Limitations of forest overstory composition and age as proxies for habitat in a harvested boreal forest
Bibliographic record
Abstract
Despite the importance of wildlife habitat protection in meeting land use management objectives, criteria for habitat identification are surprisingly amorphous. For example, while much current habitat modeling has tended to \navoid the term "niche modeling," niche assumptions are implicit - the presence of predators and competitors is essential to whether or not a species uses, or will use, an area. Nonetheless, there are species for which important elements of niche are not generally associated with legal interpretations of their "habitat". Woodland caribou (Rangifer tarandus caribou) are one such example. The range of the \nforest-dwelling ecotypes of woodland caribou has been declining in Canada since at least the late 1940s, and woodland caribou were assessed as Threatened by the Committee on the Status of Endangered Wildlife in Canada and were listed under the Federal Species at Risk Act in 2003. They are also protected under Ontario's Endangered Species Act (2007) and other provincial and territorial legislation. The consequences of management decisions, and the lens through which these decisions are assessed, have been intensified due to these legal implications. \nMost current research supports the hypothesis that higher predation is the key factor in decline and that larger wolf (Canis lupus) populations are due to \nincreased abundance of early seral stage, forage-rich hardwood and mixedwood \nforests, created largely by logging, which support additional prey for wolves, including moose (Alces alces L.). While predators and apparent competitors appear to play a primary role in habitat selection by caribou, habitat modeling generally relies on forest overstory and age as a surrogate for predator avoidance. Yet, how well these models correspond to caribou, wolf, and moose use is largely unknown. Legal interpretations of protection rest primarily on interpretations of forest overstory and age, making explicit only the importance of forest disturbance. \nHere, I tested the ability of forest resource inventories (FRI), a key tool in identifying and quantifying wildlife habitat in forest management, to assess 3 key elements associated with caribou winter habitat: lichen, regenerating understory and predator use. I assessed the presence of Cladonia lichen, an important winter forage species for woodland caribou, using stand characteristics provided in FRIs. Further, I used ground data collected from regenerating areas (2009-2010) of previously conifer-dominated forests in northwestern Ontario, Canada, 10 and 30 years after logging, and 10 and 30 years after fire, to test if understory development and moose forage abundance differed between the two disturbance types and artificial or natural regeneration approaches. In addition, I used winter aerial surveys (2010-2013) and logistic regression to compare the characteristics of a conventional habitat model (forest overstory composition and age) to other habitat characteristics (and/or their surrogates). I also applied a novel approach for Structural Equation Modeling (SEM) to explore causal and indirect caribou habitat relationships at broad and fine scales. \nI found FRI was not capable of accurately predicting understory vegetation, specifically Cladonia lichen, in spite of the ability of field-based data using the same characteristics providing strong prediction. Further, I found understory composition varied significantly depending on post-harvest regeneration approach. Abundance of shrubs, as well as herbaceous plants (forage for apparent competitors for woodland caribou), was greater in naturally- regenerated post-harvest stands than similarly aged fire origin or post-harvest stands that used more intensive regeneration approaches. And lastly, I found that older, conifer forests alone, as depicted in FRI, did not provide good predictive capabilities of caribou use at broad scales. \nWhile conventional models based on forest overstory composition and age may be useful as a coarse filter in interpreting caribou habitat, more attention should be given to their limitations in landscapes changed by industrial development, particularly where road networks are likely to facilitate predator access and the identification of such habitat has legal implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".