Google Scholar 
    Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

    Google Scholar 
    César, R. G. et al. It is not just about time: agricultural practices and surrounding forest cover affect secondary forest recovery in agricultural landscapes. Biotropica 53, 496–508 (2021).Article 

    Google Scholar 
    Crouzeilles, R. et al. A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes. J. Appl. Ecol. 56, 2675–2686 (2019).Article 

    Google Scholar 
    Villard, M.-A. & Metzger, J. P. Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J. Appl. Ecol. 51, 309–318 (2014).Article 

    Google Scholar 
    Taylor, P. D., Fahrig, L. & With, K. A. Landscape connectivity: a return to the basics. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 29–43 (Cambridge University Press, 2006).Tischendorf, L. & Fahrig, L. On the usage and measurement of landscape connectivity. Oikos 90, 7–19 (2000).Article 

    Google Scholar 
    McRae, B. H., Hall, S. A., Beier, P. & Theobald, D. M. Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits. PLoS ONE 7, e52604 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Torrubia, S. et al. Getting the most connectivity per conservation dollar. Front. Ecol. Environ. 12, 491–497 (2014).Article 

    Google Scholar 
    Crouzeilles, R. et al. A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 11666 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leal-Ramos, D. et al. Forest and connectivity loss drive changes in movement behavior of bird species. Ecography 43, 1203–1214 (2020).Article 

    Google Scholar 
    Pérez-Cárdenas, N. et al. Effects of landscape composition and site land-use intensity on secondary succession in a tropical dry forest. For. Ecol. Manage. 482, 118818 (2021).Article 

    Google Scholar 
    Holl, K. D., Reid, J. L., Chaves-Fallas, J. M., Oviedo-Brenes, F. & Zahawi, R. A. Local tropical forest restoration strategies affect tree recruitment more strongly than does landscape forest cover. J. Appl. Ecol. 54, 1091–1099 (2017).Article 

    Google Scholar 
    Holl, K. D., Zahawi, R. A., Cole, R. J., Ostertag, R. & Cordell, S. Planting seedlings in tree islands versus plantations as a large-scale tropical forest restoration strategy. Restor. Ecol. 19, 470–479 (2011).Article 

    Google Scholar 
    Cole, R. J., Holl, K. D. & Zahawi, R. A. Seed rain under tree islands planted to restore degraded lands in a tropical agricultural landscape. Ecol. Appl. 20, 1255–1269 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zahawi, R. A., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50, 88–96 (2013).Article 

    Google Scholar 
    Reid, J. L., Kormann, U., Zarrate-Chary, D., Holl, K. D. & Zahawi, R. A. Predicting toucan-mediated seed dispersal in tropical forest restoration. Ecosphere (In press).Zahawi, R. A. et al. Proximity and abundance of mother trees affects recruitment patterns in a long-term tropical forest restoration study. Ecography 44,1826–1837 (2021).Lehouck, V. et al. Habitat disturbance reduces seed dispersal of a forest interior tree in a fragmented African cloud forest. Oikos 118, 1023–1034 (2009).Article 

    Google Scholar 
    Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).Article 

    Google Scholar 
    Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Cons. 230, 179–186 (2019).Article 

    Google Scholar 
    Schupp, E. W., Jordano, P. & Gómez, J. M. Seed dispersal effectiveness revisited: a conceptual review. New Phytol. 188, 333–353 (2010).PubMed 
    Article 

    Google Scholar 
    Rogers, H. S., Donoso, I., Traveset, A. & Fricke, E. C. Cascading impacts of seed disperser loss on plant communities and ecosystems. Annu. Rev. Ecol. Evol. Syst. 52, 641–666 (2021).Article 

    Google Scholar 
    Howe, H. F. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).Article 

    Google Scholar 
    Holdridge, L. R., Grenke, W. C., Hatheway, W. H., Liang, T. & Tosi, J. A. J. Forest environments in tropical life zones: a pilot study (Pergamon Press, 1971).
    Google Scholar 
    Zahawi, R. A., Duran, G. & Kormann, U. Sixty-seven years of land-use change in Southern Costa Rica. PLoS ONE 10, e0143554 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Holl, K. D. et al. Applied nucleation facilitates tropical forest recovery: Lessons learned from a 15-year study. J. Appl. Ecol. 57, 2316–2328 (2020).Article 

    Google Scholar 
    Reid, J. L., Mendenhall, C. D., Rosales, J. A., Zahawi, R. A. & Holl, K. D. Landscape context mediates avian habitat choice in tropical forest restoration. PLoS ONE 9, e90573 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Buchanan, G. M., Donald, P. F. & Butchart, S. H. M. Identifying priority areas for conservation: a global assessment for forest-dependent birds. PLoS ONE 6, e29080 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Carrara, E. et al. Impact of landscape composition and configuration on forest specialist and generalist bird species in the fragmented Lacandona rainforest, Mexico. Biol. Conser. 184, 117–126 (2015).Article 

    Google Scholar 
    Chao, A. & Shen, T. J. Program SPADE (Species Prediction and Diversity Estimation). Program and User’s Guide. (http://chao.stat.nthu.edu.tw, 2010).Chazdon, R. L. et al. A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343 (2011).PubMed 
    Article 

    Google Scholar 
    de Souza, R. P. & Válio, I. F. M. Seed size, seed germination, and seedling survival of Brazilian tropical tree species differing in successional status. Biotropica 33, 447–457 (2001).Article 

    Google Scholar 
    Werden, L. K., Holl, K. D., Rosales, J. A., Sylvester, J. M. & Zahawi, R. A. Effects of dispersal- and niche-based factors on tree recruitment in tropical wet forest restoration. Ecol. Appl. 30, e02139 (2020).PubMed 

    Google Scholar 
    Mendenhall, C. D., Shields-Estrada, A., Krishnaswami, A. J. & Daily, G. C. Quantifying and sustaining biodiversity in tropical agricultural landscapes. PNAS 113, 14544–14551 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jesus, F. M., Pivello, V. R., Meirelles, S. T., Franco, G. A. D. C. & Metzger, J. P. The importance of landscape structure for seed dispersal in rain forest fragments. J. Veg. Sci. 23, 1126–1136 (2012).Article 

    Google Scholar 
    Galán-Acedo, C., Arroyo-Rodríguez, V., Estrada, A. & Ramos-Fernández, G. Drivers of the spatial scale that best predict primate responses to landscape structure. Ecography 41, 2027–2037 (2018).Article 

    Google Scholar 
    Pardini, R., de Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Cons. 124, 253–266 (2005).Article 

    Google Scholar 
    Forman, R. T. T. & Godron, M. Landscape ecology. (Wiley, 1986).QGIS Development Team. QGIS Geographic Information System. (Open Source Geospatial Foundation, 2016).Gillies, C. S. & Clair, C. C. S. Riparian corridors enhance movement of a forest specialist bird in fragmented tropical forest. PNAS 105, 19774–19779 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Harvey, C. A., Tucker, N. I. & Estrada, A. Live fences, isolated trees, and windbreaks: tools for conserving biodiversity in fragmented tropical landscapes. in Agroforestry and biodiversity conservation in tropical landscapes 261–289 (2004).Harvey, C. A. et al. Contribution of live fences to the ecological integrity of agricultural landscapes. Agric. Ecosyst. Environ. 111, 200–230 (2005).Article 

    Google Scholar 
    Saura, S., Bodin, Ö. & Fortin, M.-J. EDITOR’S CHOICE: Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).Article 

    Google Scholar 
    He, H. S., DeZonia, B. E. & Mladenoff, D. J. An aggregation index (AI) to quantify spatial patterns of landscapes. Landscape Ecol. 15, 591–601 (2000).Article 

    Google Scholar 
    Radford, J. Q., Bennett, A. F. & Cheers, G. J. Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol. Cons. 124, 317–337 (2005).Article 

    Google Scholar 
    Pires, A. S., Lira, P. K., Fernandez, F. A. S., Schittini, G. M. & Oliveira, L. C. Frequency of movements of small mammals among Atlantic Coastal Forest fragments in Brazil. Biol. Conserv. 108, 229–237 (2002).Article 

    Google Scholar 
    Holbrook, K. M. Home range and movement patterns of toucans: implications for seed dispersal. Biotropica 43, 357–364 (2011).Article 

    Google Scholar 
    Şekercioğlu, Ç. H. et al. Tropical countryside riparian corridors provide critical habitat and connectivity for seed-dispersing forest birds in a fragmented landscape. J Ornithol 156, 343–353 (2015).Article 

    Google Scholar 
    Eigenbrod, F., Hecnar, S. J. & Fahrig, L. Sub-optimal study design has major impacts on landscape-scale inference. Biol. Conserv. 144, 298–305 (2011).Article 

    Google Scholar 
    McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. (2012).Jackson, H. B. & Fahrig, L. Are ecologists conducting research at the optimal scale?. Global Ecol. Biogeography 24, 52–63 (2015).Article 

    Google Scholar 
    Jackson, H. B. & Fahrig, L. What size is a biologically relevant landscape?. Landscape Ecol 27, 929–941 (2012).Article 

