Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-17T18:04:47.725Z Has data issue: false hasContentIssue false
  • English
  • Français

Incorporating local habitat heterogeneity and productivity measures when modelling vertebrate richness

Published online by Cambridge University Press:  07 October 2019

W Justin Cooper Open the ORCID record for W Justin Cooper [Opens in a new window] ,
William J McShea ,
David A Luther  and
Tavis Forrester
W Justin Cooper*
Affiliation:
Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA Biology Department, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
William J McShea
Affiliation:
Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA
David A Luther
Affiliation:
Biology Department, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA Smithsonian Mason School of Conservation, 1500 Remount Road, Front Royal, VA 22630, USA
Tavis Forrester
Affiliation:
Smithsonian Conservation Biology Institute, 1500 Remount Road, Front Royal, VA 22630, USA Oregon Department of Fish and Wildlife, 1401 Gekeler Lane, La Grande, OR 97850, USA
*
Author for correspondence: W Justin Cooper, Email: wcooper2@gmu.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Declining species richness is a global concern; however, the coarse-scale metrics used at regional or landscape levels might not accurately represent the important habitat characteristics needed to estimate species richness. Currently, there exists a lack of knowledge with regard to the spatial extent necessary to correlate remotely sensed habitat metrics to species richness and animal surveys. We provide a protocol for determining the best scale to use when merging remotely sensed habitat and animal survey data as a step towards improving estimates of vertebrate species richness on broad scales. We test the relative importance of fine-resolution habitat heterogeneity and productivity metrics at multiple spatial scales as predictors of species richness for birds, frogs and mammals using a Bayesian approach and a combination of passive monitoring technologies. Model performance was different for each taxonomic group and dependent on the scale at which habitat heterogeneity and productivity were measured. Optimal scales included a 20-m radius for bats and frogs, an 80-m radius for birds and a 180-m radius for terrestrial mammals. Our results indicate that optimal scales do exist when merging remotely sensed habitat measures with ground-based surveys, but they differ between vertebrate groups. Additionally, the selection of a measurement scale is highly influential to our understanding of the relationships between species richness and habitat characteristics.

