in

Effects of climate change and land cover on the distributions of a critical tree family in the Philippines

  • 1.

    Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 1, 1–7. https://doi.org/10.1126/science.1196624 (2010).

    CAS  Article  Google Scholar 

  • 2.

    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 3.

    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 4.

    Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).

    CAS  Article  Google Scholar 

  • 5.

    Sodhi, N. S. & Brook, B. W. Southeast Asian Biodiversity in Crisis (Cambridge University Press, Cambridge, 2006).

    Google Scholar 

  • 6.

    Fischer, J. & Lindenmayer, D. B. Landscape modification and habitat fragmentation: A synthesis. Glob. Ecol. Biogeogr. 16, 265–280 (2007).

    Article  Google Scholar 

  • 7.

    Murcia, C. Forest fragmentation and the pollination of neotropical plants. For. Patches Trop. Landsc. 1, 19–36 (1996).

    Google Scholar 

  • 8.

    Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  • 9.

    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, 1024–1026 (2011).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 10.

    Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: Lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).

    ADS  Article  Google Scholar 

  • 11.

    Deb, J. C., Phinn, S., Butt, N. & McAlpine, C. A. The impact of climate change on the distribution of two threatened Dipterocarp trees. Ecol. Evol. 7, 2238–2248 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  • 12.

    Garcia, K., Lasco, R., Ines, A., Lyon, B. & Pulhin, F. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Appl. Geogr. 44, 12–22 (2013).

    Article  Google Scholar 

  • 13.

    Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 14.

    McShea, W. J. What are the roles of species distribution models in conservation planning?. Environ. Conserv. 41, 93–96 (2014).

    Article  Google Scholar 

  • 15.

    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 

  • 16.

    Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010: Deforestation in insular Southeast Asia 2000–2010. Glob. Change Biol. 17, 2261–2270 (2011).

    ADS  Article  Google Scholar 

  • 17.

    Sodhi, N. S. et al. The state and conservation of Southeast Asian biodiversity. Biodivers. Conserv. 19, 317–328 (2010).

    Article  Google Scholar 

  • 18.

    Yusuf, A. A. & Francisco, H. Climate change vulnerability mapping for Southeast Asia. (2009).

  • 19.

    Ambal, R. G. R. et al. Key biodiversity areas in the Philippines: priorities for conservation. J. Threat. Taxa 4, 2788–2796 (2012).

    Article  Google Scholar 

  • 20.

    Feeley, K. J. & Silman, M. R. The data void in modeling current and future distributions of tropical species. Glob. Change Biol. 17, 626–630 (2011).

    ADS  Article  Google Scholar 

  • 21.

    Ramos, L. T., Torres, A. M., Pulhin, F. B. & Lasco, R. D. Developing a georeferenced database of selected threatened forest tree species in the Philippines. Philipp. J. Sci. 141, 165–177 (2012).

    Google Scholar 

  • 22.

    Liu, D. S., Iverson, L. R. & Brown, S. Rates and patterns of deforestation in the Philippines: application of geographic information system analysis. For. Ecol. Manag. 57, 1–16 (1993).

    Article  Google Scholar 

  • 23.

    Shively, G. & Pagiola, S. Agricultural intensification, local labor markets, and deforestation in the Philippines. Environ. Dev. Econ. 9, 241–266 (2004).

    Article  Google Scholar 

  • 24.

    Ashton, P. S. Dipterocarpaceae. Dipterocarpaceae. 9, 237–552 (1982).

    Google Scholar 

  • 25.

    De Guzman, E. D., Umali, R. M. & Sotalbo, E. D. Guide to Philippine Flora and Fauna, Vol. 3: Dipterocarps, Non-Dipterocarps. Nat. Resour. Manag. Cent. Minist. Nat. Resour. Univ. Philipp. (1986).

  • 26.

    Fernando, E. S., Suh, M. H., Lee, J. & Lee, D. K. Forest formations of the Philippines. (ASEAN-Korea Environmental Cooperation Unit, 2008).

  • 27.

    Tuck, S. L. et al. The value of biodiversity for the functioning of tropical forests: insurance effects during the first decade of the Sabah biodiversity experiment. Proc. R. Soc. B Biol. Sci. 283, 20161451 (2016).

    Article  Google Scholar 

  • 28.

    Brearley, F. Q., Banin, L. F. & Saner, P. The ecology of the Asian dipterocarps. Plant Ecol. Divers. 9, 429–436 (2016).

    Article  Google Scholar 

  • 29.

    Schulte, A. Dipterocarp forest ecosystem theory based on matter balance and biodiversity. in Dipterocarp Forest Ecosystems: Towards Sustainable Management 3–28 (1996).

