The study of invasion ecology usually focuses on the negative impacts of alien species, while potential positive impacts are often overlooked. Understanding of biotic interactions may thus be skewed towards the negative, which could have important implications for ecological management and conservation. This article provides a comprehensive review of all types of impacts, both beneficial and detrimental, that can result from species translocation. An extensive review of literature on species introductions to terrestrial, freshwater and marine ecosystems and involving a wide range of taxa (including microorganisms, parasites, plants, insects, amphibians, reptiles, birds, mammals, fish and Crustacea) showed that, despite limited research into facilitative alien-native interactions, such interactions occur surprisingly frequently. Examples were found of introduced species acting as hosts, food sources, pollinators or seed dispersers for native species, as well as providing herbivory, predatory or parasite release. However, research showed that numerous negative interactions also occurred and combination impacts (when an alien benefits some natives but disadvantages others) were common. In many cases, the traditional view that biological invasions constitute a significant threat to native biota is both accurate and appropriate. Efforts to prevent translocation and control non-native species can be vital. However, the “native good, alien bad” maxim does not convey the complexity of invasion ecology: alien species do not axiomatically pose a threat to native biota. In order to move understanding of invasion ecology forward and to develop maximally-effective management strategies, facilitative alien-native interactions need to be added into the alien species debate

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R²), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R² values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines.

Widely available and inexpensive mobile phone applications offer users, whether professional ecologists or interested amateurs, the potential for simple and rapid automated identification of species, without the need to use field guides and identification keys. The increasing accuracy of machine learning is well established, but it is currently unclear if, and under what circumstances, free‐to‐use mobile phone applications are accurate for identifying plants to species level in real‐world field conditions. We test five popular and free identification applications for plants using 857 professionally identified images of 277 species from 204 genera. Across all applications, 85% of images were identified correctly in the top five suggestions, and 69% were correct with the first suggestion. Plant type (woody, forbs, grasses, rushes/sedges, ferns/horsetails) was a significant determinant of identification performance for each application. For some applications, image saliency was also important; exposure and focus were not significant. Applications performed well, with at least one of the three best‐performing applications identifying 96% of images correctly as their first suggestion. We conclude that, subject to some caveats, free phone‐based plant identification applications are valid and useful tools for those wanting rapid identification and for anyone wanting to engage with the natural world. Read the freePlain Language Summaryfor this article on the Journal blog.

The phenology of many species shows strong sensitivity to climate change; however, with few large scale intra-specific studies it is unclear how such sensitivity varies over a species’ range. We document large intra-specific variation in phenological sensitivity to temperature using laying date information from 67 populations of two co-familial European songbirds, the great tit (Parus major) and blue tit (Cyanistes caeruleus), covering a large part of their breeding range. Populations inhabiting deciduous habitats showed stronger phenological sensitivity than those in evergreen and mixed habitats. However, populations with higher sensitivity tended to have experienced less rapid change in climate over the past decades, such that populations with high phenological sensitivity will not necessarily exhibit the strongest phenological advancement. Our results show that to effectively assess the impact of climate change on phenology across a species’ range it will be necessary to account for intra-specific variation in phenological sensitivity, climate change exposure, and the ecological characteristics of a population.

Pre-roost murmuration displays by European starlings Sturnus vulgaris are a spectacular example of collective animal behaviour. To date, empirical research has focussed largely on flock movement and biomechanics whereas research on possible causal mechanisms that affect flock size and murmuration duration has been limited and restricted to a small number of sites. Possible explanations for this behaviour include reducing predation through the dilution, detection or predator confusion effects (the “safer together” hypotheses) or recruiting more birds to create larger (warmer) roosts (the “warmer together” hypothesis). We collected data on size, duration, habitat, temperature and predators from >3,000 murmurations using citizen science. Sightings were submitted from 23 countries but UK records predominated. Murmurations occurred across a range of habitats but there was no association between habitat and size/duration. Size increased significantly from October to early February, followed by a decrease until the end of the season in March (overall mean 30,082 birds; maximum 750,000 birds). Mean duration was 26 minutes (± 44 seconds SEM). Displays were longest at the start/end of the season, probably due to a significant positive relationship with day length. Birds of prey were recorded at 29.6% of murmurations. The presence of predators including harrier Circus, peregrine Falco peregrinus, and sparrowhawk Accipiter nisus was positively correlated with murmuration size (R² = 0.401) and duration (R² = 0.258), especially when these species were flying near to, or actively engaging with, starlings. Temperature was negatively correlated with duration but the effect was much weaker than that of day length. When predators were present, murmurations were statistically more likely to end with all birds going down en masse to roost rather than dispersing from the site. Our findings suggest that starling murmurations are primarily an anti-predator adaptation rather than being undertaken to attract larger numbers of individuals to increase roost warmth.