Climate change causes far-reaching disruption in nature, where tolerance thresholds already have been exceeded for some plants and animals. In the short-term, deer may respond to climate through individual physiological and behavioral responses. Over time, individual responses can aggregate to the population level and ultimately lead to evolutionary adaptations. We systematically reviewed literature (published 2000-2022) to summarize the effect of temperature, rainfall, snow, combined measures (e.g., the North Atlantic Oscillation) and extreme events, on deer species inhabiting boreal and temperate forests in terms of their physiology, spatial use and population dynamics. We targeted deer species which inhabit relevant biomes in North America, Europe and Asia: moose, roe deer, elk, red deer, sika deer, fallow deer, white-tailed deer, mule deer, caribou and reindeer. Our review (218 papers) shows that many deer populations will likely benefit in-part from warmer winters, but hotter and drier summers may exceed their physiological tolerances. We found support for deer expressing both morphological, physiological, and behavioral plasticity in response to climate variability. For example, some deer species can limit the effects of harsh weather conditions by modifying habitat use and daily activity patterns, while the physiological responses of female deer can lead to long-lasting effects on population dynamics. We identified 20 patterns, among which some illustrate antagonistic pathways, suggesting that detrimental effects will cancel out some of the benefits of climate change. Our findings highlight the influence of local variables (eg. population density and predation) for how deer will respond to climatic conditions. We identified several knowledge gaps, such as studies regarding the potential impact on these animals of extreme weather events, snow type and wetter autumns. The patterns we have identified in this literature review should help managers understand how populations of deer may be affected by regionally projected futures regarding temperature, rainfall and snow.

Literature search and screening

We searched for relevant literature with publication month and years Jan 2000- Nov 2022 in two databases: Web of Science (https://www.webofscience.com/; The Core Collection) and Scopus (https://www.scopus.com). We used the same nested Boolean (i.e., AND between different groups of search terms, OR within groups of similar search terms and NOT for excluding search terms) search string in the title, abstract and keywords fields for both Web of Science (TS) and Scopus (TITLE-ABS-KEY) (complete search strings in the supplementary material, Appendix S1). We targeted the relevant deer species for the boreal and temperate forests (i.e., Alces alces, Capreolus capreolus, Cervus spp., Dama dama, Odocoileus spp., Rangifer tarandus; for distribution maps, see Fig. S2), by using a combination of Latin and common names that we combined with geographical constraints based on names of biogeographical regions, countries, and states. We combined this search string with climate related variables (temperature, precipitation etc., Appendix S2). From here on, we refer to Cervus elaphus as red deer, and C. canadensis as wapiti. We refer to R. tarandus living in Europe and Asia as reindeer but as caribou when living in North America. We restrained the search by language (English) and document type (peer-reviewed papers). Our aim was to be as least exclusive as possible, but this led to some unexpected irrelevant documents. We therefore added exclusion terms to filter out non-targeted biogeographical regions and scientific fields. We did not exclude any topical part of our search because it would be impossible to make a coherent pre-emptive list of terms to exclude.

The search hits from Web of Science and Scopus were merged and cleaned of duplicates, resulting in 8154 unique papers. Screening of papers was conducted using Rayyan (Ouzzani et al. 2016), a free web application for reviewing articles. Decisions on exclusion or inclusion were first made by reading the title and abstract of each article and determining their conformity to the criteria targeted by the search terms: right topic (i.e., in context of climate change), species (Cervidae excluding semi-domestic reindeer), geography (boreal and temperate zones), language (English) and type of study (new, or new synthesis of, empirical temporal data on deer response to climate). We included papers of migratory caribou residing in forest for larger parts of the year. Note that papers did not have to specify a climate change context to be included. It was sufficient that it contained temporal data on deer and weather variations. Given the controversies surrounding definitions of climate change, rather few papers proclaim having documented climate change and a stricter criterion would have excluded almost all papers.

The robustness of the exclusion criteria and the individual screener divergence of the first screening were tested before the actual screening was done. Fifty randomly drawn papers were reviewed by all authors individually without conferring. The papers were randomly distributed among authors. The discrepancies were rather few (13 out of 49 papers (27%) had at least 1 person with a different opinion than the others). After discussing each of these cases in detail, the basis for coherent decision making was improved. To verify the improvement, another control procedure was applied for the remaining screening: 289 papers were each read by two to four authors. The result of this control screening showed 18 (6%) conflicting decisions.

Screening of the remaining 7815 papers was done by the authors one by one and assigned equally among readers according to alphabetic order by the first author of the papers. The first screening finally generated 556 papers possibly relevant for the review. All papers with conflicting decisions in the test and control screenings were included among the 556. The possibly relevant papers were then equally divided between the authors. These papers were read completely and again scrutinized for conformation to criteria, resulting in a final list of 218 papers relevant for review. Data from these papers were then tabulated and systemized per demographics (species, location, season, etc.), deer responses and climate factor. Further details on this data collection are specified in Appendix S3. The table here in Dryad includes the detailed tabulations used to produce Table 1, Figure 1, Figure in the main article, and Table S3 in the Appendix.