    Google Scholar 
    McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. Multi-scale habitat selection modeling: a review and outlook. Landscape Ecol 31, 1161–1175 (2016).Article 

    Google Scholar 
    Huais, P. Y. multifit: an R function for multi-scale analysis in landscape ecology. Landscape Ecol 33, 1023–1028 (2018).Article 

    Google Scholar 
    R Development Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Crawley, M. J. Statistical modelling in the R book. (John Wiley & Sons Ltd., 2007).Leite, M. de S., Tambosi, L. R., Romitelli, I. & Metzger, J. P. Landscape ecology perspective in restoration projects for biodiversity conservation: a review. Natureza & Conservação 11, 108–118 (2013).Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. Applied linear statistical models. (McGraw-Hill/Irwin, 1996).Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).Calcagno, V. & Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Soft. 34, 1–29 (2010).Article 

    Google Scholar 
    Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).Article 

    Google Scholar 
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).Article 

    Google Scholar 
    Fagan, M. E., DeFries, R. S., Sesnie, S. E., Arroyo-Mora, J. P. & Chazdon, R. L. Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor. Ecol. Appl. 26, 1456–1474 (2016).PubMed 
    Article 

    Google Scholar 
    Reid, J. L. & Holl, K. D. Arrival ≠ survival. Restor. Ecol. 21, 153–155 (2013).Article 

    Google Scholar 
    Pejchar, L. et al. Birds as agents of seed dispersal in a human-dominated landscape in southern Costa Rica. Biol. Cons. 141, 536–544 (2008).Article 

    Google Scholar 
    Norden, N. et al. Is temporal variation of seedling communities determined by environment or by seed arrival? A test in a neotropical forest. J. Ecol. 95, 507–516 (2007).Article 

    Google Scholar 
    Tabarelli, M., Lopes, A. V. & Peres, C. A. Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40, 657–661 (2008).Article 

    Google Scholar 
    Lôbo, D., Leão, T., Melo, F. P. L., Santos, A. M. M. & Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers. Distrib. 17, 287–296 (2011).Article 

    Google Scholar 
    Costa, J. B. P., Melo, F. P. L., Santos, B. A. & Tabarelli, M. Reduced availability of large seeds constrains Atlantic forest regeneration. Acta Oecologica 39, 61–66 (2012).ADS 
    Article 

    Google Scholar 
    Miguet, P., Jackson, H. B., Jackson, N. D., Martin, A. E. & Fahrig, L. What determines the spatial extent of landscape effects on species?. Landscape Ecol 31, 1177–1194 (2016).Article 

    Google Scholar  More

  • in

    More than half of data deficient species predicted to be threatened by extinction

    Cardillo, M. & Meijaard, E. Are comparative studies of extinction risk useful for conservation? Trends Ecol. Evol. 27, 167–171 (2012).PubMed 
    Article 

    Google Scholar 
    Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).PubMed 
    Article 

    Google Scholar 
    Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2, 81–98 (2015).
    Google Scholar 
    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Sci. (80-.). 366, eaax3100 (2019).Article 
    CAS 

    Google Scholar 
    Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Sci. (80-.) 353, 288–291 (2016).CAS 
    Article 

    Google Scholar 
    Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Sci. (80-.). 344, 1246752–1246752 (2014).CAS 
    Article 

    Google Scholar 
    IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo (2019) https://doi.org/10.5281/zenodo.3831674.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rodrigues, A., Pilgrim, J., Lamoreux, J., Hoffmann, M. & Brooks, T. The value of the IUCN Red List for conservation. Trends Ecol. Evol. 21, 71–76 (2006).PubMed 
    Article 

    Google Scholar 
    Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).PubMed 
    Article 

    Google Scholar 
    Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B. & Worm, B. How many species are there on Earth and in the Ocean? PLoS Biol. 9, e1001127 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Purvis, A. & Hector, A. Getting the measure of biodiversity. Nature 405, 212–219 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bachman, S. P. et al. Progress, challenges and opportunities for Red Listing. Biol. Conserv. 234, 45–55 (2019).Article 

    Google Scholar 
    Rondinini, C., Di Marco, M., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN red list. Conserv. Lett. 7, 126–130 (2014).Article 

    Google Scholar 
    IUCN. The IUCN Red List of Threatened Species. Version 2021-2. https://www.iucnredlist.org (2021).Cazalis, V. et al. Bridging the research-implementation gap in IUCN Red List assessments. Trends Ecol. Evol. 37, 359–370 (2022).PubMed 
    Article 

    Google Scholar 
    IUCN Standards and Petitions Committee. Guidelines for using the IUCN Red List Categories and Criteria. Prepared by the Standards and Petitions Committee. Downloadable from https://www.iucnredlist.org/documents/RedListGuidelines.pdf vol. 15 (2022).Bland, L. M. et al. Toward reassessing data‐deficient species. Conserv. Biol. 31, 531–539 (2017).PubMed 
    Article 

    Google Scholar 
    Butchart, S. H. M. & Bird, J. P. Data Deficient birds on the IUCN Red List: What don’t we know and why does it matter? Biol. Conserv. 143, 239–247 (2010).Article 

    Google Scholar 
    Zhao, L. et al. Spatial knowledge deficiencies drive taxonomic and geographic selectivity in data deficiency. Biol. Conserv. 231, 174–180 (2019).Article 

    Google Scholar 
    Parsons, E. C. M. Why IUCN should replace “Data Deficient” conservation status with a precautionary “Assume Threatened” Status—A Cetacean Case Study. Front. Mar. Sci. 3, 2015–2017 (2016).
    Google Scholar 
    Roberts, D. L., Taylor, L. & Joppa, L. N. Threatened or Data Deficient: assessing the conservation status of poorly known species. Divers. Distrib. 22, 558–565 (2016).Article 

    Google Scholar 
    Jetz, W. & Freckleton, R. P. Towards a general framework for predicting threat status of data-deficient species from phylogenetic, spatial and environmental information. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140016 (2015).Article 

    Google Scholar 
    Howard, S. D. & Bickford, D. P. Amphibians over the edge: silent extinction risk of Data Deficient species. Divers. Distrib. 20, 837–846 (2014).Article 

    Google Scholar 
    Jarić, I., Courchamp, F., Gessner, J. & Roberts, D. L. Potentially threatened: a Data Deficient flag for conservation management. Biodivers. Conserv. 25, 1995–2000 (2016).Article 

    Google Scholar 
    Mair, L. et al. A metric for spatially explicit contributions to science-based species targets. Nat. Ecol. Evol. 5, 836–844 (2021).PubMed 
    Article 

    Google Scholar 
    Butchart, S. H. M. et al. Measuring Global Trends in the status of biodiversity: red list indices for birds. PLoS Biol. 2, e383 (2004).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    United Nations. Transforming our World: the 2030 Agenda for Sustainable Development. A/RES/70/1 (2015).Butchart, S. H. M. et al. Using Red List Indices to measure progress towards the 2010 target and beyond. Philos. Trans. R. Soc. B Biol. Sci. 360, 255–268 (2005).CAS 
    Article 

    Google Scholar 
    Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Moran, D. & Kanemoto, K. Identifying species threat hotspots from global supply chains. Nat. Ecol. Evol. 1, 0023 (2017).Article 

    Google Scholar 
    Mooers, A. Ø., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for Phylogenetic Conservation Prioritization. PLoS One 3, e3700 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Runting, R. K., Phinn, S., Xie, Z., Venter, O. & Watson, J. E. M. Opportunities for big data in conservation and sustainability. Nat. Commun. 11, 2003 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).PubMed 
    Article 

    Google Scholar 
    Hino, M., Benami, E. & Brooks, N. Machine learning for environmental monitoring. Nat. Sustain 1, 583–588 (2018).Article 

    Google Scholar 
    Wearn, O. R., Freeman, R. & Jacoby, D. M. P. Responsible AI for conservation. Nat. Mach. Intell. 1, 72–73 (2019).Article 

    Google Scholar 
    Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).Article 

    Google Scholar 
    Bland, L. M. & Böhm, M. Overcoming data deficiency in reptiles. Biol. Conserv. 204, 16–22 (2016).Article 

    Google Scholar 
    Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 
    Article 

    Google Scholar 
    Luiz, O. J., Woods, R. M., Madin, E. M. P. & Madin, J. S. Predicting IUCN extinction risk categories for the World’s Data Deficient Groupers (Teleostei: Epinephelidae). Conserv. Lett. 9, 342–350 (2016).Article 

    Google Scholar 
    Stévart, T. et al. A third of the tropical African flora is potentially threatened with extinction. Sci. Adv. 5, eaax9444 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Darrah, S. E., Bland, L. M., Bachman, S. P., Clubbe, C. P. & Trias-Blasi, A. Using coarse-scale species distribution data to predict extinction risk in plants. Divers. Distrib. 23, 435–447 (2017).Article 

    Google Scholar 
    Walls, R. H. L. & Dulvy, N. K. Tracking the rising extinction risk of sharks and rays in the Northeast Atlantic Ocean and Mediterranean Sea. Sci. Rep. 11, 15397 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Walls, R. H. L. & Dulvy, N. K. Eliminating the dark matter of data deficiency by predicting the conservation status of Northeast Atlantic and Mediterranean Sea sharks and rays. Biol. Conserv. 246, 108459 (2020).Article 

