Skip to main content

Advertisement

SpringerLink
Log in

Relating mammal species richness to landscape patterns across multiple spatial scales

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Understanding the relationships between spatial pattern, spatial scale and biodiversity can help ecologists to assess the impacts of environmental change and inform management plans. Spatial pattern research has often focussed on the effect of modified landscapes on species diversity. However, few studies have examined species responses to spatial pattern from other sources, including those which vary over time, such as fire.

Objectives

We investigated the effect of composition and configuration for topographic, ecological and disturbance factors on mammal species richness. In addition, we examined whether the magnitude and predictive strength of the relationship with richness varied with spatial scale.

Methods

We sampled ground-dwelling mammals at 187 sites in the Otway Ranges of south-eastern Australia. A gradient modelling approach was used to characterise landscape composition and configuration for each predictor. Relationships with mammal richness were modelled using Bayesian Networks at ten different spatial scales (7–1165 ha).

Results

Composition and configuration were both important to species richness, although the strength and presence of relationships varied across the ten scales. Patterns in NDVI, time since fire, habitat complexity and elevation had the strongest effects on mammal species richness.

Conclusions

Our findings highlight the importance of measuring both the composition and configuration of environmental measures at different spatial scales to assess their effect on species richness. Further, studies focusing on just one environmental measure of spatial pattern or one spatial scale will miss important relationships between environmental variables and species richness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abdel Moniem HEM, Holland JD (2013) Habitat connectivity for pollinator beetles using surface metrics. Landsc Ecol 28:1251–1267

    Google Scholar 

  • Arroyo-Rodríguez V, Rojas C, Saldaña-Vázquez RA, Stoner KE (2016) Landscape composition is more important than landscape configuration for phyllostomid bat assemblages in a fragmented biodiversity hotspot. Biol Conserv 198:84–92

    Google Scholar 

  • Atlas of Living Australia (2020) Atlas of Living Australia occurrence download. Retrieved 22nd July 2020, from https://doi.org/10.26197/5f17bd5357e68

  • Atwood TC, Marcot BG, Douglas DC, Amstrup SC, Rode KD, Durner GM, Bromaghin JF (2016) Forecasting the relative influence of environmental and anthropogenic stressors on polar bears. Ecosphere 7:e01370

    Google Scholar 

  • August PV (1983) The role of habitat complexity and heterogeneity in structuring tropical mammal communities. Ecology 64:1495–1507

    Google Scholar 

  • Auld TD, Bradstock RA (1996) Soil temperatures after the passage of a fire: Do they influence the germination of buried seeds? Aust J Ecol 21:106–109

    Google Scholar 

  • Banks SC, Knight EJ, McBurney L, Blair D, Lindenmayer DB (2011) The effects of wildfire on mortality and resources for an arboreal marsupial: resilience to fire events but susceptibility to fire regime change. PLoS ONE 6:e22952

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bennett A (1993) Microhabitat use by the long-nosed potoroo, Potorous tridactylus, and other small mammals in remnant forest vegetation, south-western Victoria. Wildl Res 20:267

    Google Scholar 

  • Bennett AF, Radford JQ, Haslem A (2006) Properties of land mosaics: implications for nature conservation in agricultural environments. Biol Conserv 133:250–264

    Google Scholar 

  • Bivand R, Hauke J, Kossowski T (2013) Computing the Jacobian in Gaussian spatial autoregressive models: an illustrated comparison of available methods. Geogr Anal 45:150–179

    Google Scholar 

  • Bivand R, Piras G (2015) Comparing implementations of estimation methods for spatial econometrics. J Stat Softw 63:1–36

    Google Scholar 

  • Booth GD, Niccolucci MJ, Schuster EG (1994) Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. United States Department of Agriculture, Forest Service, Ogden, USA

    Google Scholar 

  • Burgess EE, Maron M (2016) Does the response of bird assemblages to fire mosaic properties vary among spatial scales and foraging guilds? Landsc Ecol 31:687–699.

    Google Scholar 

  • Cartar RV, Real LA (1997) Habitat structure and animal movement: the behaviour of bumble bees in uniform and random spatial resource distributions. Oecologia 112:430–434.

