Overwintering Physiology and Microhabitat Use of Phyllocnistis populiella (Lepidoptera: Gracilliariidae) in Interior Alaska

2012 ◽  
Vol 41 (1) ◽  
pp. 180-187 ◽  
Author(s):  
Diane Wagner ◽  
Patricia Doak ◽  
Todd Sformo ◽  
Paige M. Steiner ◽  
Bryan Carlson
Keyword(s):  
Microhabitat Use ◽  
Interior Alaska ◽  
Phyllocnistis Populiella

Variable extrafloral nectary expression and its consequences in quaking aspen

2007 ◽  
Vol 85 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Patricia Doak ◽  
Diane Wagner ◽  
Adam Watson
Keyword(s):  
Populus Tremuloides ◽  
Natural Variation ◽  
Leaf Miner ◽  
Quaking Aspen ◽  
Insect Community ◽  
Interior Alaska ◽  
Leaf Mining ◽  
Secretory Glands ◽  
The Relationship ◽  
Phyllocnistis Populiella

Extrafloral nectaries (EFNs) are secretory glands most commonly linked to defensive mutualisms. Both a plant’s need for defense and the strength of defense provided by mutualists will vary with plant condition and local insect community. Thus, the benefit of EFNs may vary spatially and temporally. However, little attention has been paid to natural variation in the presence and abundance of EFNs within and among individuals of the same species. Quaking aspen, Populus tremuloides Michx., bear EFNs on a subset of their leaves. Here, we describe patterns of EFN expression on shoots within ramets, among ramets, and among putative clones in interior Alaska. We also examine the relationship between EFN presence and herbivory by both the very abundant aspen leaf miner, Phyllocnistis populiella Chambers, and less common chewing herbivores. The proportion of leaves bearing EFNs varied from 33% to 87% among distinct aspen stands. Within stands, short (1–2 m height) ramets had higher EFN frequency than their taller (>4 m) neighbors. Patterns of herbivory also differed between short and tall ramets. Compared with leaves without EFNs, those with EFNs suffered less mining damage on short ramets but slightly higher damage on tall ramets. Tall ramets suffered more chewing damage than short ramets, but this damage was unrelated to the presence of EFNs. Our results suggest that variable EFN expression may be explained by variation in the benefits of EFNs. Leaves with EFNs on short ramets benefit through reduction in leaf mining, but this benefit does not extend to tall ramets or other forms of herbivory.

Geologic Map of Central (Interior) Alaska

1998 ◽  
Author(s):  
Frederic H. Wilson ◽  
James H. Dover ◽  
Dwight C. Bradley ◽  
Florence R. Weber ◽  
Thomas K. Bundtzen ◽  
...  
Keyword(s):  
Interior Alaska ◽  
Geologic Map

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

Comparative drought sensitivity of co‐occurring white spruce and paper birch in interior Alaska

2021 ◽  
Author(s):  
Patrick F. Sullivan ◽  
Annalis H. Brownlee ◽  
Sarah B.Z. Ellison ◽  
Sean M.P. Cahoon
Keyword(s):  
White Spruce ◽  
Drought Sensitivity ◽  
Interior Alaska ◽  
Paper Birch

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

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