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A framework to evaluate climate effects on forest tree diseases
Author(s) -
Hen Paul E.,
Frankel Susan J.,
Woods Alex J.,
Worrall James J.,
Norlander Daniel,
Zambino Paul J.,
Warwell Marcus V.,
Shaw Charles G.
Publication year - 2020
Publication title -
forest pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.535
H-Index - 49
eISSN - 1439-0329
pISSN - 1437-4781
DOI - 10.1111/efp.12649
Subject(s) - climate change , biology , ecology , disease , precipitation , abiotic component , temporal scales , vegetation (pathology) , tree (set theory) , environmental resource management , environmental science , geography , meteorology , medicine , mathematical analysis , mathematics , pathology
A conceptual framework for evaluation of climate effects on tree diseases is presented. Climate can exacerbate tree diseases by favouring pathogen biology, including reproduction and infection processes. Climatic conditions can also cause abiotic disease—direct stress or mortality when trees’ physiological limits are exceeded. When stress is sublethal, weakened trees may subsequently be killed by secondary organisms. To demonstrate climate's involvement in disease, associations between climatic conditions and disease expression provide the primary evidence of atmospheric involvement because experimentation is often impractical for mature trees. This framework tests spatial and temporal relationships of climate and disease at several scales to document climate effects, if any. The presence and absence of the disease can be contrasted with climate data and models at geographic scales: stand, regional and species range. Temporal variation in weather, climate and climate change is examined during onset, development and remission of the disease. Predisposing factors such as site and stand conditions can modify the climate effects of some diseases, especially at finer spatial scales. Spatially explicit climate models that display temperature and precipitation or derivative models such as snow and drought stress provide useful data, and however, information on disease extent at different spatial scales and monitoring through time are often incomplete. The framework can be used to overcome limitations in other disease causality approaches, such as Koch's postulates, and allow for the integration of vegetation, pathogen and environmental data into causality determinations.