    Google Scholar 
    IUCN. Species Information Service. Version 2020-3. https://www.iucnredlist.org/resources/spatial-data-download (2021).IUCN. The IUCN Red List of Threatened Species. Version 2020-3. https://www.iucnredlist.org (2020).Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).Article 

    Google Scholar 
    Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–34 (2014).Article 

    Google Scholar 
    Selig, E. R. et al. Global priorities for Marine biodiversity conservation. PLoS One 9, e82898 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    O’Hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Mittermeier, R. A., Goetsch Mittermeier, C., Gil, P. R. & Wilson, E. O. Megadiversity: Earth’s Biologically Wealthiest Nations. CEMEX (2005).Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0. (2020).GBIF. The Global Biodiversity Information Facility: What is GBIF? https://www.gbif.org/what-is-gbif (2021).OBIS. Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. (2021).Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.6.0. https://cran.r-project.org/package=rgbif (2021).Provoost, P. & Bosch, S. robis: Ocean Biodiversity Information System (OBIS) Client. R package version 2.3.9. https://CRAN.R-project.org/package=robis. (2020).Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad, Dataset https://doi.org/10.5061/dryad.kd1d4 (2018).ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. http://maps.elie.ucl.ac.be/CCI/viewer/download.php (2017).Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch‐Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Chang. Biol. 25, 811–826 (2019).PubMed 
    Article 

    Google Scholar 
    Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. 109, 16083–16088 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). Cambridge, UK: UNEP-WCMC and IUCN www.protectedplanet.net (2021).Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Sci. (80-.) 342, 850–853 (2013).CAS 
    Article 

    Google Scholar 
    Tuanmu, M. N. & Jetz, W. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 24, 1329–1339 (2015).Article 

    Google Scholar 
    Maggi, F., Tang, F. H. M., la Cecilia, D. & McBratney, A. PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Sci. Data 6, 170 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Byers, L. et al. A Global Database of Power Plants. World Resour. Inst. 1–18 (2019).Mulligan, M., van Soesbergen, A. & Sáenz, L. GOODD, a global dataset of more than 38,000 georeferenced dams. Sci. Data 7, 31 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boulay, A.-M. et al. The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). Int. J. Life Cycle Assess. 23, 368–378 (2018).Article 

    Google Scholar 
    Barbarossa, V. et al. Erratum: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015. Sci. Data 5, 180078 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl Acad. Sci. 117, 3648–3655 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Domisch, S., Amatulli, G. & Jetz, W. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci. Data 2, 150073 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 
    Article 

    Google Scholar 
    Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163 (2006).PubMed 
    Article 

    Google Scholar 
    Schlossberg, S., Chase, M. J., Gobush, K. S., Wasser, S. K. & Lindsay, K. State-space models reveal a continuing elephant poaching problem in most of Africa. Sci. Rep. 10, 10166 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Burn, R. W., Underwood, F. M. & Blanc, J. Global trends and factors associated with the illegal killing of Elephants: a hierarchical Bayesian Analysis of Carcass Encounter Data. PLoS One 6, e24165 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hauenstein, S., Kshatriya, M., Blanc, J., Dormann, C. F. & Beale, C. M. African elephant poaching rates correlate with local poverty, national corruption and global ivory price. Nat. Commun. 10, 2242 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    UNDP. Human Development Report 2020. The Next Frontier: Human Development and the Anthropocene. New York. http://hdr.undp.org/en/content/human-development-report-2020. (2020).Transparency International. Corruption Perceptions Index 2020. (2020).Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 12485 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 7615 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Sci. (80-.) 319, 948–952 (2008).CAS 
    Article 

    Google Scholar 
    Assis, J. et al. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).Article 

    Google Scholar 
    Tyberghein, L. et al. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21, 272–281 (2012).Article 

    Google Scholar 
    Zizka, A., Silvestro, D., Vitt, P. & Knight, T. M. Automated conservation assessment of the orchid family with deep learning. Conserv. Biol. 35, 897–908 (2021).PubMed 
    Article 

    Google Scholar 
    Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning. The Elements of Statistical Learning vol. 27 (Springer New York, 2001).Kampichler, C., Wieland, R., Calmé, S., Weissenberger, H. & Arriaga-Weiss, S. Classification in conservation biology: a comparison of five machine-learning methods. Ecol. Inform. 5, 441–450 (2010).Article 

    Google Scholar 
    LeDell, E. et al. h2o: R Interface for the ‘H2O’ Scalable Machine Learning Platform. R package version 3.36.0.4. https://github.com/h2oai/h2o-3 (2022).H2O.ai. H2O AutoML. https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html (2022).Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
    Article 

    Google Scholar 
    Kuhn, M. Building Predictive Models in R using the caret Package. J. Stat. Softw. 28, 1–26 (2008).Article 

    Google Scholar 
    Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).Article 

    Google Scholar 
    Harrell Jr, F. E. Hmisc: Harrell miscellaneous. R package version 4.5-0. (2021).van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super Learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://www.r-project.org/ (2021).RStudio Team. RStudio: integrated development environment for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://cran.r-project.org/package=raster (2019).Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. https://cran.r-project.org/package=rgdal (2019).Bivand, R. & Lewin-Koh, N. maptools: Tools for Handling Spatial Objects. R package version 0.9-5. https://cran.r-project.org/package=maptools/ (2019).Bivand, R. & Rundel, C. rgeos: Interface to Geometry Engine – Open Source (‘GEOS’). R package version 0.5-1. https://cran.r-project.org/package=rgeos (2019).Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R. (Springer New York, 2013).Pebesma, E. Simple features for R: standardized support for Spatial Vector Data. R. J. 10, 439 (2018).Article 

    Google Scholar 
    Ross, N. Fasterize: Fast Polygon to Raster Conversion. R package version 1.0.3. https://CRAN.R-project.org/package=fasterize (2020).Microsoft Corporation & Weston, S. doParallel: Foreach Parallel Adaptor for the ‘parallel’ Package. R package version 1.0.16. https://CRAN.R-project.org/package=doParallel (2020).Wickham, H. stringr: simple, consistent wrappers for common string operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr (2019).Tuszynski, J. caTools: tools: Moving Window Statistics, GIF, Base64, ROC AUC, etc. R package version 1.18.1. https://CRAN.R-project.org/package=caTools (2021).Wickham, H. et al. Welcome to the tidyverse. Journal of Open Source Software, 4, 1686. https://doi.org/10.21105/joss.01686 (2019).Dragulescu, A. & Arendt, C. xlsx: Read, Write, Format Excel 2007 and Excel 97/2000/XP/2003 Files. R package version 0.6.5. (2020).Wickham, H. & Bryan, J. readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl (2019).ESRI. ArcGIS Pro version 2.9.0. https://www.esri.com/en-us/home (2022).Kuhn, M. caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, NY (2016).Wilke, C. O. ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.3. https://CRAN.R-project.org/package=ggridges (2021).South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0. https://CRAN.R-project.org/package=rnaturalearth (2017).Garnier, S. viridis: Default Color Maps from ‘matplotlib’. R package version 0.5.1. https://CRAN.R-project.org/package=viridis (2018).Borgelt, J. jannebor/dd_forecast: Code for study ‘More than half of Data Deficient species predicted to be threatened by extinction’ (v1.0.1). https://doi.org/10.5281/zenodo.6627688.Zenodo (2022). More

  • in

    Climate change did not alter the effects of Bt maize on soil Collembola in northeast China

    Chaudhary, G. & Singh, S. K. Global status of genetically modified crops and its commercialization: environmental issues in logistics and manufacturing. (2019).Zwahlen, C., Hilbeck, A., Gugerli, P. & Nentwig, W. Degradation of the Cry1Ab protein within transgenic Bacillus thuringiensis corn tissue in the field. Mol. Ecol. 12, 765–775 (2010).Article 

    Google Scholar 
    Kamota, A., Muchaonyerwa, P. & Mnkeni, P. N. S. Decomposition of surface-applied and soil-incorporated Bt maize leaf litter and Cry1Ab protein during winter fallow in South Africa. Pedosphere 24, 251–257 (2014).CAS 
    Article 

    Google Scholar 
    Xue, K., Diaz, B. R. & Thies, J. E. Stability of Cry3Bb1 protein in soils and its degradation in transgenic corn residues. Soil Biol. Biochem. 76, 119–126 (2014).CAS 
    Article 

    Google Scholar 
    Griffiths, N. A. et al. Occurrence, leaching, and degradation of Cry1Ab protein from transgenic maize detritus in agricultural streams. Sci. Total Environ. 592, 97–105 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wang, B. F., Yin, J. Q., Wu, F. C., Jiang, Z. L. & Song, X. Y. Field decomposition of Bt-506 maize leaves and its effect on Collembola in the black soil region of Northeast China. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2021.e01480 (2021).Article 