    PubMed  Google Scholar 

  • Cheal D (2010) Growth stages and tolerable fire intervals for Victoria’s native vegetation data sets. Fire and Adaptive Management Report No. 84. Department of Sustainability and Environment, East Melbourne, Victoria, Australia

  • Clarke MF (2008) Catering for the needs of fauna in fire management: science or just wishful thinking? Wildl Res 35:385–394.

    Google Scholar 

  • Cooke RM, Kurowicka D, Hanea AM, Morales O, Ababei DA, Ale B, Roelen A (2007) Continuous/Discrete non-parametric Bayesian belief nets with UNICORN and UNINET. http://resolver.tudelft.nl/uuid:ec6eb1df-2bb6-4aa0-98b2-e1990c03e4b0

  • Cushman SA, Gutzweiler K, Evans JS, McGarigal K (2010) The gradient paradigm: a conceptual and analytical framework for landscape ecology. In: Cushman SA, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, New York, pp 83–110

    Google Scholar 

  • Delalandre L, Montesinos-Navarro A (2018) Can co-occurrence networks predict plant-plant interactions in a semi-arid gypsum community? Perspect Plant Ecol Evol Syst 31:36–43.

    Google Scholar 

  • Delcourt HR, Delcourt PA, Webb T (1983) Dynamic plant ecology: the spectrum of vegetational change in space and time. Quat Sci Rev 1:153–175.

    Google Scholar 

  • Department of Environment Land Water and Planning (DELWP) (2018) Fire history overlay of most recent fires. Victoria, Australia. https://www.data.vic.gov.au/data/dataset/fire-history-overlay-of-most-recent-fires

  • Department of Sustainability and Environment (DSE) (2012) Ecological vegetation class (EVC) benchmarks for each bioregion. Victoria, Australia

    Google Scholar 

  • Dess T (2016) Fire operations plan 2017/18 - 2019/20: Barwon South West region. Department of Environment, Land, Water and Planning, Victoria, Australia

    Google Scholar 

  • Desta F, Colbert JJ, Rentch JS, Gottschalk KW (2004) Aspect induced differences in vegetation, soil, and microclimatic characteristics of an Appalachian watershed. Castanea 69:92–108

    Google Scholar 

  • Di Stefano J, McCarthy MA, York A, Duff TJ, Slingo J, Christie F (2013) Defining vegetation age class distributions for multispecies conservation in fire-prone landscapes. Biol Conserv 166:111–117.

    Google Scholar 

  • Di Stefano J, Owen L, Morris R, Duff TOM, York A (2011) Fire, landscape change and models of small mammal habitat suitability at multiple spatial scales. Austral Ecol 36:638–649.

    Google Scholar 

  • Docherty TDS, Hethcoat MG, MacTavish LM, MacTavish D, Dell S, Stephens PA, Willis SG (2020) Burning savanna for avian species richness and functional diversity. Ecol Appl. https://doi.org/10.1002/eap.2091

    Article  PubMed  Google Scholar 

  • Dorph A, Swan M, Rochelmeyer E, Di Stefano J (2020) Complex habitat drives mammal communities in a flammable landscape. For Ecol Manage 462:117979.

    Google Scholar 

  • Driscoll DA, Lindenmayer DB, Bennett AF, Bode M, Bradstock RA, Cary GJ, Clarke MF, Dexter N, Fensham R, Friend G, Gill M, James S, Kay G, Keith DA, MacGregor C, Russell-Smith J, Salt D, Watson JEM, Williams RJ, York A (2010) Fire management for biodiversity conservation: key research questions and our capacity to answer them. Biol Conserv 143:1928–1939.

    Google Scholar 

  • Fairman TA, Bennett LT, Nitschke CR (2019) Short-interval wildfires increase likelihood of resprouting failure in fire-tolerant trees. J Environ Manage 231:59–65.