  • 30.

    Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30 (2013).

    ADS  Article  Google Scholar 

  • 31.

    Granados, A. Ecological Effects of Disrupting Plant-Animal Interactions (University of British Columbia, Vancouver, 2017).

    Google Scholar 

  • 32.

    Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 33.

    Albrecht, J. et al. Correlated loss of ecosystem services in coupled mutualistic networks. Nat. Commun. 5, 3810 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 34.

    Keppel, G. et al. The capacity of refugia for conservation planning under climate change. Front. Ecol. Environ. 13, 106–112 (2015).

    Article  Google Scholar 

  • 35.

    Kettle, C. J. Ecological considerations for using dipterocarps for restoration of lowland rainforest in Southeast Asia. Biodivers. Conserv. 19, 1137–1151 (2010).

    Article  Google Scholar 

  • 36.

    Titeux, N. et al. Biodiversity scenarios neglect future land-use changes. Glob. Change Biol. 22, 2505–2515 (2016).

    ADS  Article  Google Scholar 

  • 37.

    Pimm, S. L., Jenkins, C. N. & Li, B. V. How to protect half of Earth to ensure it protects sufficient biodiversity. Sci. Adv. 4, 2616 (2018).

    ADS  Article  Google Scholar 

  • 38.

    Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 39.

    Ashcroft, M. B. Identifying refugia from climate change. J. Biogeogr. 1, 1407–1413. https://doi.org/10.1111/j.1365-2699.2010.02300.x (2010).

    Article  Google Scholar 

  • 40.

    Graham, V., Baumgartner, J. B., Beaumont, L. J., Esperón-Rodríguez, M. & Grech, A. Prioritizing the protection of climate refugia: designing a climate-ready protected area network. J. Environ. Plan. Manag. 1, 1–19. https://doi.org/10.1080/09640568.2019.1573722 (2019).

    Article  Google Scholar 

  • 41.

    Mair, L. et al. Land use changes could modify future negative effects of climate change on old-growth forest indicator species. Divers. Distrib. 24, 1416–1425 (2018).

    Article  Google Scholar 

  • 42.

    Methorst, J., Böhning-Gaese, K., Khaliq, I. & Hof, C. A framework integrating physiology, dispersal and land-use to project species ranges under climate change. J. Avian Biol. 48, 1532–1548 (2017).

    Article  Google Scholar 

  • 43.

    Segan, D. B., Murray, K. A. & Watson, J. E. M. A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Glob. Ecol. Conserv. 5, 12–21 (2016).

    Article  Google Scholar 

  • 44.

    Milanesi, P., Della Rocca, F. & Robinson, R. A. Integrating dynamic environmental predictors and species occurrences: Toward true dynamic species distribution models. Ecol. Evol. 10, 1087–1092 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  • 45.

    Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).

    ADS  Article  Google Scholar 

  • 46.

    Peterson, A. T., Cobos, M. E. & Jiménez-García, D. Major challenges for correlational ecological niche model projections to future climate conditions: Climate change, ecological niche models, and uncertainty. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).

    ADS  PubMed  Article  PubMed Central  Google Scholar 

  • 47.

    Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche Contractions in declining species: Mechanisms and consequences. Trends Ecol. Evol. 32, 346–355 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  • 48.

    PAGASA. Daily Rainfall and Temperature. http://bagong.pagasa.dost.gov.ph/climate/climate-monitoring#daily-rainfall-and-temperature (2019).

  • 49.

    GBIF. GBIF Occurrence Download. https://doi.org/10.15468/dl.cetigh (2020).

  • 50.

    Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).

    Article  Google Scholar 

  • 51.

    DAO. Updated national list of threatened Philippine plants and their categories. Dep. Environ. Nat. Resour. Repub. Philipp. Quezon City Manila (2017).

  • 52.

    IUCN. IUCN Red List of Threatened Species. (IUCN, Geneva, 2019).

  • 53.

    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).

    Article  Google Scholar 

  • 54.

    Newbold, T. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Prog. Phys. Geogr. 34, 3–22 (2010).

    Article  Google Scholar 

  • 55.

    Yackulic, C. B. et al. Presence-only modelling using MAXENT: when can we trust the inferences?. Methods Ecol. Evol. 4, 236–243 (2013).

    Article  Google Scholar 

  • 56.

    Pelser, P. B., Barcelona, J. F. & Nickrent, D. L. Co’s Digital Flora of the Philippines. (2011).

  • 57.

    IPNI. The International Plant Names Index. http://www.ipni.org (2020).

  • 58.

    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 

  • 59.