    Google Scholar 
    Shu, Y. H., Zhang, Y. Y., Zeng, H., Zhang, Y. H. & Wang, J. W. Effects of Cry1Ab Bt maize straw return on bacterial community of earthworm Eisenia Fetida. Chemosphere 173, 1–13 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Čerevková, A., Miklisová, D., Szoboszlay, M. S., Tebbe, C. C. & Cagáň, L. The responses of soil nematode communities to Bt maize cultivation at four field sites across Europe. Soil Biol. Biochem. 119, 194–202 (2018).Article 
    CAS 

    Google Scholar 
    Liu, T. et al. Root and detritus of transgenic Bt crop did not change nematode abundance and community composition but enhanced trophic connections. Sci. Total Environ. 644, 822–829 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Domínguez, M. T., Holthof, E., Smith, A. R., Koller, E. & Emmett, B. A. Contrasting response of summer soil respiration and enzyme activities to long-term warming and drought in a wet shrubland (NE Wales, UK). Appl. Soil Ecol. 110, 151–155 (2016).Article 

    Google Scholar 
    Zhang, Q. F. et al. Are the combined effects of warming and drought on foliar C:N:P:K stoichiometry in a subtropical forest greater than their individual effects?. Forest Ecol. Manag. 448, 256–266 (2019).Article 

    Google Scholar 
    Chen, Q., Niu, B., Hu, Y., Luo, T. & Zhang, G. Warming and increased precipitation indirectly affect the composition and turnover of labile-fraction soil organic matter by directly affecting vegetation and microorganisms. Sci. Total Environ. 714, 136787.1-136787.9 (2020).
    Google Scholar 
    Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).Article 

    Google Scholar 
    Martin, J. T., Pederson, G. T., Woodhouse, C. A., Cook, E. R. & King, J. Increased drought severity tracks warming in the United States’ largest river basin. Proc. Natl. Acad. Sci. USA 117, 201916208 (2020).
    Google Scholar 
    Ma, S., Zhu, C. & Liu, J. Combined impacts of warm central equatorial pacific sea surface temperatures and anthropogenic warming on the 2019 severe drought in east China. Adv. Atmos. Sci. 37, 1149–1163 (2020).Article 

    Google Scholar 
    Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north–south European gradient. Ecosystems 7, 598–612 (2004).Article 

    Google Scholar 
    Sardans, J., Peñuelas, J. & Estiarte, M. Warming and drought alter soil phosphatase activity and soil P availability in a Mediterranean shrubland. Plant Soil 289, 227–238 (2006).CAS 
    Article 

    Google Scholar 
    Viciedo, D. O., Prado, R., Martinez, C. A., Habermann, H. & Piccolo, M. Short-term warming and water stress affect Panicum maximum Jacq. stoichiometric homeostasis and biomass production. Sci. Total Environ. 681, 267–274 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    Meeran, K. et al. Warming and elevated CO2 intensify drought and recovery responses of grassland carbon allocation to soil respiration. Glob. Change Biol. 27, 3230–3243 (2021).Article 

    Google Scholar 
    Lang, B., Rall, B. C., Scheu, S. & Brose, U. Effects of environmental warming and drought on size-structured soil food webs. Oikos 123, 1224–1233 (2014).Article 

    Google Scholar 
    Pold, G., Melillo, J. M. & Deangelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. 6, 480 (2010).
    Google Scholar 
    Séneca, J. et al. Composition and activity of nitrifier communities in soil are unresponsive to elevated temperature and CO2, but strongly affected by drought. ISME J. 14, 1–16 (2020).Article 
    CAS 

    Google Scholar 
    Santos, A. et al. Water stress alters lignin content and related gene expression in two sugarcane genotypes. J. Agric. Food Chem. 63, 4708 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Albert, K. R. et al. Effects of elevated CO2, warming and drought episodes on plant carbon uptake in a temperate heath ecosystem are controlled by soil water status. Plant Cell Environ. 34, 1207–1222 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north-south European gradient. Ecosystems 7, 598–612 (2004).Article 

    Google Scholar 
    Zhu, E., Cao, Z., Jia, J., Liu, C. & Feng, X. Inactive and inefficient: Warming and drought effect on microbial carbon processing in alpine grassland at depth. Glob. Change Biol. https://doi.org/10.1111/gcb.15541 (2021).Article 

    Google Scholar 
    Sardans, J., Peñuelas, J. & Estiarte, M. Changes in soil enzymes related to C and N cycle and in soil C and N content under prolonged warming and drought in a Mediterranean shrubland. Appl. Soil Ecol. 39, 223–235 (2008).Article 

    Google Scholar 
    Xu, G. L. et al. Seasonal exposure to drought and air warming affects soil Collembola and Mites. PLoS ONE 7, e43102 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chang, L. et al. Warming limits daytime but not nighttime activity of epigeic microarthropods in Songnen grasslands. Appl. Soil Ecol. 141, 79–83 (2019).Article 

    Google Scholar 
    Dai, A. G., Trenberth, K. E. & Qian, T. T. A global dataset of palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130 (2004).ADS 
    Article 

    Google Scholar 
    Bongaarts, J. Intergovernmental panel on climate change special report on global warming of 1.5 °C Switzerland: IPCC, 2018. Popul. Dev. Rev. 45, 251–252 (2019).Article 

    Google Scholar 
    Bellinger, P.F., Christiansen, K. A. & Janssens, F. Checklist of the Collembola of the World. 1996–2019. http://www.collembola.org (Accessed 10 Sept 2021).Hopkin, S. P. Biology of the Springtails (Insecta:Collembola) 1–330 (Oxford University Press, 1997).
    Google Scholar 
    Rusek, J. Biodiversity of Collembola and their functional role in the ecosystem. Biodivers. Conserv. 7, 1207–1219 (1998).Article 

    Google Scholar 
    Filser, J. The role of Collembola in carbon and nitrogen cycling in soil. Pedobiologia 46, 234–245 (2002).
    Google Scholar 
    Endlweber, K. & Scheu, S. Effects of Collembola on root properties of two competing ruderal plant species. Soil Biol. Biochem. 38, 2025–2031 (2006).CAS 
    Article 

    Google Scholar 
    Rebek, E. J., Hogg, D. B. & Young, D. K. Effect of four cropping systems on the abundance and diversity of epedaphic Springtails (Hexapoda: Parainsecta: Collembola) in southern Wisconsin. Environ. Entomol. 31, 37–46 (2002).Article 

    Google Scholar 
    Santorufo, L. et al. An assessment of the influence of the urban environment on collembolan communities in soils using taxonomy- and trait-based approaches. Appl. Soil Ecol. 78, 48–56 (2014).Article 

    Google Scholar 
    Rossetti, I. et al. Isolated cork oak trees affect soil properties and biodiversity in a Mediterranean wooded grassland. Agric. Ecosyst. Environ. 202, 203–216 (2015).Article 

    Google Scholar 
    Hönemann, L., Zurbrügg, C. & Nentwig, W. Effects of Bt-corn decomposition on the composition of the soil meso- and macrofauna. Appl. Soil Ecol. 40, 203–209 (2008).Article 

    Google Scholar 
    Arias-Martín, M. et al. Effects of three-year cultivation of Cry1Ab-expressing Bt maize on soil microarthropod communities. Agric. Ecosyst. Environ. 220, 125–134 (2016).Article 
    CAS 

    Google Scholar 
    Song, X. Y. et al. Use of taxonomic and trait-based approaches to evaluate the effects of transgenic Cry1Ac corn on the community characteristics of soil Collembola. Environ. Entomol. 48, 263–269 (2019).PubMed 
    Article 

    Google Scholar 
    Thibaud, J. M. Intermue ettemperatures lethales chez les insects collemboles arthropleones. II.—Isotomidae, Entomobryidae et Tomoceridae. Rev. Ecol. Biol. Sol. 14, 267–278 (1977).
    Google Scholar 
    Eisenbeis, G. & Wichard, W. Atlas on the Biology of Soil Arthropods 200–228 (Springer, 1987).Book 

    Google Scholar 
    Wang, B. F., Wu, F. C., Yin, J. Q., Jiang, Z. L. & Song, X. Y. Use of taxonomic and trait-based approaches to evaluate the effect of Bt maize expressing cry1Ie protein on non-target Collembola: A case study in Northeast China. Insects. https://doi.org/10.3390/insects12020088 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chang, L., Song, X. Y., Wang, B. F., Wu, D. H. & Reddy, G. Effect of Bt corn (Bt 38) cultivation on community structure of Collembola. Ann. Entomol. Soc. Am. 113, 1–5 (2020).CAS 
    Article 

    Google Scholar 
    Al-Deeb, M., Wilde, G. E., Blair, J. M. & Todd, T. C. Effect of Bt corn for corn rootworm control on nontarget soil microarthropods and nematodes. Environ. Entomol. 32, 859–865 (2003).Article 

    Google Scholar 
    Bitzer, R. J., Rice, M. E., Pilcher, C. D., Pilcher, C. L. & Lam, W. F. Biodiversity and community structure of epedaphic and euedaphic springtails (Collembola) in transgenic rootworm Bt maize. Environ. Entomol. 34, 1346–1376 (2005).Article 

