a b s t r a c tLight detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features -optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

Observations reported by citizens are crucial to the ability of scientists to inform policy on biodiversity. This is particularly relevant in the case of preventing and controlling biological invasions; that is, the introduction and spread of species outside their natural ranges as a consequence of human activity. Such invasions of natural ecosystems represent one of the main threats to biodiversity, economy, and human well-being globally, and policies on tackling this issue require a strong evidence base that increasingly is built on citizen science. Many citizens are motivated to collect data for their own interest, while presumably, few expect to make a major impact on policy. The needs of policy-makers are not always aligned with the approaches used by citizens to collect and share data. Therefore, how can we motivate citizen science for the needs of policy without compromising the enjoyment that citizens gain from collecting biodiversity observations? How can policy-makers support citizens to collect the data they need?Solutions require two components, a combination of social and technological innovation. Initiatives aimed at supporting decision-making processes should involve more societal actors and be built in a more collaborative or even co-created manner with citizens, scientists, and policy-makers. Technological solutions can be achieved through regular, rapid, and open publication of biodiversity data products. We envisage frequent publication of maps and indicators from rapidly mobilized data, with clear pointers to gaps in knowledge. Improving the links between data collection and delivery of policy-relevant information demonstrates -to citizens and their organizations -the need for their data, and gives them a clear view on the impact of their data on policy. This visibility also empowers stakeholder organizations in the policy development process.

AimMapping the geographic distribution of non-native aquatic species is a critically important precursor to understanding the anthropogenic and environmental factors that drive freshwater biological invasions. Such efforts are often limited to local scales and/or to single species, due to the challenges of data acquisition at larger scales. Here, we map the distribution of non-native freshwater species richness across the continental United States and investigate the role of human activity in driving macro-scale patterns of aquatic invasion.LocationThe continental United States.MethodsWe assembled maps of non-native aquatic species richness by compiling occurrence data on exotic animal and plant species from publicly accessible databases. Using a dasymetric model of human population density and a spatially explicit model of recreational freshwater fishing demand, we analysed the effect of these metrics of human influence on the degree of invasion at the watershed scale, while controlling for spatial and sampling bias. We also assessed the effects that a temporal mismatch between occurrence data (collected since 1815) and cross-sectional predictors (developed using 2010 data) may have on model fit.ResultsNon-native aquatic species richness exhibits a highly patchy distribution, with hotspots in the Northeast, Great Lakes, Florida, and human population centres on the Pacific coast. These richness patterns are correlated with population density, but are much more strongly predicted by patterns of recreational fishing demand. These relationships are strengthened by temporal matching of datasets and are robust to corrections for sampling effort.Main conclusionsDistributions of aquatic non-native species across the continental US are better predicted by freshwater recreational fishing than by human population density. This suggests that observed patterns are driven by a mechanistic link between recreational activity and aquatic non-native species richness and are not merely the outcome of sampling bias associated with human population density.

Globally, the number of invasive alien species (IAS) continues to increase and management and policy responses typically need to be adopted before conclusive empirical evidence on their environmental and socioeconomic impacts are available. Consequently, numerous protocols exist for assessing IAS impacts and differ considerably in which evidence they include. However, inclusive strategies for building a transparent evidence base underlying IAS impact assessments are lacking, potentially affecting our ability to reliably identify priority IAS. Using alien parrots in Europe as a case study, here we apply an evidence-mapping scheme to classify impact evidence and evaluate the consequences of accepting different subsets of available evidence on impact assessment outcomes. We collected environmental and socioeconomic impact data in multiple languages using a “wiki-review” process, comprising a systematic evidence search and an online editing and consultation phase. Evidence was classified by parrot species, impact category (e.g. infrastructure), geographical area (e.g. native range), source type (e.g. peer-review), study design (e.g. experimental) and impact direction (deleterious, beneficial and no impact). Our comprehensive database comprised 386 impact entries from 233 sources. Most evidence was anecdotal (50%). A total of 42% of entries reported damage to agriculture (mainly in native ranges), while within Europe most entries concerned interspecific competition (39%). We demonstrate that the types of evidence included in assessments can strongly influence impact severity scores. For example, including evidence from the native range or anecdotal evidence resulted in an overall switch from minimal-moderate to moderate-major overall impact scores. We advise using such an evidence-mapping approach to create an inclusive and updatable database as the foundation for more transparent IAS impact assessments. When openly shared, such evidence-mapping can help better inform IAS research, management and policy.