    Dormann, C. F. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob. Ecol. Biogeogr. 16, 129–138 (2007).

    Article  Google Scholar 

  • 60.

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

    Article  Google Scholar 

  • 61.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    ADS  Article  Google Scholar 

  • 62.

    Kamworapan, S. & Surussavadee, C. Evaluation of CMIP5 global climate models for simulating climatological temperature and precipitation for Southeast Asia. Adv. Meteorol. 2019, 1–18 (2019).

    Article  Google Scholar 

  • 63.

    Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213 (2011).

    ADS  CAS  Article  Google Scholar 

  • 64.

    PAGASA. Observed and Projected Climate Change in the Philippines. (2018).

  • 65.

    Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 66.

    R Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna Austria 55, 275–286 (2013).

  • 67.

    Hijmans, R. J. & Etten, J. V. Geographic analysis and modeling with raster data. R Package Version 2, 1–25 (2012).

    Google Scholar 

  • 68.

    Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).

    Article  Google Scholar 

  • 69.

    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).

    Article  Google Scholar 

  • 70.

    Townsend Peterson, A., Papeş, M. & Eaton, M. Transferability and model evaluation in ecological niche modeling: A comparison of GARP and Maxent. Ecography 30, 550–560 (2007).

    Article  Google Scholar 

  • 71.

    Breiner, F. T., Nobis, M. P., Bergamini, A. & Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 9, 802–808 (2018).

    Article  Google Scholar 

  • 72.

    Zhu, G. P. & Peterson, A. T. Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth. Biol. Invasions 19, 2519–2532 (2017).

    Article  Google Scholar 

  • 73.

    Araujo, M. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).

    PubMed  Article  PubMed Central  Google Scholar 

  • 74.

    Hannemann, H., Willis, K. J. & Macias-Fauria, M. The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling. Glob. Ecol. Biogeogr. 25, 26–35 (2016).

    Article  Google Scholar 

  • 75.

    Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography https://doi.org/10.1111/ecog.04890 (2020).

    Article  Google Scholar 

  • 76.

    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).

    Article  Google Scholar 

  • 77.

    Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).

    Article  Google Scholar 

  • 78.

    Fourcade, Y., Besnard, A. G. & Secondi, J. Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics. Glob. Ecol. Biogeogr. 27, 245–256 (2018).

    Article  Google Scholar 

  • 79.

    Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. A single-algorithm ensemble approach to estimating suitability and uncertainty: Cross-time projections for four Malagasy tenrecs. Divers. Distrib. 23, 196–208 (2017).

    Article  Google Scholar 

  • 80.

    Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Model. 269, 9–17 (2013).

    Article  Google Scholar 

  • 81.

    Iturbide, M. et al. A framework for species distribution modelling with improved pseudo-absence generation. Ecol. Model. 312, 166–174 (2015).

    Article  Google Scholar 

  • 82.

    VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?. Ecol. Model. 220, 589–594 (2009).

    Article  Google Scholar 

  • 83.

    Chefaoui, R. M. & Lobo, J. M. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Model. 210, 478–486 (2008).

    Article  Google Scholar 

  • 84.

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

    Article  Google Scholar 

  • 85.

    Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ 5, e3093 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  • 86.

    Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).

    Article  Google Scholar 

  • 87.

    Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).

    Article  Google Scholar 

  • 88.

    Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  • 89.

    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?: How to use pseudo-absences in niche modelling?. Methods Ecol. Evol. 3, 327–338 (2012).

    Article  Google Scholar 

  • 90.

    Anderson, R. P. & Gonzalez, I. Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent. Ecol. Model. 222, 2796–2811 (2011).

    Article  Google Scholar 

  • 91.

    Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).

    Article  Google Scholar 

  • 92.

    Velasco, J. A. & González-Salazar, C. Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling. Ecol. Inform. 51, 25–32 (2019).

    Article  Google Scholar 

  • 93.

    Vignali, S., Barras, A. & Braunisch, V. SDMtune: Species distribution model selection. R Package Version 101 (2019) https://github.com/ConsBiol-unibern/SDMtune.

  • 94.

    Liu, C., Newell, G. & White, M. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  • 95.

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

    Article  Google Scholar 

  • 96.

    Somodi, I., Lepesi, N. & Botta-Dukát, Z. Prevalence dependence in model goodness measures with special emphasis on true skill statistics. Ecol. Evol. 7, 863–872 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  • 97.

    Warren, D. L., Matzke, N. J. & Iglesias, T. L. Evaluating species distribution models with discrimination accuracy is uninformative for many applications. https://doi.org/10.1101/684399 (2019)

  • 98.

