I love distance-based sampling. Quick and often very effective. There have been numerous innovations and developments associated with the estimation of density, spatial pattern, and other important attributes of distribution. The flexibility depending on underlying dispersion patterns has also significantly evolved.
1. Run out parallel transects perpendicular to the longest axis of your study site.
2. Estimate relative distances & dispersion patterns prior to detailed sampling.
3. On each transect, measure the distance from regular points on transect to nearest shrub (x) then from that shrub to the next nearest shrub moving in the same direction (y), i.e. do not go backwards hence the t-square nomenclature.
4. Select appropriate regular intervals within a transect to ensure that you do not sample the same shrubs.
5. Consider alternating sides of transect.
6. Disperse transects similarly to ensure representative, non-overlapping coverage of study site.
7. Sample at least 150 shrubs to effectively estimate dispersion and approximately 100 for density.
8. Georeference the start and end point of every transect.
9. Use these data to calculate density, dispersion, and explore size-classes of shrubs.
10. Select appropriately blocked variation from with site for animal surveys associated with the shrubs relative to non-canopied sites.
Foundation species effect estimation
(a) For each shrub, record height, widest canopy dimension, and minimum canopy dimension to be able to estimate shrub volumes.
(b) At each shrub, use a small quadrat to record all species present and total abundance.
(c) At each shrub, record animal activity/presence signs.
Full details of the basics of this distance-based sampling listed in these publications
Diggle, P. J. 1977. The Detection of Random Heterogeneity in Plant Populations. – Biometrics 33: 390-394.
Diggle, P. J. 1982. Some Statistical Aspects of Spatial Distribution Models for Plants and Trees. – Studia Forestalia Suecia 1: 162.
Diggle, P. J., Besage, J. and Gleaves, J. T. 1976. Statistical analysis of spatial point patterns by means of distance methods. – Biometrika 32: 659-667.
Diggle, P. J. 1983. Statistical analysis of spatial point patterns. – Academic Press.
Sample applications & statistical innovations/expansions
Henderson, A. 2008. Using the T-square sampling method to estimate population size, demographics and other characteristics in emergency food security assessments (EFSAs). – World Food Programme Technical Guidance Sheet 11: 1-17.
Whiting, M. J., Dixon, J. R. and Murray, R. C. 1993. Spatial Distribution of a Population of Texas Horned Lizards (Phrynosoma cornutum: Phrynosomatidae) Relative to Habitat and Prey. – The Southwestern Naturalist 38: 150-154.
Aerts, R., November, E., Van der Borght, I., Behailu, M., Hermy, M. and Muys, B. 2006. Effects of pioneer shrubs on the recruitment of the fleshy-fruited tree Olea europaea ssp. cuspidata in Afromontane savanna. – Applied Vegetation Science 9: 117-126.
Hines, W. G. S. and Hines, R. J. O. H. 1979. The Eberhardt Statistic and the Detection of Nonrandomness of Spatial Point Distributions. – Biometrika 66: 73-79.
Bostoen, K., Chalabi, Z. and Grais, R. F. 2007. Optimisation of the T-square sampling method to estimate population sizes. – Emerging Themes in Epidemiology 4: 7.
Liu, C. 2001. A comparison of five distance-based methods for spatial pattern analysis. – Journal of Vegetation Science 12: 411-416.
Barabesi, L. 2001. A design-based approach to the estimation of plant density using point-to-plant sampling. – Journal of Agricultural, Biological, and Environmental Statistics 6: 89-98.
Clayton, G. and Cox, T. F. 1986. Some Robust Density Estimators for Spatial Point Processes. – Biometrics 42: 753-767.