Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

Recent elevated winter loss of honey bee colonies is a major concern. The presence of the mite Varroa destructor in colonies places an important pressure on bee health. V. destructor shortens the lifespan of individual bees, while long lifespan during winter is a primary requirement to survive until the next spring. We investigated in two subsequent years the effects of different levels of V. destructor infestation during the transition from short-lived summer bees to long-lived winter bees on the lifespan of individual bees and the survival of bee colonies during winter. Colonies treated earlier in the season to reduce V. destructor infestation during the development of winter bees were expected to have longer bee lifespan and higher colony survival after winter. Mite infestation was reduced using acaricide treatments during different months (July, August, September, or not treated). We found that the number of capped brood cells decreased drastically between August and November, while at the same time, the lifespan of the bees (marked cohorts) increased indicating the transition to winter bees. Low V. destructor infestation levels before and during the transition to winter bees resulted in an increase in lifespan of bees and higher colony survival compared to colonies that were not treated and that had higher infestation levels. A variety of stress-related factors could have contributed to the variation in longevity and winter survival that we found between years. This study contributes to theory about the multiple causes for the recent elevated colony losses in honey bees. Our study shows the correlation between long lifespan of winter bees and colony loss in spring. Moreover, we show that colonies treated earlier in the season had reduced V. destructor infestation during the development of winter bees resulting in longer bee lifespan and higher colony survival after winter.

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.

Summary: Drought stress selects for a suite of plant traits at root, stem and leaf level. Two strategies are proposed for trees growing in seasonally water‐stressed environments: drought tolerance and drought avoidance. These are respectively associated with evergreen phenology, where plants retain their leaves throughout the year, and deciduous phenology, where plants drop their leaves during dry seasons. Evergreen species are thought to have leaf traits supporting lower photosynthesis and transpiration rates, in order to conserve water during dry periods. We evaluated 18 morphological, chemical and physiological leaf traits of 51 abundant savanna tree species that differed in leaf habit (deciduous and evergreen), selected from two climate types (semi‐arid and humid) in three continents (Australia, Africa and South America) (annual rainfall range: 500–1550 mm), and grown in a common garden experiment. We hypothesised that evergreen species have more conservative water use and differ more across climate types than deciduous species because evergreen species are forced to endure extended water deficits during dry seasons. Trait shifts between semi‐arid and humid savannas did not differ between evergreen and deciduous species. Evergreen species had similar assimilation rates but lower photosynthetic water‐use efficiency (PWUE) than deciduous species, possibly to extend their leaf lifespans by protecting their photosynthetic machinery from overheating through evaporative cooling. Species of humid and semi‐arid environments did not differ with respect to assimilation rate or PWUE, but semi‐arid species did have smaller leaf sizes and greater leaf potassium and phosphorus concentrations. These traits may enable semi‐arid species to maximize growth during episodes of favourable moisture availability. Species from the three continents differed in their leaf traits. These probably reflect the greater proportion of evergreen species in Australia as compared to the other continents and generally infertile soils in the South American sampling sites compared to the wider fertility range in the African sites. Synthesis: Water stress in savannas does not select for more conservative water use, but may select for rapid adjustment to prevailing water conditions and for heat avoidance mechanisms.