    PubMed  Google Scholar 

  • Farnsworth LM, Nimmo DG, Kelly LT, Bennett AF, Clarke MF (2014) Does pyrodiversity beget alpha, beta or gamma diversity? A case study using reptiles from semi-arid Australia. Divers Distrib 20:663–673.

    Google Scholar 

  • Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, New York

    Google Scholar 

  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian data analysis, 3rd edn. CRC Press, Boca Raton, FL, USA

    Google Scholar 

  • Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences

  • Guélat J, Kéry M (2018) Effects of spatial autocorrelation and imperfect detection on species distribution models. Methods Ecol Evol 9:1614–1625.

    Google Scholar 

  • Guillera-Arroita G, Kéry M, Lahoz-Monfort JJ (2019) Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation. Ecol Evol 9:780–792.

    PubMed  PubMed Central  Google Scholar 

  • Hageer Y, Esperón-Rodríguez M, Baumgartner JB, Beaumont LJ (2017) Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species? PeerJ 5:e3446.

    PubMed  PubMed Central  Google Scholar 

  • Haining R (2003) Spatial data analysis: theory and practice. Cambridge University Press, Cambridge

    Google Scholar 

  • Hanea A, Napoles OM, Ababei D (2015) Non-parametric Bayesian networks: improving theory and reviewing applications. Reliab Eng Syst Saf 144:265–284.

    Google Scholar 

  • Haslem A, Avitabile SC, Taylor RS, Kelly LT, Watson SJ, Nimmo DG, Kenny SA, Callister KE, Spence-Bailey LM, Bennett AF, Clarke MF (2012) Time-since-fire and inter-fire interval influence hollow availability for fauna in a fire-prone system. Biol Conserv 152:212–221.

    Google Scholar 

  • Haslem A, Kelly LT, Nimmo DG, Watson SJ, Kenny SA, Taylor RS, Avitabile SC, Callister KE, Spence-Bailey LM, Clarke MF (2011) Habitat or fuel? Implications of long-term, post-fire dynamics for the development of key resources for fauna and fire. J Appl Ecol 48:247–256.

    Google Scholar 

  • Haslem A, Leonard SWJ, Bruce MJ, Christie F, Holland GJ, Kelly LT, MacHunter J, Bennett AF, Clarke MF, York A (2016) Do multiple fires interact to affect vegetation structure in temperate eucalypt forests? Ecol Appl 26:2412–2421.

    PubMed  Google Scholar 

  • Hawkins BA, Field R, Cornell HV, Currie DJ, Guégan J-F, Kaufman DM, Kerr JT, Mittelbach GG, Oberdorff T, O’Brien EM, Porter EE, Turner JRG (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology 84:3105–3117.

    Google Scholar 

  • Hempson GP, Parr CL, Archibald S, Anderson TM, Mustaphi CJC, Dobson AP, Donaldson JE, Morrison TA, Probert J, Beale CM (2017) Continent-level drivers of African pyrodiversity. Ecography 40:1–10.

    Google Scholar 

  • Hijmans RJ (2019) raster: geographic data analysis and modeling.

  • Hindell MA, Lee AK (1987) Habitat use and tree preferences of koalas in a mixed eucalypt forest. Wildl Res 14:349–360.

    Google Scholar 

  • Holland JD, Bert DG, Fahrig L (2004) Determining the spatial scale of species’ response to habitat. Bioscience 54:227–233.

    Google Scholar 

  • Holland JD, Yang S (2016) Multi-scale studies and the ecological neighborhood. Curr Landsc Ecol Reports 1:135–145.

    Google Scholar 

  • Hradsky BA, Penman TD, Ababei D, Hanea A, Ritchie EG, York A, Di Stefano J (2017) Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions. Ecosphere 8:e01926.

    Google Scholar 

  • Johnson CJ, Parker KL, Heard DC, Gillingham MP (2002) Movement parameters of ungulates and scale-specific responses to the environment. J Anim Ecol 71:225–235.

    Google Scholar 

  • Kellner K (2019) jagsUI: a wrapper around “rjags” to streamline “JAGS” analyses. (v. 1.5.1).

  • Kellner KF, Swihart RK (2014) Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9:e111436.