Keywords

acoustics monitoringcamera traphabitat heterogeneityhyperspectralLiDARproductivityremote sensingscalespecies richnessvertebrate
Type
Research Paper
Information
Environmental Conservation , Volume 47 , Issue 1: Thematic Section: Bringing Species and Ecosystems Together with Remote Sensing Tools to Develop New Biodiversity Metrics and Indicators , March 2020 , pp. 7 - 14
Copyright
© Foundation for Environmental Conservation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bae, S, Müller, J, Lee, D, Vierling, KT, Vogeler, JC, Vierling, LA, Hudak, AT et al. (2018) Taxonomic, functional, and phylogenetic diversity of bird assemblages are oppositely associated to productivity and heterogeneity in temperate forests. Remote Sensing of Environment 215: 145–156.CrossRefGoogle Scholar
Barg, JJ, Aiama, DM, Jones, J, Robertson, RJ (2006) Within-territory habitat use and microhabitat selection by male cerulean warblers (Dendroica cerulea). The Auk 123: 795–806.CrossRefGoogle Scholar
Blomquist, SM, Hunter, ML (2010) A multi-scale assessment of amphibian habitat selection: Wood frog response to timber harvesting. Ecoscience 17: 251–264.CrossRefGoogle Scholar
Bouvier, M, Durrieu, S, Fournier, RA, Renaud, JP (2015) Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment 156: 322–334.CrossRefGoogle Scholar
Boyce, MS (2006) Scale for resource selection functions. Diversity and Distributions 12: 269–276.CrossRefGoogle Scholar
Boyce, MS, Mao, JS, Merrill, EH, Fortin, D, Monica, G, Fryxell, J, Turchin, P (2003) Scale and heterogeneity in habitat selection by elk in Yellowstone National Park. Ecoscience 10: 421–431.CrossRefGoogle Scholar
Britzke, ER, Murray, KL, Heywood, JS, Robbins, LW (2002) Acoustic identification. In: The Indiana Bat: Biology and Management of an Endangered Species, eds Kurta, A, Kennedy, J, pp. 221–225. Austin, TX, USA: Bat Conservation International.Google Scholar
Davies, AB, Asner, GP (2014) Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends in Ecology & Evolution 29: 681–691.CrossRefGoogle ScholarPubMed
De Solla, SR, Shirose, LJ, Fernie, KJ, Barrett, GC, Brousseau, CS, Bishop, CA (2005) Effect of sampling effort and species detectability on volunteer based anuran monitoring programs. Biological Conservation 121: 585–594.CrossRefGoogle Scholar
Depraetere, M, Pavoine, S, Jiguet, F, Gasc, A, Duvail, S, Sueur, J (2012) Monitoring animal diversity using acoustic indices: implementation in a temperate woodland. Ecological Indicators 13: 46–54.CrossRefGoogle Scholar
Dorazio, RM, Royle, JA, Söderström, B, Glimskär, A (2006) Estimating species richness and accumulation by modeling species occurrence and detectability. Ecology 87: 842–854.CrossRefGoogle ScholarPubMed
Flyger, VF (1960) Movements and home range of the gray squirrel, Sciurus carolinensis, in two Maryland woodlots. Ecology 41: 365–369.CrossRefGoogle Scholar
Forrester, T, O’Brien, T, Fegraus, E, Jansen, PA, Palmer, J, Kays, R, Ahumada, J et al. (2016) An open standard for camera trap data. Biodiversity Data Journal 4: e10197.CrossRefGoogle Scholar
Gao, B (1996) NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: 257–266.CrossRefGoogle Scholar
Gelman, A, Hill, J (2006) Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
He, KS, Bradley, BA, Cord, AF, Rocchini, D, Tuanmu, M-N, Schmidtlein, S et al. (2015) Will remote sensing shape the next generation of species distribution models? Remote Sensing in Ecology and Conservation 1: 4–18.CrossRefGoogle Scholar
Hijmans, RJ (2017) raster: geographic data analysis and modeling. R package version 2.6–7.0.Google Scholar
Homer, C, Dewitz, J, Yang, L, Jin, S, Danielson, P, Xian, G et al. (2015) Completion of the 2011 national land cover database for the conterminous United States – representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing 81: 345–354.Google Scholar
Huete, A, Didan, K, Miura, T, Rodriguez, E, Gao, X, Ferreira, L (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195–213.CrossRefGoogle Scholar
Jackson, HB, Fahrig, L (2012) What size is a biologically relevant landscape? Landscape Ecology 27: 929–941.CrossRefGoogle Scholar
Jenkins, CN, Pimm, SL, Joppa, LN (2013) Global patterns of terrestrial vertebrate diversity and conservation. Proceedings of the National Academy of Sciences of the United States of America 110: E2602–E2610.CrossRefGoogle ScholarPubMed
Johnson, DH (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecology 61: 65–71.CrossRefGoogle Scholar
Kalogirou, S (2017) lctools: local correlation, spatial inequalities, geographically weighted regression and other tools. R package version 0.2–6.0.Google Scholar