    Google Scholar 
    Yang, Y. et al. Toxicological and biochemical analyses demonstrate no toxic effect of Cry1C and Cry2A to Folsomia candida. Sci. Rep. 5, 15619 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jiang, Z., Zhou, L., Wang, B. F., Wang, D. M. & Song, X. Y. Toxicological and biochemical analyses demonstrate no toxic effect of Bt maize on the Folsomia candida. PLoS ONE 15, e0232747 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Frouz, J., Elhottová, D., Helingerová, M. & Kocourek, F. The effect of bt corn on soil invertebrates, soil microbial community and decomposition rates of corn post-harvest residues under field and laboratory conditions. J. Sustain. Agric. 32, 645–655 (2008).Article 

    Google Scholar 
    Daghighi, E., Filser, J., Koehler, H. & Kesel, R. Long-term succession of Collembola communities in relation to climate change and vegetation. Pedobiologia 64, 25–38 (2017).Article 

    Google Scholar 
    Chang, L. et al. Green more than brown food resources drive the effect of simulated climate change on Collembola: A soil transplantation experiment in Northeast China. Geoderma 392, 115008 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Convey, P., Block, W. & Peat, H. J. Soil arthropods as indicators of water stress in Antarctic terrestrial habitats. Glob. Change Biol. 9, 1718–1730 (2003).ADS 
    Article 

    Google Scholar 
    Alvarez, T., Frampton, G. K. & Goulson, D. The effects of drought upon epigeal Collembola from arable soils. Agric. For. Entomol. 1, 243–248 (2015).Article 

    Google Scholar 
    Fountain, M. T. & Hopkin, S. P. Folsomia candida (collembola): A “standard” soil arthropod. Annu. Rev. Entomol. 50, 201–222 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Holmstrup, M. Water relations and drought sensitivity of Folsomia candida eggs (Collembola: Isotomidae). Eur. J. Entomol. 116, 229–234 (2019).Article 

    Google Scholar 
    Meehan, M. L., Barreto, C., Turnbull, M. S., Bradley, R. L. & Lindo, Z. Response of soil fauna to simulated global change factors depends on ambient climate conditions. Pedobiologia 83, 150672 (2020).Article 

    Google Scholar 
    Harte, J., Rawa, A. & Price, V. Effects of manipulated soil microclimate on mesofaunal biomass and diversity. Soil Biol. Biochem. 28, 313–322 (1996).CAS 
    Article 

    Google Scholar 
    Lindberg, N. Soil fauna and global change: responses to experimental drought, irrigation, fertilisation and soil warming. Acta Universitatis Agriculturae Sueciae Silvestria 37, + Papers I-IV (2003).Bokhorst, S. et al. Extreme winter warming events more negatively impact small rather than large soil fauna: shift in community composition explained by traits not taxa. Global Change Biolo. 18, 1152–1162 (2012).Macfadyen, A. Improved funnel-type extractors for soil arthropods. J. Anim. Ecol. 30, 171–184 (1961).Article 

    Google Scholar 
    Christiansen, K. A. & Bellinge, P. F. The Collembola of North America, North of the Rio Grande: A Taxonomic Analysis 2nd edn. (Grinnell College, 1998).
    Google Scholar 
    Fjellberg, A. The Collembola of Fennoscandia and Denmark. Part II: Entomobryomorpha and Symphypleona. In Fauna Entomologica Scandinavica, Vol. 42, 1−264 (Koninklijke Brill, 2007).Potapov, M. Synopses on Palaearctic Collembola: Isotomidae. Abhandlungen und Berichte des Naturkundemuseums, Görlitz, Poland 73, 1–603 (2001).
    Google Scholar 
    Yin, W. Y. Pictorial Keys to Soil Animals of China. 282−292, 592−600 (Science Press, 1998).Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

    Google Scholar 
    Cerabolini, B., Pierce, S., Luzzaro, A. & Ossola, A. Species evenness affects ecosystem processes in situ via diversity in the adaptive strategies of dominant species. Plant Ecol. 207, 333–345 (2010).Article 

    Google Scholar  More

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    Effects of organic fertilizers on growth characteristics and fruit quality in Pear-jujube in the Loess Plateau

    Effect of different organic fertilizers on the growth of Pear-jujubeEffect of different organic fertilizers on the bearing branch length of Pear-jujubeJujube-bearing branch has the dual role of fruiting and photosynthesis32,33. It can be seen from Fig. 1 that different organic fertilizer treatments have a significant impact on the growth of jujube-bearing branches. Among them, the longest jujube-bearing branch in the SC treatment is 20.17 cm, which is significantly higher than that in CK and CF; the jujube-bearing branch length in the SC, SM and BM treatment are increased by 34%, 23% and 25% compared with that in CK, and the difference is significant (P  SM  > SC  > CK. Among them, the density of light of BM is the largest. It reaches 38.06 mol/(m2 d). CF, SC, SM and BM respectively increase by 11.54%, 8.09%, 7.96% and 15.13% compared with CK, and the difference is significant. The canopy transmittance of jujube is BM  CF  > SM  > SC. The highest Tr of BM reaches 8.66 µmol/moL. It may be related to higher LAI, and the instantaneous water use efficiency of SC is highest, which reaches 3.30%. The WUEp of CF, SC, SM and BM treatments increase by 22.4%, 64.2%, 44.3% and 30.8%, respectively, compared with that of CK. It reaches a significant difference level (P  SM  > BM  > CF  > CK. Compared with CK (9.37%), the SC, SM, BM, and CF increased by 3.69, 3.18, 1.11 and 0.40% points, respectively. Organic fertilizer is beneficial to increase the water content of the soil. Among them, soybean cake fertilizer (SC) has the largest increase, which is significantly different from CK (P  SM  > SC  > CF  > CK. The RWC of BM reaches 94.20%, which is significantly different from CK (P  SM  > BM  > CK. The total flavonoid content of SC reaches 14.35 mg/kg, which is 24.57% higher than that of CK. The total flavonoid content of SM and BM increase by 17.01% and 9.2%, respectively, compared with that of CK. Moreover, each treatment is significantly different from CK (P  More

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    Humans pressure wetland multifunctionality

    Daskalova, G. N. et al. Science 368, 1341–1347 (2020).CAS 
    Article 

    Google Scholar 
    Cardinale, B. J. et al. Nature 486, 59–67 (2012).CAS 
    Article 

    Google Scholar 
    Hector, A. & Bagchi, R. Nature 448, 188–190 (2007).CAS 
    Article 

    Google Scholar 
    Fanin, N. et al. Nat. Ecol. Evol. 2, 269–278 (2018).Article 

    Google Scholar 
    Duffy, J. E. Front. Ecol. Environ. 7, 437–444 (2009).Article 

    Google Scholar 
    Manning, P. et al. Adv. Ecol. Res. 61, 323–356 (2019).Article 

    Google Scholar 
    Lefcheck, J. S. et al. Nat. Commun. 6, 6936 (2015).CAS 
    Article 

    Google Scholar 
    Soliveres, S. et al. Nature 536, 456–459 (2016).CAS 
    Article 

    Google Scholar 
    Moi, D. A. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01827-7 (2022).Article 

    Google Scholar 
    Venter, O. et al. Sci. Data 3, 160067 (2016).Article 

    Google Scholar 
    Allan, E. et al. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
    Article 

    Google Scholar 
    Manning, P. et al. Nat. Ecol. Evol. 2, 427–436 (2018).Article 

    Google Scholar 
    Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

    Google Scholar 
    Schuldt, A. et al. Nat. Commun. 9, 2989 (2018).Article 

    Google Scholar 
    Jochum, M. et al. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 

    Google Scholar 
    Dudgeon, D. et al. Biol. Rev. 81, 163–182 (2005).Article 

    Google Scholar 
    Blois, J. L. et al. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).CAS 
    Article 

    Google Scholar 
    França, F. et al. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

    Google Scholar 
    Ewers, R. M. et al. Nat. Commun. 6, 6836 (2015).CAS 
    Article 

    Google Scholar 
    Reich, P. B. et al. Science 336, 589–592 (2012).CAS 
    Article 

    Google Scholar  More

  • in

    Soil carbon stocks in forest-tundra ecotones along a 500 km latitudinal gradient in northern Norway

    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, 1–11 (2009).Article 
    CAS 

    Google Scholar 
    Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wardle, D. A., Nilsson, M. C., Zackrisson, O. & Gallet, C. Determinants of litter mixing effects in a Swedish boreal forest. Soil Biol. Biochem. 35, 827–835 (2003).CAS 
    Article 

    Google Scholar 
    Moen, J., Cairns, D. M. & Lafon, C. W. Factors structuring the treeline ecotone in Fennoscandia. Plant Ecol. Divers. 1, 77–87 (2008).Article 

    Google Scholar 
    Sjögersten, S. & Wookey, P. A. Climatic and resource quality controls on soil respiration across a forest-tundra ecotone in Swedish Lapland. Soil Biol. Biochem. 34, 1633–1646 (2002).Article 

    Google Scholar 
    Sjögersten, S., Turner, B. L., Mahieu, N., Condron, L. M. & Wookey, P. A. Soil organic matter biochemistry and potential susceptibility to climatic change across the forest-tundra ecotone in the Fennoscandian mountains. Glob. Change Biol. 9, 759–772 (2003).ADS 
    Article 