    PubMed  PubMed Central  Google Scholar 

  • Kelly LT, Bennett AF, Clarke MF, McCarthy MA (2015) Optimal fire histories for biodiversity conservation. Conserv Biol 29:473–481.

    PubMed  Google Scholar 

  • Kelly LT, Brotons L, McCarthy MA (2017) Putting pyrodiversity to work for animal conservation. Conserv Biol 31:952–955.

    PubMed  Google Scholar 

  • Kelly LT, Nimmo DG, Spence-Bailey LM, Taylor RS, Watson SJ, Clarke MF, Bennett AF (2012) Managing fire mosaics for small mammal conservation: a landscape perspective. J Appl Ecol 49:412–421.

    Google Scholar 

  • Kendron PJ, Frazier AE, Ovando-Montejo GA, Wang J (2018) Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales. Landsc Ecol 33:1489–1504.

    Google Scholar 

  • Kéry M, Royle JA (2008) Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. J Appl Ecol 45:589–598.

    Google Scholar 

  • Kéry M, Royle JA (2016) Applied hierarchical modeling in ecology: analysis of distribution, abundance and species richness in R and BUGS (Volume 1), 1st edn. Academic Press, Cambridge, MA

    Google Scholar 

  • Korb KB, Nicholson AE (2004) Bayesian artificial intelligence. Chapman & Hall/CRC Press UK, Boca Raton, FL, USA

    Google Scholar 

  • Landuyt D, Broekx S, D’hondt R, Engelen G, Aertsens J, Goethals PLM (2013) A review of Bayesian belief networks in ecosystem service modelling. Environ. Model. Softw. 46:1–11

    Google Scholar 

  • Lang DT (2014) RDCOMClient: R-DCOM client. http://www.omegahat.org/RDCOMClient, http://www.omegahat.org, http://www.omegahat.org/bugs

  • Law BS, Dickman CR (1998) The use of habitat mosaics by terrestrial vertebrate fauna: implications for conservation and management. Biodivers Conserv 7:323–333.

    Google Scholar 

  • Letnic M, Dickman CR, Tischler MK, Tamayo B, Beh C-L (2004) The responses of small mammals and lizards to post-fire succession and rainfall in arid Australia. J Arid Environ 59:85–114

    Google Scholar 

  • MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:2248–2255

    Google Scholar 

  • Maikano GN, Cohn J, Di Stefano J (2018) Are germination cues for soil-stored seed banks different in structurally different fire-prone communities? Austral Ecol 43:89–101.

    Google Scholar 

  • Martin RE, Sapsis DB (1992) Fires as agents of biodiversity: pyrodiversity promotes biodiversity. In: Proceedings of the conference on biodiversity of northwest California ecosystems. Cooperative Extension, University of California, Berkeley

  • Mayor SJ, Schneider DC, Schaefer JA, Mahoney SP (2009) Habitat selection at multiple scales. Ecoscience 16:238–247.

    Google Scholar 

  • McColl-Gausden SC, Penman TD (2019) Pathways of change: predicting the effects of fire on flammability. J Environ Manage 232:243–253.

    PubMed  Google Scholar 

  • McColl-Gausden SC, Bennett LT, Duff TJ, Cawson JG, Penman TD (2019) Climatic and edaphic gradients predict variation in wildland fuel hazard in south-eastern Australia. Ecography 43:443–455.

    Google Scholar 

  • McGarigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps.

  • McGarigal K, Tagil S, Cushman SA (2009) Surface metrics: an alternative to patch metrics for the quantification of landscape structure. Landsc Ecol 24:433–450.

    Google Scholar 

  • McGranahan DA, Hovick TJ, Elmore RD, Engle DM, Fuhlendorf SD (2018) Moderate patchiness optimizes heterogeneity, stability, and beta diversity in mesic grassland. Ecol Evol 8:5008–5015.

    PubMed  PubMed Central  Google Scholar 

  • McLean KA, Trainor AM, Asner GP, Crofoot MC, Hopkins ME, Campbell CJ, Martin RE, Knapp DE, Jansen PA (2016) Movement patterns of three arboreal primates in a Neotropical moist forest explained by LiDAR-estimated canopy structure. Landsc Ecol 31:1849–1862.

