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Mazerolle, M. J.

Publications and source records attributed to Mazerolle, M. J..

2 recordsLinked to original sources

Out-of-sight or out-of-site? Forays and detection probabilities in single-season occupancy models

Occupancy models have become popular in wildlife survey analyses because they account for the frequent failure to detect individuals of targeted species. Those individuals sometimes move outside sampling sites, i.e. exhibit temporary emigration. In such cases, occupancy models may become difficult to interpret or even misleading either at the species or the individual level, because they confound presence at the site, availability for detection given presence, and actual detection by the observer. We quantified the probabilities of these three components with spruce grouse (Falcipennis canadensis) in southern Quebec, Canada. We conducted call-response surveys of 24 grouse monitored by radio-telemetry. We defined sites empirically as circular areas of 83 m radius centered on the observer, corresponding to the maximum detection distance obtained. Based on telemetry locations, grouse were present at the site during 42 % of the surveys. Six stationary grouse were present during surveys, but were never detected. Thus, only individuals that moved in the presence of the observer (89 %) were considered available for detection. Individuals available for detection were detected in 51 % of the cases. We simulated detection histories and built single-season occupancy models, based on the empirical relationship between detection probability and the distance measured between observers and grouse. When temporary emigration was ignored, site occupancy was{psi} = 0.89, and the associated probability of detection was p = 0.23. When instances of temporary emigration were dropped, estimates were{psi} = 0.88 and p = 0.41. Using only grouse available for detection, estimates were{psi} = 0.87 and p = 0.42. Disentangling the components of detection probabilities had little impact on occupancy estimates, but showed a major effect of temporary emigration on estimated detection probabilities.

ecology↗

Modelling vegetation understory cover using LiDAR metrics

Forest understory vegetation is an important feature of wildlife habitat among other things. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult. LiDAR has the potential to generate remotely sensed forest understory structure data, yet this potential has to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, SMAPE=15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. Based on these results, we conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, but that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.

ecology↗