    Google Scholar 
    IPCC. IPCC report global warming of 1.5 °C. Ipcc Sr15. 2, 17–20 (2018).
    Google Scholar 
    Hobbie, S. E., Nadelhoffer, K. J. & Högberg, P. A synthesis: The role of nutrients as constraints on carbon balances in boreal and arctic regions. Plant Soil 242, 163–170 (2002).CAS 
    Article 

    Google Scholar 
    DeLuca, T. H. & Boisvenue, C. Boreal forest soil carbon: Distribution, function and modelling. Forestry 85, 161–184 (2012).Article 

    Google Scholar 
    Hansson, A., Dargusch, P. & Shulmeister, J. A review of modern treeline migration, the factors controlling it and the implications for carbon storage. J. Mt. Sci. 18, 291–306 (2021).Article 

    Google Scholar 
    Sjögersten, S. & Wookey, P. A. The impact of climate change on ecosystem carbon dynamics at the Scandinavian mountain birch forest-tundra heath ecotone. Ambio 38, 2–10 (2009).PubMed 
    Article 

    Google Scholar 
    Rustad, L. E. et al. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 126, 543–562 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Kullman, L. Rapid recent range-margin rise of tree and shrub species in the Swedish Scandes. J. Ecol. 90, 68–77 (2002).Article 

    Google Scholar 
    Lloyd, A. H. & Fastie, C. L. Recent changes in treeline forest distribution and structure in interior Alaska. Ecoscience 10, 176–185 (2003).Article 

    Google Scholar 
    Truong, C., Palmé, A. E. & Felber, F. Recent invasion of the mountain birch Betula pubescens ssp. tortuosa above the treeline due to climate change: Genetic and ecological study in northern Sweden. J. Evol. Biol. 20, 369–380 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Danby, R. K. & Hik, D. S. Variability, contingency and rapid change in recent subarctic alpine tree line dynamics. J. Ecol. 95, 352–363 (2007).Article 

    Google Scholar 
    Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).PubMed 
    Article 

    Google Scholar 
    Tingstad, L., Olsen, S. L., Klanderud, K., Vandvik, V. & Ohlson, M. Temperature, precipitation and biotic interactions as determinants of tree seedling recruitment across the tree line ecotone. Oecologia 179, 599–608 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    Hofgaard, A. Inter-Relationships between treeline position, species diversity, land use and climate change in the Central Scandes Mountains of Norway. Annika Hofgaard Source Glob. Ecol. Biogeogr. Lett. 6(6), 419–429 (1997).Article 

    Google Scholar 
    Olsson, E. G. A., Austrheim, G. & Grenne, S. N. Landscape change patterns in mountains, land use and environmental diversity, Mid-Norway 1960–1993. Landsc. Ecol. 15, 155–170 (2000).Article 

    Google Scholar 
    Weintraub, M. N. & Schimel, J. P. Interactions between carbon and nitrogen mineralization and soil organic matter chemistry in arctic tundra soils. Ecosystems 6, 129–143 (2003).CAS 
    Article 

    Google Scholar 
    Melillo, J. M. et al. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173–2176 (2002).Kammer, A. et al. Treeline shifts in the Ural mountains affect soil organic matter dynamics. Glob. Change Biol. 15, 1570–1583 (2009).ADS 
    Article 

    Google Scholar 
    Parker, T. C., Subke, J. A. & Wookey, P. A. Rapid carbon turnover beneath shrub and tree vegetation is associated with low soil carbon stocks at a subarctic treeline. Glob. Change Biol. 21, 2070–2081 (2015).ADS 
    Article 

    Google Scholar 
    Speed, J. D. M. et al. Continuous and discontinuous variation in ecosystem carbon stocks with elevation across a treeline ecotone. Biogeosciences 12, 1615–1627 (2015).ADS 
    Article 

    Google Scholar 
    Hartley, I. P. et al. A potential loss of carbon associated with greater plant growth in the European Arctic. Nat. Clim. Chang. 2, 875–879 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Yoo, K., Amundson, R., Heimsath, A. M. & Dietrich, W. E. Spatial patterns of soil organic carbon on hillslopes: Integrating geomorphic processes and the biological C cycle. Geoderma 130, 47–65 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhu, M. et al. Soil organic carbon as functions of slope aspects and soil depths in a semiarid alpine region of Northwest China. CATENA 152, 94–102 (2017).CAS 
    Article 

    Google Scholar 
    Hilli, S., Stark, S. & Derome, J. Litter decomposition rates in relation to litter stocks in boreal coniferous forests along climatic and soil fertility gradients. Appl. Soil Ecol. 46, 200–208 (2010).Article 

    Google Scholar 
    Parker, T. C. et al. Exploring drivers of litter decomposition in a greening Arctic: Results from a transplant experiment across a treeline. Ecology 99, 2284–2294 (2018).PubMed 
    Article 

    Google Scholar 
    Strand, L. T., Callesen, I., Dalsgaard, L. & de Wit, H. A. Carbon and nitrogen stocks in Norwegian forest soils—The importance of soil formation, climate, and vegetation type for organic matter accumulation. Can. J. For. Res. 46, 1459–1473 (2016).CAS 
    Article 

    Google Scholar 
    Thieme, N., Bollandsås, O. M., Gobakken, T. & Næsset, E. Detection of small single trees in the forest-tundra ecotone using height values from airborne laser scanning. Can. J. Remote Sens. 37, 264–274 (2011).ADS 
    Article 

    Google Scholar 
    Mienna, I. M., Klanderud, K., Ørka, H. O., Bryn, A. & Bollandsås, O. M. Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral- and three-dimensional information from UAV -based aerial imagery. Remote Sens. Ecol. Conserv. https://doi.org/10.1002/rse2.260 (2022).Article 

    Google Scholar 
    Tveito, O. E., Bjørdal, I., Skjelvåg, A. O. & Aune, B. A GIS-based agro-ecological decision system based on gridded climatology. Meteorol. Appl. 12, 57–68 (2005).ADS 
    Article 

    Google Scholar 
    Carter, T. R. Changes in the thermal growing season in Nordic countries during the past century and prospects for the future. Agric. Food Sci. Finl. 7, 161–179 (1998).Article 

    Google Scholar 
    Abdi, H. Partial least square regression PLS-regression. Encyclopedia Res. Methods Social Sci. 792.295 (2003).Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).CAS 
    Article 

    Google Scholar 
    Liland, K. H., Mevik, B.-H., Wehrens, R. & Hiemstra, P. Package ‘ pls ’. (2021).Mevik, B.-H. & Wehrens, R. Introduction to the pls Package. Help Sect. ‘pls’ Packag. RStudio Softw. 1–23 (2015).Huang, X. et al. Soil moisture dynamics within soil profiles and associated environmental controls. CATENA 136, 189–196 (2016).Article 

    Google Scholar 
    Trap, J., Hättenschwiler, S., Gattin, I. & Aubert, M. Forest ageing: An unexpected driver of beech leaf litter quality variability in European forests with strong consequences on soil processes. For. Ecol. Manage. 302, 338–345 (2013).Article 

    Google Scholar 
    Sørensen, M. V. et al. Draining the pool? Carbon storage and fluxes in three alpine plant communities. Ecosystems 21, 316–330 (2018).Article 
    CAS 

    Google Scholar 
    Qian, H., Joseph, R. & Zeng, N. Enhanced terrestrial carbon uptake in the Northern High Latitudes in the 21st century from the Coupled Carbon Cycle Climate Model Intercomparison Project model projections. Glob. Chang. Biol. 16, 641–656 (2010).ADS 
    Article 

    Google Scholar 
    Sturm, M. et al. Snow—Shrub interactions in Arctic Tundra : A hypothesis with climatic implications. J. Clim. 14, 336–344 (2001).ADS 
    Article 

    Google Scholar 
    Grogan, P. & Jonasse, S. Ecosystem CO2 production during winter in a Swedish subarctic region: The relative importance of climate and vegetation type. Glob. Change Biol. 12, 1479–1495 (2006).ADS 
    Article 

    Google Scholar 
    Sistla, S. A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497, 615–617 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wiesmeier, M. et al. Soil organic carbon storage as a key function of soils—A review of drivers and indicators at various scales. Geoderma 333, 149–162 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Brooks, P. D. & Williams, M. W. Snowpack controls on nitrogen cycling and export in seasonally snow-covered catchments. Hydrological processes 13, 2177–2190 (1999).Broll, G. et al. Landscape mosaic in the treeline ecotone on Mt. Rodjanoaivi, Subarctic Finland. Fenn. J. Geogr. 185, 89–105 (2007).
    Google Scholar 
    Turetsky, M. R. The role of bryophytes in carbon and nitrogen cycling. Bryologist 106, 395–409 (2003).Article 

    Google Scholar  More

  • in

    Linking personality traits and reproductive success in common marmoset (Callithrix jacchus)

    Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 
    Article 

    Google Scholar 
    Smith, B. R. & Blumstein, D. T. Fitness consequences of personality: A meta-analysis. Behav. Ecol. 19, 448–455 (2008).Article 

