Cool as a moose: How can browsing counteract climate warming effects across boreal forest ecosystems?

Herbivory has potential to modify vegetation responses to climatic changes. However, climate and herbivory also affect each other, and rarely work in isolation from other ecological factors, such as plant-plant competition. Thus, it is challenging to predict the extent to which herbivory can counteract, amplify, or interact with climate impacts on ecosystems. Here, we investigate how moose modify climatic responses of boreal trees by using experimental exclosures on two continents and modeling complex causal pathways including several climatic factors, multiple tree species, competition, tree height, time, food availability, and herbivore presence, density, and browsing intensity. We show that moose can counteract, that is, "cool down" positive temperature responses of trees, but that this effect varies between species depending on moose foraging preferences. Growth of preferred deciduous trees was strongly affected by moose, whereas growth of less preferred conifers was mostly driven by climate and tree height. In addition, moose changed temperature responses of rowan in Norway and balsam fir in Canada, by making fir more responsive to temperature but decreasing the strength of the temperature response of rowan. Snow protected trees from browsing, and therefore moose "cooling power" might increase should a warming climate result in decreased snow cover. Furthermore, we found evidence of indirect effects of moose via plant-plant competition: By constraining growth of competing trees, moose can contribute positively to the growth of other trees. Our study shows that in boreal forests, herbivory cooling power is highly context dependent, and in order to understand its potential to prevent changes induced by warming climate, species differences, snow, competition, and climate effects on browsing need to be considered.

Contrasting spatial, temporal and environmental patterns in observation and specimen based species occurrence data.

Species occurrence data records the location and time of an encounter with a species, and is valuable for many aspects of ecological and evolutionary analyses. A key distinction within species occurrence data is between (1) collected and preserved specimens that can be taxonomically validated (i.e., natural history collections), and (2) observations, which are more error prone but richer in terms of number and spread of observations. In this study we analyse the distribution in temporal, spatial, taxonomic and environmental coverage of specimen- and observation based species occurrence data for land plants in Norway, a region with strong climatic and human population density gradients. Of 4.8 million species occurrence records, the majority (78%) were observations. However, there was a greater species richness in the specimen record (N = 4691) than in the observation record (N = 3193) and most species were recorded more as specimens than observations. Specimen data was on average older, and collected later during the year. Both record types were highly influenced by a small number of prolific contributors. The species most highly represented in the observation data set were widespread or invasive, while in the specimen records, taxonomically challenging species were overrepresented. Species occurrence records were unevenly spatially distributed. Both specimen and observation records were concentrated in regions of Norway with high human population density and with high temperatures and precipitation, but in different regions within Norway. Observation and specimen records thus differ in taxonomic, temporal, spatial and environmental coverage for a well-sampled group and study region, potentially influencing the ecological inferences made from studies utilizing species occurrence data. The distribution of observation data dominates the dataset, so inferences of species diversity and distributions do not correspond to the evolutionary or physiological knowledge of species, which is based on specimen data. We make recommendations for users of biodiversity data, and collectors to better exploit the complementary strengths of these distinct biodiversity data types.