Q18 – S. Herniman

Avian habitat suitability in Newfoundland

Species extinctions are happening at a rate greater than the natural background extinction rate (Pimm and Raven, 2000), as a result of this, it is important to know what aspects of a habitat each species requires to survive. High diversity of birds in an ecosystem can indicate a healthy ecosystem (Browder et al., 2002; Gregory and Strien, 2010; Robledano et al., 2010). Since bird abundance is dependent on forest structure (Beedy, 1981; Engstrom et al., 1984), structural metrics can be used to predict bird diversity in areas devoid of bird population data. About 20% of the island of Newfoundland is forested (fig 1). As such, predicting bird diversity in Newfoundland’s forests is important to the management and conservation of the island’s wild lands.

Traditionally, data on forest structure have been collected by on the ground field surveyors. This method comes with several drawbacks: data collection is time consuming, limited by accessibility, expensive, and is reserved to small samples of a forested area. There is also an element of surveyor bias which must be overcome with training and practice (Ott, 2017). More recently, an active remote sensing technology known as airborne laser scanning (ALS) has grown in popularity in the forestry industry. ALS is capable of capturing a census of the structural aspects of a forest quickly, however the technology creates large files which must be processed and collecting the data can be very expensive (Tattoni et al., 2012). Due to the high monetary expense of ALS, researchers are often forced to use ageing data for analysis (Vierling et al., 2014).

A link between structural vegetation indices and bird species diversity has long been known from traditional field surveys (Erdelen, 1984; Karr and Roth, 1971; MacArthur and MacArthur, 1961; Mills et al., 1991; Willson, 1974), and many studies have used ALS data to study these relationships (Bradbury et al., 2005; Clawges et al., 2008; Seavy et al., 2009), however, to model these relationships on a wall to wall scale over large tracts of land, traditional forest cruising and ALS data are inadequate due to their high cost. Passively collected remote sensing data such as multispectral images are available on a wall to wall scale at little or no cost making them ideal for wall to wall modelling, however there is no established link between the structural characteristics of forest structural indices and multispectral data. It is therefore essential to establish relationships between these different scales and data types to model bird habitat suitability over large areas. This study focuses on using remote sensing data at multiple scales to address this issue.

This project aims to link forest structure data to bird presence data at two spatial scales over Newfoundland – a focal area, known as Harry’s River Watershed (fig 2), and the entirety of the island (fig 3). The procedure will be congruent at both scales: build relationships between avian occurrence and forest structure, measured in permanent sample plots, then extract these relationships with ALS data and subsequently extrapolate over the whole of Newfoundland using contiguous spatial layers.

This study will ultimately address the following questions:

  1. Which structural characteristics of Newfoundland forests have the greatest influence on the presence of birds?
  2. Can structural characteristics be used to infer bird habitat over Newfoundland with remote sensing?





Beedy, E.C., 1981. Bird Communities and Forest Structure in the Sierra Nevada of California. The Condor 83, 97–105. https://doi.org/10.2307/1367415

Bradbury, R.B., Hill Ross A., Mason David C., Hinsley Shelley A., Wilson Jeremy D., Balzter Heiko, Anderson Guy Q. A., Whittingham Mark J., Davenport Ian J., Bellamy Paul E., 2005. Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments. Ibis 147, 443–452. https://doi.org/10.1111/j.1474-919x.2005.00438.x

Browder, S.F., Johnson, D.H., Ball, I.J., 2002. Assemblages of breeding birds as indicators of grassland condition. Ecol. Indic. 2, 257–270. https://doi.org/10.1016/S1470-160X(02)00060-2

Clawges, R., Vierling, K., Vierling, L., Rowell, E., 2008. The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sens. Environ., Earth Observations for Terrestrial Biodiversity and Ecosystems Special Issue 112, 2064–2073. https://doi.org/10.1016/j.rse.2007.08.023

Engstrom, R.T., Crawford, R.L., Baker, W.W., 1984. Breeding Bird Populations in Relation to Changing Forest Structure following Fire Exclusion: A 15-Year Study. Wilson Bull. 96, 437–450.

Erdelen, M., 1984. Bird communities and vegetation structure: I. Correlations and comparisons of simple and diversity indices. Oecologia 61, 277–284. https://doi.org/10.1007/BF00396773

Gregory, R.D., Strien, A. van, 2010. Wild Bird Indicators: Using Composite Population Trends of Birds as Measures of Environmental Health. http://dx.doi.org/10.2326/osj.9.3. https://doi.org/10.2326/osj.9.3

Karr, J.R., Roth, R.R., 1971. Vegetation Structure and Avian Diversity in Several New World Areas. Am. Nat. 105, 423–435. https://doi.org/10.1086/282735

MacArthur, R.H., MacArthur, J.W., 1961. On Bird Species Diversity. Ecology 42, 594–598. https://doi.org/10.2307/1932254

Mills, G.S., Dunning, J.B., Bates, J.M., 1991. The Relationship between Breeding Bird Density and Vegetation Volume. Wilson Bull. 103, 468–479.

Ott, E., 2017. Personal communication.

Pimm, S.L., Raven, P., 2000. Biodiversity: Extinction by numbers. Nature 403, 843. https://doi.org/10.1038/35002708

Robledano, F., Esteve, M.A., Farinós, P., Carreño, M.F., Martínez-Fernández, J., 2010. Terrestrial birds as indicators of agricultural-induced changes and associated loss in conservation value of Mediterranean wetlands. Ecol. Indic. 10, 274–286. https://doi.org/10.1016/j.ecolind.2009.05.006

Seavy, N.E., Viers Joshua H., Wood Julian K., 2009. Riparian bird response to vegetation structure: a multiscale analysis using LiDAR measurements of canopy height. Ecol. Appl. 19, 1848–1857. https://doi.org/10.1890/08-1124.1

Tattoni, C., Rizzolli, F., Pedrini, P., 2012. Can LiDAR data improve bird habitat suitability models? Ecol. Model., 7th European Conference on Ecological Modelling (ECEM) 245, 103–110. https://doi.org/10.1016/j.ecolmodel.2012.03.020

Vierling, K.T., Swift, C.E., Hudak, A.T., Vogeler, J.C., Vierling, L.A., 2014. How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities. Remote Sens. Lett. 5, 185–193. https://doi.org/10.1080/2150704X.2014.891773

Willson, M.F., 1974. Avian Community Organization and Habitat Structure. Ecology 55, 1017–1029. https://doi.org/10.2307/1940352