    Google Scholar 

  • Menkhorst P, Knight F (2004) A field guide to the mammals of Australia. Oxford University Press, Melbourne, Australia

    Google Scholar 

  • Milns I, Beale CM, Smith VA (2010) Revealing ecological networks using Bayesian network inference algorithms. Ecology 91:1892–1899.

    PubMed  Google Scholar 

  • Moreira F, Delgado A, Ferreira S, Borralho R, Oliveira N, Inácio M, Silva JS, Rego F (2003) Effects of prescribed fire on vegetation structure and breeding birds in young Pinus pinaster stands of northern Portugal. For Ecol Manage 184:225–237.

    Google Scholar 

  • Morris G, Hostetler JA, Oli MK, Conner LM (2011) Effects of predation, fire, and supplemental feeding on populations of two species of Peromyscus mice. J Mammal 92:934–944.

    Google Scholar 

  • Murphy BP, Bradstock RA, Boer MM, Carter J, Cary GJ, Cochrane MA, Fensham RJ, Russell-Smith J, Williamson GJ, Bowman DMJS (2013) Fire regimes of Australia: a pyrogeographic model system. J Biogeogr 40:1048–1058.

    Google Scholar 

  • Nyberg JB, Marcot BG, Sulyma R (2006) Using Bayesian belief networks in adaptive management. Can J For Res 36:3104–3116.

    Google Scholar 

  • Nyman P, Sherwin CB, Langhans C, Lane PNJ, Sheridan GJ (2014) Downscaling regional climate data to calculate the radiative index of dryness in complex terrain. Aust Meteorol Oceanogr J 64:109–122

    Google Scholar 

  • Parkins K, Scott A, Di Stefano J, Swan M, Sitters H, York A (2019) Habitat use at fire edges: does animal activity follow temporal patterns of habitat change? For Ecol Manage 451:117343.

    Google Scholar 

  • Pausas JG, Keeley JE (2017) Epicormic resprouting in fire-prone ecosystems. Trends Plant Sci 22:1008–1015.

    CAS  PubMed  Google Scholar 

  • Pawson SM, Marcot BG, Woodberry OG (2017) Predicting forest insect flight activity: a Bayesian network approach. PLoS ONE 12:e0183464.

    PubMed  PubMed Central  Google Scholar 

  • Pearce J, Lindenmayer D (1998) Bioclimatic analysis to enhance reintroduction biology of the endangered helmeted honeyeater (Lichenostomus melanops cassidix) in Southeastern Australia. Restor Ecol 6:238–243.

    Google Scholar 

  • Penman T, Christie F, Andersen AN, Bradstock RA, Cary GJ, Henderson MK, Price O, Tran C, Wardle GM, Williams RJ, York A (2011) Prescribed burning: how can it work to conserve the things we value? Int J Wildl Fire 20:721–733.

    Google Scholar 

  • Penman TD, Binns D, Shiels AR, Plummer S (2008) Germination responses of a dry sclerophyll forest soil-stored seedbank to fire related cues. Cunninghamia 10:547–555

    Google Scholar 

  • Pettorelli N, Olav Vik J, Mysterud A, Gaillard J-M, Tucker CJ, Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol 20:503–510.

    PubMed  Google Scholar 

  • Plavsic MJ (2014) Proximate and ultimate drivers of small-mammal recolonization after fire: microhabitat conditions, rainfall and species traits. Anim Conserv 17:573–582.

    Google Scholar 

  • Plummer M (2019) rjags: Bayesian graphical models using MCMC. (v. 4–10). https://cran.r-project.org/package=rjags

  • R Core Team (2017) R: a language and environment for statistical computing. (v. 3.4.3). www.r-project.org

  • Rettie WJ, Messier F (2000) Hierarchical habitat selection by woodland caribou: its relationship to limiting factors. Ecography 23:466–478.

