Mitigating the impacts of global anthropogenic change on species is conservation’s greatest challenge. Forecasting the effects of actions to mitigate threats is hampered by incomplete information on species’ responses. We develop an approach to predict community restructuring under threat management, which combines models of responses to threats with network analyses of species co-occurrence. We discover that contributions by species to network co-occurrence predict their recovery under reduction of multiple threats. Highly connected species are likely to benefit more from threat management than poorly connected species. Importantly, we show that information from a few species on co-occurrence and expected responses to alternative threat management actions can be used to train a response model for an entire community. We use a unique management dataset for a threatened bird community to validate our predictions and, in doing so, demonstrate positive feedbacks in occurrence and co-occurrence resulting from shared threat management responses during ecosystem recovery.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Diamond, J. M. in Ecology and Evolution of Communities (eds Cody, M. L. & Diamond, J. M.) 342–444 (Harvard Univ. Press, Cambridge, 1975).
Gotelli, N. J. & McCabe, D. J. Species co-occurrence: a meta-analysis of JM Diamond’s assembly rules model. Ecology 83, 2091–2096 (2006).
Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611–1615 (2013).
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interaction networks vary through space and time. Oikos 124, 243–251 (2015).
Naeem, S., Thompson, L. J., Lawler, S. P., Lawton, J. H. & Woodfin, R. M. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994).
Tulloch, A. I. T. et al. Dynamic species co-occurrence networks require dynamic biodiversity surrogates. Ecography 39, 1185–1196 (2016).
Chadès, I., Curtis, J. M. R. & Martin, T. G. Setting realistic recovery targets for two interacting endangered species, sea otter and northern abalone. Conserv Biol. 26, 1016–1025 (2012).
Blanchard, J. L. et al. Evaluating targets and trade-offs among fisheries and conservation objectives using a multispecies size spectrum model. J. Appl. Ecol. 51, 612–622 (2014).
Jacobsen, N. S., Gislason, H. & Andersen, K. H. The consequences of balanced harvesting of fish communities. Proc. R. Soc. B 281, 20132701 (2014).
Lai, H. R., Mayfield, M. M., Gay-des-combes, J. M., Spiegelberger, T. & Dwyer, J. M. Distinct invasion strategies operating within a natural annual plant system. Ecol. Lett. 18, 336–346 (2015).
Kathleen Lyons, S. et al. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529, 80–83 (2016).
Firn, J. et al. Priority threat management of invasive animals to protect biodiversity under climate change. Glob. Change Biol. 21, 3917–3930 (2015).
Auerbach, N. A. et al. Effects of threat management interactions on conservation priorities. Conserv. Biol. 29, 1626–1635 (2015).
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).
Sheldon, K. S., Yang, S. & Tewksbury, J. J. Climate change and community disassembly: impacts of warming on tropical and temperate montane community structure. Ecol. Lett. 14, 1191–1200 (2011).
Hille Ris Lambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).
Tilman, D. Ecological competition between algae: experimental confirmation of resource-based competition theory. Science 192, 463–465 (1976).
Adler, P. B., Ellner, S. P. & Levine, J. M. Coexistence of perennial plants: an embarrassment of niches. Ecol. Lett. 13, 1019–1029 (2010).
Araújo, M. B., Rozenfeld, A., Rahbek, C. & Marquet, P. A. Using species co-occurrence networks to assess the impacts of climate change. Ecography 34, 897–908 (2011).
Saavedra, S., Stouffer, D. B., Uzzi, B. & Bascompte, J. Strong contributors to network persistence are the most vulnerable to extinction. Nature 478, 233–235 (2011).
Borthagaray, A. I., Arim, M. & Marquet, P. A. Inferring species roles in metacommunity structure from species co-occurrence networks. Proc. R. Soc. B 281, 20141425 (2014).
Tulloch, A. I. T., Mortelliti, A., Kay, G., Florance, D. & Lindenmayer, D. Using empirical models of species colonization under multiple threatening processes to identify complementary threat mitigation strategies. Conserv. Biol. 30, 867–882 (2016).
Valiente-Banuet, A. & Verdú, M. Human impacts on multiple ecological networks act synergistically to drive ecosystem collapse. Front. Ecol. Environ. 11, 408–413 (2013).
Didham, R. K., Tylianakis, J. M., Gemmell, N. J., Rand, T. A. & Ewers, R. M. Interactive effects of habitat modification and species invasion on native species decline. Trends Ecol. Evol. 22, 489–496 (2007).
Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).
Araujo, M. B., Rozenfeld, A., Rahbek, C. & Marquet, P. A. Using species co-occurrence networks to assess the impacts of climate change. Ecography 34, 897–908 (2011).
Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).
Maron, M. et al. Avifaunal disarray due to a single despotic species. Divers. Distrib. 19, 1468–1479 (2013).
Lindenmayer, D. B. et al. What makes an effective restoration planting for woodland birds? Biol. Conserv. 143, 289–301 (2010).
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).
Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Func. Ecol. 29, 299–307 (2015).
Barrat, A., Barthélemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl Acad. Sci. USA 101, 3747–3752 (2004).
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).
Kuiper, J. J. et al. Food-web stability signals critical transitions in temperate shallow lakes. Nat. Commun. 6, 7727 (2015).
Fortuna, M. A., Gómez-Rodríguez, C. & Bascompte, J. Spatial network structure and amphibian persistence in stochastic environments. Proc. R. Soc. B 273, 1429–1434 (2006).
Tylianakis, J. M., Laliberte, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).
Milazzo, M., Mirto, S., Domenici, P. & Gristina, M. Climate change exacerbates interspecific interactions in sympatric coastal fishes. J. Anim. Ecol. 82, 468–477 (2013).
Bell, J. R., Andrew King, R., Bohan, D. A. & Symondson, W. O. C. Spatial co-occurrence networks predict the feeding histories of polyphagous arthropod predators at field scales. Ecography 33, 64–72 (2010).
Fitzgibbon, C. D. Mixed-species grouping in Thomson’s and Grant’s gazelles: the antipredator benefits. Anim. Behav. 39, 1116–1126 (1990).
Vanderduys, E. P., Kutt, A. S., Perry, J. J. & Perkins, G. C. The composition of mixed-species bird flocks in northern Australian savannas. Emu 112, 218–226 (2012).
Mac Nally, R. & Timewell, C. A. R. Resource availability controls bird-assemblage composition through interspecific aggression. Auk 122, 1097–1111 (2005).
Griffith, D. M., Veech, J. A. & Marsh, C. J. cooccur: probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).
Martin, T. G. & McIntye, S. Impacts of livestock grazing and tree clearing on birds of woodland and riparian habitats. Conserv. Biol. 21, 504–514 (2007).
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).
Fowler, M. S. Increasing community size and connectance can increase stability in competitive communities. J. Theor. Biol. 258, 179–188 (2009).
Vesk, P. A., Nolan, R., Thomson, J. R., Dorrough, J. W. & Nally, R. M. Time lags in provision of habitat resources through revegetation. Biol. Conserv. 141, 174–186 (2008).
Steen, D. A. et al. Bird assemblage response to restoration of fire-suppressed longleaf pine sandhills. Ecol. Appl. 23, 134–147 (2013).
Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).
Fiske, I. & Chandler, R. unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23 (2011).
D.B.L. is supported by an ARC Laureate Fellowship. A.I.T.T. is funded by the Australian Research Council Centre of Excellence for Environmental Decisions (CEED). The monitoring program was coordinated by D. Florance from The Australian National University (ANU) and approved by the Australian National University Animal Ethics Committee, and field staff from ANU and volunteers from the Canberra Ornithologists Group assisted with surveys. We thank M. Westgate and P. Lane for valuable discussions on methodology.
The authors declare no competing financial interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–3, Supplementary Tables 1–7, Supplementary References
Step 1 input ‘threats present’ species by site data for analysis of community of 88 bird species in Grassy Box Woodlands from 2011–2013
Step 1 output ‘threats present’ co-occurrence results: significant species pairs for 88 spp
Step 1 output ‘threats present’ co-occurrence results: pairwise effect size matrix for 88 spp
Step 2 output ‘threats present’ expected threat management responses: predicted changes in colonization rates for 37 species under 7 management strategies
Step 3 input ‘threats present’ pairwise data on co-occurrence (results from step 1) and species’ expected responses to threat reduction (results from step 2) for models predicting expected outcomes of 7 management strategies
Step 3 output ‘threats present’ pairwise data from model predicting expected outcomes of managing 3 threats (input for step 4)
Step 4 output ‘threats present’ species-level data on predicted change in colonization when managing 3 threats to all 88 species
Step 5/6 input ‘threats managed’ site by species data to calculate change in site occupancy, with sites categorized as 0, 1, 2 or 3 threats remaining
Step 5/6 input ‘threats managed’ species-level data for validation of change in site occupancy after management
Step 5/6 input ‘threats managed’ pairwise-level data for validation of predicted ‘increasers’ and ‘decreasers’
Input for Matlab matrix manipulation: an excel version of step 1 output pairwise effect size matrix for 88 spp
Input for Matlab matrix manipulation: an excel version of step 1 output significant species pairs for 88 spp
R code for the conceptual framework of predicting community responses
Matlab code for co-occurrence matrix manipulation
About this article
Cite this article
Tulloch, A.I.T., Chadès, I. & Lindenmayer, D. Species co-occurrence analysis predicts management outcomes for multiple threats. Nat Ecol Evol 2, 465–474 (2018). https://doi.org/10.1038/s41559-017-0457-3
Reconsidering priorities for forest conservation when considering the threats of mining and armed conflict
Community Ecology (2021)
Nature Ecology & Evolution (2020)
Scientific Reports (2020)
Meta-analysis on big data of bioactive compounds from mangrove ecosystem to treat neurodegenerative disease