Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2?= 0.61, df = 1, F?= 14.3, p?= 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2?= 0.72, df = 1, F?= 23.09, p?= 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices

A Multi-Scale Test of the Forage Maturation Hypothesis in a Partially Migratory Ungulate Population

The forage maturation hypothesis (FMH) proposes that ungulate migration is driven by selection for high forage quality. Because quality declines with plant maturation, but intake declines at low biomass, ungulates are predicted to select for intermediate forage biomass to maximize energy intake by following phenological gradients during the growing season. We tested the FMH in the Canadian Rocky Mountains by comparing forage availability and selection by both migrant and nonmigratory resident elk (Cervus elaphus) during three growing seasons from 2002-2004. First, we confirmed that the expected trade-off between forage quality and quantity occurred across vegetation communities. Next, we modeled forage biomass and phenology during the growing season by combining ground and remote-sensing approaches. The growing season started 2.2 days earlier every 1 km east of the continental divide, was delayed by 50 days for every 1000-m increase in elevation, and occurred 8 days earlier on south aspects. Migrant and resident selection for forage biomass was then compared across three spatial scales (across the study area, within summer home ranges, and along movement paths) using VHF and GPS telemetry locations from 119 female elk. Migrant home ranges occurred closer to the continental divide in areas of higher topographical diversity, resulting in migrants consistently selecting for intermediate biomass at the two largest scales, but not at the. nest scale along movement paths. In contrast, residents selected maximum forage biomass across all spatial scales. To evaluate the consequences of selection, we compared exposure at telemetry locations of migrant and resident elk to expected forage biomass and digestibility. The expected digestibility for migrant elk in summer was 6.5% higher than for residents, which was corroborated with higher fecal nitrogen levels for migrants. The observed differences in digestibility should increase migrant elk body mass, pregnancy rates, and adult and calf survival rates. Whether bottom-up effects of improved forage quality are realized will ultimately depend on trade-offs between forage and predation. Nevertheless, this study provides comprehensive evidence that montane ungulate migration leads to greater access to higher-quality forage relative to nonmigratory congeners, as predicted by the forage maturation hypothesis, resulting primarily from large-scale selection patterns

Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks

Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration

Appendix A. Modeling peak of growing season availability of forage-biomass components for elk.

Modeling peak of growing season availability of forage-biomass components for elk

Appendix E. Predicted probabilities of use by migrant and resident elk as a function of herbaceous forage biomass at two levels within the home-range scale.

Predicted probabilities of use by migrant and resident elk as a function of herbaceous forage biomass at two levels within the home-range scale

Appendix F. Linear mixed-effects time-series models for forage (herbaceous and shrub) biomass exposure for migrant and resident elk GPS locations, 2002–2004.

Linear mixed-effects time-series models for forage (herbaceous and shrub) biomass exposure for migrant and resident elk GPS locations, 2002–2004

Appendix B. Modeling forage maturation using NDVI and ground maturation models.

Modeling forage maturation using NDVI and ground maturation models

Appendix C. Estimating growing-season parameters for phenology modeling.

Estimating growing-season parameters for phenology modeling

Appendix D. Individual MODIS-interval quality–quantity regression equations.

Individual MODIS-interval quality–quantity regression equations