    Google Scholar 
    Gasparini, C., Speechley, E. M. & Polverino, G. The bold and the sperm: Positive association between boldness and sperm number in the guppy. R. Soc. Open Sci. 6, 190474 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jokela, M., Alvergne, A., Pollet, T. V. & Lummaa, V. Reproductive behavior and personality traits of the five factor model. Eur. J. Pers. 25, 487–500 (2011).Article 

    Google Scholar 
    Schuett, W., Dall, S. R. X. & Royle, N. J. Pairs of zebra finches with similar ‘personalities’ make better parents. Anim. Behav. 81, 609–618 (2011).Article 

    Google Scholar 
    Vetter, S. G. et al. Shy is sometimes better: Personality and juvenile body mass affect adult reproductive success in wild boars, Sus scrofa. Anim. Behav. 115, 193–205 (2016).Article 

    Google Scholar 
    Weiss, A. Personality traits: A view from the animal kingdom. J. Pers. 86, 12–22 (2018).PubMed 
    Article 

    Google Scholar 
    Bergmüller, R. & Taborsky, M. Animal personality due to social niche specialisation. Trends Ecol. Evol. 25, 504–511 (2010).PubMed 
    Article 

    Google Scholar 
    Montiglio, P. O., Wey, T. W., Chang, A. T., Fogarty, S. & Sih, A. Correlational selection on personality and social plasticity: Morphology and social context determine behavioural effects on mating success. J. Anim. Ecol. 86, 213–226 (2017).PubMed 
    Article 

    Google Scholar 
    Wolf, M. & McNamara, J. M. On the evolution of personalities via frequency-dependent selection. Am. Nat. 179, 679–692 (2012).PubMed 
    Article 

    Google Scholar 
    Munson, A. A., Jones, C., Schraft, H. & Sih, A. You’re just my type: Mate choice and behavioral types. Trends Ecol. Evol. 35, 823–833 (2020).PubMed 
    Article 

    Google Scholar 
    Muller, H. & Chittka, L. Animal personalities: The advantage of diversity. Curr. Biol. 18, 961–963 (2008).Article 
    CAS 

    Google Scholar 
    Biro, P. A. & Stamps, J. A. Are animal personality traits linked to life-history productivity?. Trends Ecol. Evol. 23, 361–368 (2008).PubMed 
    Article 

    Google Scholar 
    Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. B 271, 847–852 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boon, A. K., Réale, D. & Boutin, S. The interaction between personality, offspring fitness and food abundance in North American red squirrels. Ecol. Lett. 10, 1094–1104 (2007).PubMed 
    Article 

    Google Scholar 
    Nicolaus, M., Tinbergen, J. M., Ubels, R., Both, C. & Dingemanse, N. J. Density fluctuations represent a key process maintaining personality variation in a wild passerine bird. Ecol. Lett. 19, 478–486 (2016).PubMed 
    Article 

    Google Scholar 
    Altschul, D. M. et al. Personality links with lifespan in chimpanzees. eLife 7, e33781 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Réale, D., Martin, J., Coltman, D. W., Poissant, J. & Festa-Bianchet, M. Male personality, life-history strategies and reproductive success in a promiscuous mammal. J. Evol. Biol. 22, 1599–1607 (2009).PubMed 
    Article 

    Google Scholar 
    Brent, L. J. N. et al. Personality traits in rhesus macaques (Macaca mulatta) are heritable but do not predict reproductive output. Int. J. Primatol. 35, 188–209 (2014).PubMed 
    Article 

    Google Scholar 
    Rangassamy, M., Dalmas, M., Féron, C., Gouat, P. & Rödel, H. G. Similarity of personalities speeds up reproduction in pairs of a monogamous rodent. Anim. Behav. 103, 7–15 (2015).Article 

    Google Scholar 
    Schuett, W., Tregenza, T. & Dall, S. R. X. Sexual selection and animal personality. Biol. Rev. 85, 217–246 (2010).PubMed 
    Article 

    Google Scholar 
    Carlstead, K., Fraser, J., Bennett, C. & Kleiman, D. G. Black rhinoceros (Diceros bicornis) in US zoos: II. Behavior, breeding success, and mortality in relation to housing facilities. Zoo Biol. 18, 35–52 (1999).Article 

    Google Scholar 
    Martin-Wintle, M. S. et al. Do opposites attract? Effects of personality matching in breeding pairs of captive giant pandas on reproductive success. Biol. Conserv. 207, 27–37 (2017).Article 

    Google Scholar 
    Fox, R. A. & Millam, J. R. Personality traits of pair members predict pair compatibility and reproductive success in a socially monogamous parrot breeding in captivity. Zoo Biol. 33, 166–172 (2014).PubMed 
    Article 

    Google Scholar 
    Choi, S., Grocutt, E., Erlandsson, R. & Angerbjörn, A. Parent personality is linked to juvenile mortality and stress behavior in the arctic fox (Vulpes lagopus). Behav. Ecol. Sociobiol. 73, 162 (2019).Article 

    Google Scholar 
    Kappeler, P. M. & van Schaik, C. P. Evolution of primate social systems. Int. J. Primatol. 23, 707–740 (2002).Article 

    Google Scholar 
    Tardif, S. D. et al. Reproduction in captive common marmosets (Callithrix jacchus). Comp. Med. 53, 364–368 (2003).CAS 
    PubMed 

    Google Scholar 
    Marini, R., Wachtman, L., Tardif, S., Mansfield, K. & Fox, J. The Common Marmoset in Captivity and Biomedical Research (Academic Press, 2019). https://doi.org/10.1016/C2016-0-00861-6.Book 

    Google Scholar 
    Arruda, M. D. F., Yamamoto, M. E., Pessoa, D. M. A. & Araujo, A. Taxonomy and Natural History. In The Common Marmoset in Captivity and Biomedical Research (eds Marini, R. et al.) 3–15 (Academic Press, 2019). https://doi.org/10.1016/B978-0-12-811829-0.00001-7.Chapter 

    Google Scholar 
    Buchanan-Smith, H. M. Marmosets and tamarins. In The UFAW Handbook on the Care and Management of Laboratory and Other Research Animals (eds Hubrecht, R. & Kirkwood, J.) (Wiley-Blackwell, 2010). https://doi.org/10.1002/9781444318777.ch36.Chapter 

    Google Scholar 
    Smucny, D. A. et al. Reproductive output, maternal age, and survivorship in captive common marmoset females (Callithrix jacchus). Am. J. Primatol. 64, 107–121 (2004).PubMed 
    Article 

    Google Scholar 
    Ash, H. & Buchanan-Smith, H. M. Long-term data on reproductive output and longevity in captive female common marmosets (Callithrix jacchus). Am. J. Primatol. 76, 1062–1073 (2014).PubMed 
    Article 

    Google Scholar 
    Frye, B. M. et al. After short interbirth intervals, captive callitrichine monkeys have higher infant mortality. iScience 25, 103724 (2022).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McCoy, D. E. et al. A comparative study of litter size and sex composition in a large dataset of callitrichine monkeys. Am. J. Primatol. 81, e23038 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jaquish, C. E., Tardif, S. D. & Cheverud, J. M. Interactions between infant growth and survival: Evidence for selection on age-specific body weight in captive common marmosets (Callithrix jacchus). Am. J. Primatol. 42, 269–280 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tardif, S. D. & Jaquish, C. E. Number of ovulations in the marmoset monkey (Callithrix jacchus): Relation to body weight, age and repeatability. Am. J. Primatol. 42, 323–329 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Poole, T. B. & Evans, R. G. Reproduction, infant survival and productivity of a colony of common marmosets (Callithrix jacchus jacchus). Lab. Anim. 16, 88–97 (1982).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tardif, S. D., Richter, C. B. & Carson, R. L. Effects of sibling-rearing experience on future reproductive success in two species of callitrichidae. Am. J. Primatol. 6, 377–380 (1984).PubMed 
    Article 

    Google Scholar 
    Rothe, H., Koenig, A. & Darms, K. Infant survival and number of helpers in captive groups of common marmosets (Callithrix jacchus). Am. J. Primatol. 30, 131–137 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    Koski, S. E., Buchanan-Smith, H. M., Burkart, J. M., Bugnyar, T. & Weiss, A. Common marmoset (Callithrix jacchus) personality. J. Comp. Psychol. 131, 326–336 (2017).PubMed 
    Article 

    Google Scholar 
    Šlipogor, V., Burkart, J. M., Martin, J. S., Bugnyar, T. & Koski, S. E. Personality method validation in common marmosets (Callithrix jacchus): Getting the best of both worlds. J. Comp. Psychol. 134, 52–70 (2020).PubMed 
    Article 

    Google Scholar 
    Weiss, A., Yokoyama, C., Hayashi, T. & Inoue-Murayama, M. Personality, subjective well-being, and the serotonin 1a receptor gene in common marmosets (Callithrix jacchus). PLoS ONE 16, e0238663 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Freeman, H., Gosling, S. D. & Schapiro, S. J. Comparison of methods for assessing personality in nonhuman primates. In Personality and Temperament in Nonhuman Primates (eds Weiss, A. et al.) 17–40 (Springer, 2011).Chapter 