    Angelstam, P. Conservation of communities—the importance of edges, surroundings and landscape mosaic structure. in Ecological principles of nature conservation 9–70 (Springer, 1992).

  • 99.

    Waldhardt, R., Simmering, D. & Otte, A. Estimation and prediction of plant species richness in a mosaic landscape. Landsc. Ecol. 19, 211–226 (2004).

    Article  Google Scholar 

  • 100.

    Fischer, J. & Lindenmayer, D. B. Small patches can be valuable for biodiversity conservation: two case studies on birds in southeastern Australia. Biol. Conserv. 106, 129–136 (2002).

    Article  Google Scholar 

  • 101.

    Struebig, M. J. et al. Targeted conservation to safeguard a biodiversity hotspot from climate and land-cover change. Curr. Biol. 25, 372–378 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 102.

    UNEP-WCMC. World database on protected areas. UNEP WCMC Camb. UK (2018).

  • 103.

    LP DAAC. Global 30 arc-second elevation data set GTOPO30. Land Process Distrib. Act. Arch. Cent. (2004) http://edcdaac.usgs.gov/gtopo30/gtopo30.asp.

  • 104.

    Amaral, A. G., Munhoz, C. B. R., Walter, B. M. T., Aguirre-Gutiérrez, J. & Raes, N. Richness pattern and phytogeography of the Cerrado herb-shrub flora and implications for conservation. J. Veg. Sci. 28, 848–858 (2017).

    Article  Google Scholar 

  • 105.

    Kanagaraj, R. et al. Predicting range shifts of Asian elephants under global change. Divers. Distrib. https://doi.org/10.1111/ddi.12898 (2019).

    Article  Google Scholar 

  • 106.

    De Alban, J. D. et al. High Conservation Value Areas as a strategic approach for protected area management in the Philippines. in 1–10 (Asian Association on Remote Sensing, 2015).

  • 107.

    IUCN. IUCN Red List Categories and Criteria: Version 3.1. (IUCN, Gland, 2012).

  • 108.

    Fuller, R. A. et al. Replacing underperforming protected areas achieves better conservation outcomes. Nature 466, 365 (2010).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 109.

    Davis, K. F., Yu, K., Rulli, M. C., Pichdara, L. & D’Odorico, P. Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci. 8, 772–775 (2015).

    ADS  CAS  Article  Google Scholar 

  • 110.

    Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 111.

    Walck, J. L., Hidayati, S. N., Dixon, K. W., Thompson, K. E. N. & Poschlod, P. Climate change and plant regeneration from seed. Glob. Change Biol. 17, 2145–2161 (2011).

    ADS  Article  Google Scholar 

  • 112.

    Corlett, R. T. Seed dispersal distances and plant migration potential in tropical East Asia. Biotropica 41, 592–598 (2009).

    Article  Google Scholar 

  • 113.

    Smith, J. R. et al. Predicting dispersal of auto-gyrating fruit in tropical trees: a case study from the Dipterocarpaceae. Ecol. Evol. 5, 1794–1801 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 114.

    Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 115.

    Bonn, A. & Gaston, K. J. Capturing biodiversity: Selecting priority areas for conservation using different criteria. Biodivers. Conserv. 14, 1083–1100 (2005).

    Article  Google Scholar 

  • 116.

    Hannah, L. et al. Protected area needs in a changing climate. Front. Ecol. Environ. 5, 131–138 (2007).

    Article  Google Scholar 

  • 117.

    Carvalho, S. B., Brito, J. C., Crespo, E. G., Watts, M. E. & Possingham, H. P. Conservation planning under climate change: Toward accounting for uncertainty in predicted species distributions to increase confidence in conservation investments in space and time. Biol. Conserv. 144, 2020–2030 (2011).

    Article  Google Scholar 

  • 118.

    Lemes, P. & Loyola, R. D. Accommodating species climate-forced dispersal and uncertainties in spatial conservation planning. PLoS ONE 8, e54323 (2013).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 119.

    Suzuki, E. & Ashton, P. S. Sepal and nut size ratio of fruits of Asian Dipterocarpaceae and its implications for dispersal. J. Trop. Ecol. 12, 853–870 (1996).

    Article  Google Scholar 

  • 120.

    Ball, I. R., Possingham, H. P. & Watts, M. Marxan and relatives: software for spatial conservation prioritisation. Spat. Conserv. Prioritisation Quant. Methods Comput. Tools 1, 185–195 (2009).

    Google Scholar 

  • 121.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).

    Google Scholar 


  • Source: Ecology - nature.com

    Scientists discover slimy microbes that may help keep coral reefs healthy

    Multiple life-stage inbreeding depression impacts demography and extinction risk in an extinct-in-the-wild species