Kamoske, AG, Dahlin, KM, Stark, SC, Serbin, SP (2019) Leaf area density from airborne LiDAR: comparing sensors and resolutions in a temperate broadleaf forest ecosystem. Forest Ecology and Management 433: 364–375.CrossRefGoogle Scholar
Kruschke, JK, Meredith, M (2018) BEST: Bayesian estimation supersedes the t-Test. R package version 0.5.1.Google Scholar
Marshall, MR, Cooper, RJ (2004) Territory size of a migratory songbird in response to caterpillar density and foliage structure. Ecology 85: 432–445.CrossRefGoogle Scholar
McGarigal, K, Wan, HY, Zeller, KA, Timm, BC, Cushman, SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landscape Ecology 31: 1161–1175.CrossRefGoogle Scholar
McShea, WJ, Forrester, T, Costello, R, He, Z, Kays, R (2016) Volunteer-run cameras as distributed sensors for macrosystem mammal research. Landscape Ecology 31: 55–66.CrossRefGoogle Scholar
Menzel, MA, Carter, TC, Jablonowski, LR, Mitchell, BL, Menzel, JM, Chapman, BR (2001) Home range size and habitat use of big brown bats (Eptesicus fuscus) in a maternity colony located on a rural-urban interface in the southeast. Journal of the Elisha Mitchell Scientific Society 117: 36–45.Google Scholar
Mittelbach, GG, Steiner, CF, Scheiner, SM, Gross, KL, Reynolds, HL, Waide, RB et al. (2001) What is the observed relationship between species richness and productivity? Ecology 82: 2381–2396.CrossRefGoogle Scholar
National Ecological Observatory Network (2017a) Data Products: DP1.10003.001. Provisional data downloaded from http://data.neonscience.org on 25 January 2017. Battelle, Boulder, CO, USA.Google Scholar
National Ecological Observatory Network (2017b) Data Product IDs: DP1.30003.001, DP3.30026.001, DP3.30019.001. 2016. Provided by NEON on 5 February 2017. Battelle, Boulder, CO, USA.Google Scholar
Ozoga, JJ, Harger, EM (1966) Winter activities and feeding habits of northern Michigan coyotes. Journal of Wildlife Management 30: 809–818.CrossRefGoogle Scholar
Proença, V, Martin, LJ, Pereira, HM, Fernandez, M, McRae, L, Belnap, J et al. (2017) Global biodiversity monitoring: from data sources to Essential Biodiversity Variables. Biological Conservation 213: 256–263.CrossRefGoogle Scholar
R Development Core Team (2010) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rodewald, P (2015) The birds of North America. URL https://birdsna.org.Google Scholar
Roussel, J, Auty, D (2018) lidR: airborne LiDAR data manipulation and visualization for forestry applications. R package version 1.5.1.Google Scholar
Secades, C, O’Connor, B, Brown, C, Walpole, M (2014) Earth Observation for Biodiversity Monitoring: A Review of Current Approaches and Future Opportunities for Tracking Progress towards the Aichi Biodiversity Targets. Technical Series No. 72. Montreal, Canada: Secretariat of the Convention on Biological Diversity.Google Scholar
Senior, P, Butlin, RK, Altringham, JD (2005) Sex and segregation in temperate bats. Proceedings of the Royal Society of Biological Sciences 272: 2467–2473.CrossRefGoogle ScholarPubMed
Sing, T, Sander, O, Beerenwinkel, N, Lengauer, T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21: 3490–3491.CrossRefGoogle ScholarPubMed
Skalak, SL, Sherwin, RE, Brigham, RM (2012) Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods in Ecology and Evolution 3: 490–502.CrossRefGoogle Scholar
Su, Y-S, Yajima, M (2015). R2jags: using R to run ‘JAGS’. R package version 0.5–7.0.Google Scholar
Sueur, J, Pavoine, S, Hamerlynck, O, Duvail, S (2008) Rapid acoustic survey for biodiversity appraisal. PLoS ONE 3: e4065.CrossRefGoogle ScholarPubMed
Tews, J, Brose, U, Grimm, V, Tielbörger, K, Wichmann, MC, Schwager, M, Jeltsch, F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography 31: 79–92.CrossRefGoogle Scholar
Theobald, DM, Stevens, DL, White, D, Urquhart, NS, Olsen, AR, Norman, JB (2007) Using GIS to generate spatially balanced random survey designs for natural resource applications. Environmental Management 40: 134–146.CrossRefGoogle ScholarPubMed
Villanueva-Rivera, LJ, Pijanowski, BC (2018) soundecology: soundscape ecology. R package version 1.3.3.Google Scholar
Wiens, JA (1989) Spatial scaling in ecology. Functional Ecology 3: 385–397.CrossRefGoogle Scholar
Wimmer, J, Towsey, M, Roe, P, Williamson, I (2013) Sampling environmental acoustic recordings to determine bird species richness. Ecological Applications 23: 1419–1428.CrossRefGoogle ScholarPubMed
Zipkin, EF, Grant, EHC, Fagan, WF (2012) Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection. Ecological Applications 22: 1962–1972.CrossRefGoogle ScholarPubMed
Supplementary material: File

Cooper et al. supplementary material

Cooper et al. supplementary material

Download Cooper et al. supplementary material(File)
File 56.7 KB