    Google Scholar 

  • Ritchie LE, Betts MG, Forbes G, Vernes K (2009) Effects of landscape composition and configuration on northern flying squirrels in a forest mosaic. For Ecol Manage 257:1920–1929.

    Google Scholar 

  • Roberts SL, van Wagtendonk JW, Miles AK, Kelt DA, Lutz JA (2008) Modeling the effects of fire severity and spatial complexity on small mammals in Yosemite National Park, California. Fire Ecol 4:83–104.

    Google Scholar 

  • Rochelmeyer E, Di Stefano J, Dorph A, Swan M (2019) Linking fuel, habitat and ground-dwelling mammals in flammable landscapes. For Ecol Manage 441:215–228.

    Google Scholar 

  • Ruggiero A, Kitzberger T (2004) Environmental correlates of mammal species richness in South America: effects of spatial structure, taxonomy and geographic range. Ecography 27:401–416

    Google Scholar 

  • Santos X, Badiane A, Matos C (2016) Contrasts in short- and long-term responses of Mediterranean reptile species to fire and habitat structure. Oecologia 180:205–216.

    PubMed  Google Scholar 

  • Scutari M (2010) Learning Bayesian networks with the bnlearn R Package. J Stat Softw 35:1–22

    Google Scholar 

  • Searle KR, Stokes CJ, Gordon IJ (2008) When foraging and fear meet: using foraging hierarchies to inform assessments of landscapes of fear. Behav Ecol 19:475–482.

    Google Scholar 

  • Sebastiá MT (2004) Role of topography and soils in grassland structuring at the landscape and community scales. Basic Appl Ecol 5:331–346.

    Google Scholar 

  • Seto KC, Fleishman E, Fay JP, Betrus CJ (2004) Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI. Int J Remote Sens 25:4309–4324.

    Google Scholar 

  • Shryock DF, Esque TC, Chen FC (2015) Topography and climate are more important drivers of long-term, post-fire vegetation assembly than time-since-fire in the Sonoran Desert, US. J Veg Sci 26:1134–1147.

    Google Scholar 

  • Sitters H, Christie FJ, Di Stefano J, Swan M, Penman T, Collins PC, York A (2014) Avian responses to the diversity and configuration of fire age classes and vegetation types across a rainfall gradient. For Ecol Manage 318:13–20.

    Google Scholar 

  • Sitters H, York A, Swan M, Christie F, Di Stefano J (2016) Opposing responses of bird functional diversity to vegetation structural diversity in wet and dry forest. PLoS ONE 11:e0164917.

    PubMed  PubMed Central  Google Scholar 

  • Smith AC, Zarnetske P, Dahlin K, Wilson A, Latimer A (2020) geodiv: methods for calculating gradient surface metrics. (v. 0.1.1). https://cran.r-project.org/package=geodiv

  • SPIP (2001) The scanning probe image processor SPIP. Image metrology APS, Lyngby. http://www.imagemet.com/

  • St-Louis V, Pidgeon AM, Clayton MK, Locke BA, Bash D, Radeloff VC (2009) Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography 32:468–480.

    Google Scholar 

  • Stalenberg E, Wallis IR, Cunningham RB, Allen C, Foley WJ (2014) Nutritional correlates of koala persistence in a low-density population. PLoS ONE 9:e113930.

    PubMed  PubMed Central  Google Scholar 

  • Stein A, Gerstner K, Kreft H (2014) Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol Lett 17:866–880.

    PubMed  Google Scholar 

  • Stout KJ, Sullivan WP, Mainsah E, Luo N, Mathia T, Zahouani H (1993) The Development of methods for the characterisation of roughness in three dimensions. Publication no. EUR 15178 EN of the Commission of the European Communities Dissemination of Scientific and Technical Knowledge Unit, Luxembourg

  • Swan M, Christie F, Sitters H, York A, Di Stefano J (2015) Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure. Ecol Appl 25:2293–2305.