    Google Scholar 
    Finkenwirth, C. & Burkart, J. M. Why help? Relationship quality, not strategic grooming predicts infant-care in group-living marmosets. Physiol. Behav. 193, 108–116 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haines, J. A. et al. Sex- and context-specific associations between personality and a measure of fitness but no link with life history traits. Anim. Behav. 167, 23–39 (2020).Article 

    Google Scholar 
    Carlstead, K., Mellen, J. & Kleiman, D. G. Black rhinoceros (Diceros bicornis) in US zoos: I. Individual behavior profiles and their relationship to breeding success. Zoo Biol. 18, 17–34 (1999).Article 

    Google Scholar 
    Berg, V., Lummaa, V., Lahdenperä, M., Rotkirch, A. & Jokela, M. Personality and long-term reproductive success measured by the number of grandchildren. Evol. Hum. Behav. 35, 533–539 (2014).Article 

    Google Scholar 
    Silva, H. P. A. & Sousa, M. B. C. The pair-bond formation and its role in the stimulation of reproductive function in female common marmosets (Callithrix jacchus). Int. J. Primatol. 18, 387–400 (1997).Article 

    Google Scholar 
    Cavanaugh, J., Mustoe, A. C., Taylor, J. H. & French, J. A. Oxytocin facilitates fidelity in well-established marmoset pairs by reducing sociosexual behavior toward opposite-sex strangers. Psychoneuroendocrinology 49, 1–10 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andersen, I. L., Nævdal, E. & Bøe, K. E. Maternal investment, sibling competition, and offspring survival with increasing litter size and parity in pigs (Sus scrofa). Behav. Ecol. Sociobiol. 65, 1159–1167 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johnstone-Yellin, T. L., Shipley, L. A., Myers, W. L. & Robinson, H. S. To twin or not to twin? Trade-offs in litter size and fawn survival in mule deer. J. Mammal. 90, 453–460 (2009).Article 

    Google Scholar 
    Ariyomo, T. O. & Watt, P. J. The effect of variation in boldness and aggressiveness on the reproductive success of zebrafish. Anim. Behav. 83, 41–46 (2012).Article 

    Google Scholar 
    Patterson, L. D. & Schulte-Hostedde, A. I. Behavioural correlates of parasitism and reproductive success in male eastern chipmunks, Tamias striatus. Anim. Behav. 81, 1129–1137 (2011).Article 

    Google Scholar 
    Mutzel, A., Dingemanse, N. J., Araya-Ajoy, Y. G. & Kempenaers, B. Parental provisioning behaviour plays a key role in linking personality with reproductive success. Proc. R. Soc. B 280, 20131019 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Costa, T. S. O. et al. Individual behavioral differences and health of golden-headed lion tamarins (Leontopithecus chrysomelas). Am. J. Primatol. 82, e23118 (2020).PubMed 
    Article 

    Google Scholar 
    Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

    Google Scholar 
    Tardif, S. D., Power, M., Oftedal, O. T., Power, R. A. & Layne, D. G. Lactation, maternal behavior and infant growth in common marmoset monkeys (Callithrix jacchus): Effects of maternal size and litter size. Behav. Ecol. Sociobiol. 51, 17–25 (2001).Article 

    Google Scholar 
    Mills, D. A., Windle, C. P., Baker, H. F. & Ridley, R. M. Analysis of infant carrying in large, well-established family groups of captive marmosets (Callithrix jacchus). Primates 45, 259–265 (2004).PubMed 
    Article 

    Google Scholar 
    Leutenegger, W. Maternal-fetal weight relationships in primates. Folia Primatol. 20, 280–293 (1973).CAS 
    Article 

    Google Scholar 
    Schultz-Darken, N., Ace, L. & Ash, H. Behavior and behavioral management. In The Common Marmoset in Captivity and Biomedical Research (eds Marini, R. et al.) 109–117 (Academic Press, 2019). https://doi.org/10.1016/b978-0-12-811829-0.00007-8.Chapter 

    Google Scholar 
    Bardi, M. & Petto, A. J. Parental failure in captive common marmosets (Callithrix jacchus): A comparison with tamarins. Folia Primatol. 73, 46–48 (2002).Article 

    Google Scholar 
    Barbosa, M. N. & da Silva Mota, M. T. Alloparental responsiveness to newborns by nonreproductive, adult male, common marmosets (Callithrix jacchus). Am. J. Primatol. 75, 145–152 (2013).PubMed 
    Article 

    Google Scholar 
    Rutherford, J. N. et al. Womb to womb: Maternal litter size and birth weight but not adult characteristics predict early neonatal death of offspring in the common marmoset monkey. PLoS ONE 16, e0252093 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Finkenwirth, C., Martins, E., Deschner, T. & Burkart, J. M. Oxytocin is associated with infant-care behavior and motivation in cooperatively breeding marmoset monkeys. Horm. Behav. 80, 10–18 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edwards, H. A., Dugdale, H. L., Richardson, D. S., Komdeur, J. & Burke, T. Extra-pair parentage and personality in a cooperatively breeding bird. Behav. Ecol. Sociobiol. 72, 37 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schürch, R. & Heg, D. Variation in helper type affects group stability and reproductive decisions in a cooperative breeder. Ethology 116, 257–269 (2010).Article 

    Google Scholar 
    Class, B. & Dingemanse, N. J. A variance partitioning perspective of assortative mating: Proximate mechanisms and evolutionary implications. J. Evol. Biol. 35, 483–490 (2022).PubMed 
    Article 

    Google Scholar 
    Scherer, U., Godin, J. G. J. & Schuett, W. Do female rainbow kribs choose males on the basis of their apparent aggression and boldness? A non-correlational mate choice study. Behav. Ecol. Sociobiol. 74, 34 (2020).Article 

    Google Scholar 
    Schuett, W., Godin, J.-G.J. & Dall, S. R. X. Do female zebra finches, Taeniopygia guttata, choose their mates based on their ‘personality’?. Ethology 117, 908–917 (2011).Article 

    Google Scholar 
    Ophir, A. G., Crino, O. L., Wilkerson, Q. C., Wolff, J. O. & Phelps, S. M. Female-directed aggression predicts paternal behavior, but female prairie voles prefer affiliative males to paternal males. Brain. Behav. Evol. 71, 32–40 (2008).PubMed 
    Article 

    Google Scholar 
    Lazaro-Perea, C. Intergroup interactions in wild common marmosets, Callithrix jacchus: Territorial defence and assessment of neighbours. Anim. Behav. 62, 11–21 (2001).Article 

    Google Scholar 
    Koski, S. E. & Burkart, J. M. Common marmosets show social plasticity and group-level similarity in personality. Sci. Rep. 5, 8878 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Norman, M., Rowden, L. J. & Cowlishaw, G. Potential applications of personality assessments to the management of non-human primates: A review of 10 years of study. PeerJ 9, e12044 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gorsuch, R. L. Factor Analysis 2nd edn. (Lawrence Erlbaum Associates, 1983).MATH 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2020).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 
    CAS 

    Google Scholar 
    Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
    MATH 

    Google Scholar 
    Christensen, R. H. B. Ordinal—Regression Models for Ordinal Data. R package version 2019.4-25. (2019).Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer-Verlag, 2002). https://doi.org/10.1007/b97636.Book 
    MATH 

    Google Scholar 
    Bartoń, K. Mu-MIn: Multi-model inference. R package version 2019 1.43.6. (2019).Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Richards, S. A. Dealing with overdispersed count data in applied ecology. J. Appl. Ecol. 45, 218–227 (2008).Article 

    Google Scholar 
    Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.2.7 (2020).Lüdecke, D. sjPlot: Data Visualization for Statistics in Social Science. R package version 2.8.2 (2020)du Sert, N. P. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020).Article 
    CAS 

    Google Scholar  More

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    The role of gene expression and symbiosis in reef-building coral acquired heat tolerance

    Larvae display conserved gene expression response to heat stressLarval gene expression (GE) was quantified to assess if plastic responses in gene expression to heat stress varied depending on site of origin or parental identity. Larval survival under heat stress varied between crosses, with larvae produced from dams sourced from far Northern GBR sites exhibiting higher thermal tolerance (Fig. 1b). The cross with the lowest thermal tolerance (LSxSB) did not have any larvae survive the heat treatment (Fig. 1b, Supplementary Fig. 2). GE was examined in aposymbiotic larvae experiencing ambient conditions prior to the heat treatment (“pre”), larvae after exposure to simulated heat stress (35.5 °C for 56 hours, “post heat”), and a simultaneous control temperature of 27 °C (“post ambient”). Therefore, the “pre” larval treatment provided transcriptomic baselines of GE between genetic crosses while “post heat” and “post ambient” comparisons show a baseline for cross-specific heat responses without the confounding effect of symbiosis found in the post-metamorphic phase. Using a principal coordinates analysis (PCoA), we find that GE patterns in larvae were driven by treatment (“pre”, “post ambient”, “post heat”), explaining 29.2% of the variation in survival (padonis  More