The scale of effect of landscape context varies with the species’ response variable measured
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To detect an effect of landscape context on a species’ response, the landscape variables need to be measured within the appropriate distance from the species’ response, i.e. at the scale of effect. However, it is not clear what factors determine the scale of effect.
Our objective was to test the prediction that the scale of effect should be smallest when the species’ response is fecundity, larger for abundance, and larger still for occurrence.
We compared the scale of effect of two landscape variables (road density, forest proportion) on the three responses (fecundity, abundance, occurrence) for the wood frog (Lithobates sylvaticus) in eastern Ontario, Canada. We used egg mass surveys of 34 ponds to estimate fecundity (mean eggs/mass), abundance (number of masses), and occurrence (presence/absence of egg masses). We then empirically estimated the scale of effect of each landscape variable on each response.
The scale of effect differed among responses, from 0.2 to 3.0 km radii; however, it did not vary in the predicted order. Furthermore, the order was not consistent between the two landscape variables.
Our results show that the scale of effect of a landscape variable on a given species can differ for different response variables. However, they also suggest that these differences in the scale of effect are not predictable. Thus, the most reliable way to ensure a landscape context study is conducted at the correct spatial extent is to estimate the scale of effect empirically, rather than ‘guesstimating’ the extent a priori.
KeywordsLandscape structure Landscape size Multi-scale model Focal patch Spatial scale Landscape extent
We thank the private landowners who let us sample their ponds. Erik Pervin and Caitlin Brunton were indispensable field assistants. We thank Joseph Bennett and Jeremy Kerr for helpful comments and suggestions. We also thank the associate editor and two anonymous reviewers for their comments on an earlier version of this paper. This work was supported by a Natural Sciences and Engineering Research Council of Canada Grant to Lenore Fahrig.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All work complied with the Canadian Council on Animal Care requirements for the use of eggs in research (Category of Invasiveness A).
The data sets generated during the current study are available in the Mendeley Data repository, https://doi.org/10.17632/ccw64rpj22.1.
- Brennan JM, Bender DJ, Contreras TA, Fahrig L (2002) Focal patch landscape studies for wildlife management: optimizing sampling effort across scales. In: Liu J, Taylor WW (eds) Integrating landscape ecology into natural resource management. Cambridge University Press, Cambridge, pp 68–91. https://doi.org/10.1017/CBO9780511613654.006 CrossRefGoogle Scholar
- Clark PJ, Reed JM, Tavernia BG, Windmiller BS, Regosin JV (2008) Urbanization effects on spotted salamander and wood frog presence and abundance. Herpetol Conserv 3:67–75Google Scholar
- Crouch WB, Paton PWC (2000) Using egg-mass counts to monitor wood frog populations. Wildl Soc Bull 28:895–901Google Scholar
- Karraker NE (2007) A new method for estimating clutch sizes of ambystomatid salamanders and ranid frogs: introducing the ovagram. Herpetol Rev 38:46–48Google Scholar
- Moraga AD, Pervin E (2018) Efficient estimation of amphibian clutch using image analysis of compressed globular egg masses. Herpetol Conserv Biol 13:341–346Google Scholar
- Moretto L (2018) A small-scale response of urban bat activity to tree cover. Dissertation, Carleton UniversityGoogle Scholar
- Nakazawa M (2018) fmsb: functions for medical statistics book with some demographic data. R package version 0.6.3. https://CRAN.R-project.org/package=fmsb
- R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Thornton DH, Fletcher RJ Jr (2014) Body size and spatial scales in avian response to landscapes: a meta-analysis. Ecography 37:454–463Google Scholar
- Venables B, Ripley B (2002) MASS: support functions and datasets for Venable’s and Ripley’s MASS. Modern Applied Statistics with S. Fourth Edition, SpringerGoogle Scholar