    PubMed  Google Scholar 

  • Tingley MW, Nadeau CP, Sandor ME (2020) Multi-species occupancy models as robust estimators of community richness. Methods Ecol Evol. https://doi.org/10.1111/2041-210X.13378

    Article  Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Google Scholar 

  • Turner MG, Gardner RH (2015) Landscape ecology in theory and practice: pattern and process, 2nd edn. Springer, New York, NY

    Google Scholar 

  • Viedma O (2008) The influence of topography and fire in controlling landscape composition and structure in Sierra de Gredos (Central Spain). Landsc Ecol 23:657–672.

    Google Scholar 

  • Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York

    Google Scholar 

  • Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397.

    Google Scholar 

  • Wiest WA, Correll MD, Marcot BG, Olsen BJ, Elphick CS, Hodgman TP, Guntenspergen GR, Shriver WG (2019) Estimates of tidal-marsh bird densities using Bayesian networks. J Wildl Manage 83:109–120.

    Google Scholar 

  • Williams RJ, Duff GA, Bowman DMJS (1996) Variation in the composition and structure of tropical savannas as a function of rainfall and soil texture along a large-scale climatic gradient in the Northern Territory, Australia. J Biogeogr 23:747–756.

    Google Scholar 

  • Zipkin EF, Dewan A, Andrew Royle J (2009) Impacts of forest fragmentation on species richness: a hierarchical approach to community modelling. J Appl Ecol 46:815–822.

    Google Scholar 

  • Zipkin EF, Royle JA, Dawson DK, Bates S (2010) Multi-species occurrence models to evaluate the effects of conservation and management actions. Biol Conserv 143:479–484.

    Google Scholar 

Download references

Acknowledgements

This study was a part of a long-term fire and biodiversity research project in the Otway Ranges conducted by the Fire Ecology and Biodiversity Research Group at the University of Melbourne. We thank Fiona Christie, Holly Sitters and Alan York for their contributions to the original study design. Additionally, we thank Ellen Rochelmeyer, Julio Najera, Sarah Matthews, Marci Chai, Jenny Huang, Tia Kearney, Matthew Oaten, Louise Falls, Luke Adams, Oakley Germech, Tom Cook, and Tiani Kane for their assistance in the field. We would like to acknowledge the Traditional Owners of the land where this research took place, the Gadubanud and Wadda wurrung peoples. This research was funded by the Victorian Department of Environment, Land, Water and Planning (DELWP), the University of Melbourne, the Holsworth Wildlife Research Endowment and the Ecological Society of Australia. Finally, we would like to thank Sarah McColl-Gausden, Michael Dorph, the anonymous reviewers and Editor for comments that improved this manuscript.

Funding

This research was funded by the Victorian Department of Environment, Land, Water and Planning (DELWP), the University of Melbourne, the Holsworth Wildlife Research Endowment and the Ecological Society of Australia.

Author information

Authors and Affiliations

  1. School of Ecosystem and Forest Sciences, University of Melbourne, 4 Water Street, Creswick, VIC, 3363, Australia

    Annalie Dorph, Matthew Swan, Julian Di Stefano & Trent D. Penman

Authors
  1. Annalie Dorph
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Swan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julian Di Stefano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Trent D. Penman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: AD, MS, TP, JDS; Data Curation: AD, MS, JDS; Formal Analysis: AD, TP; Funding Acquisition: AD, MS, JDS; Investigation: AD, MS, JDS; Methodology: AD, MS, TP, JDS; Project administration: AD, MS; Supervision: MS, TP, JDS; Validation: AD; Visualization: AD; Writing—original draft: AD; Writing—review and editing: AD, MS, TP, JDS.

Corresponding author

Correspondence to Annalie Dorph.

Ethics declarations

All field work was conducted in accordance with the National Parks Act (Research Permit Number 10007746) and Forests Act (Scientific Permit Number 00420160819). Faunal surveys were approved by the University of Melbourne Faculty of Science Ethics Committee (Register Number 1513682.1).

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dorph, A., Swan, M., Di Stefano, J. et al. Relating mammal species richness to landscape patterns across multiple spatial scales. Landscape Ecol 36, 1003–1022 (2021). https://doi.org/10.1007/s10980-021-01208-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-021-01208-8

Keywords

Search

Navigation