Vegetation structure from LiDAR explains the local richness of birds across Denmark

Classic ecological research into the determinants of biodiversity patterns emphasised the important role of three‐dimensional (3D) vegetation heterogeneity. Yet, measuring vegetation structure across large areas has historically been difficult. A growing focus on large‐scale research questions has caused local vegetation heterogeneity to be overlooked compared with more readily accessible habitat metrics from, for example, land cover maps. Using newly available 3D vegetation data, we investigated the relative importance of habitat and vegetation heterogeneity for explaining patterns of bird species richness and composition across Denmark (42,394 km$^{2}$). We used standardised, repeated point counts of birds conducted by volunteers across Denmark alongside metrics of habitat availability from land‐cover maps and vegetation structure from rasterised LiDAR data (10 m resolution). We used random forest models to relate species richness to environmental features and considered trait‐specific responses by grouping species by nesting behaviour, habitat preference and primary lifestyle. Finally, we evaluated the role of habitat and vegetation heterogeneity metrics in explaining local bird assemblage composition. Overall, vegetation structure was equally as important as habitat availability for explaining bird richness patterns. However, we did not find a consistent positive relationship between species richness and habitat or vegetation heterogeneity; instead, functional groups displayed individual responses to habitat features. Meanwhile, habitat availability had the strongest correlation with the patterns of bird assemblage composition. Our results show how LiDAR and land cover data complement one another to provide insights into different facets of biodiversity patterns and demonstrate the potential of combining remote sensing and structured citizen science programmes for biodiversity research. With the growing coverage of LiDAR surveys, we are witnessing a revolution of highly detailed 3D data that will allow us to integrate vegetation heterogeneity into studies at large spatial extents and advance our understanding of species' physical niches

The role of land use and land cover change in climate change vulnerability assessments of biodiversity: a systematic review

Context For many organisms, responses to climate change (CC) will be affected by land-use and land-cover changes (LULCC). However, the extent to which LULCC is concurrently considered in climate change vulnerability assessments (CCVAs) is unclear. Objectives We identify trends in inclusion of LULCC and CC in vulnerability assessments of species and the direction and magnitude of their combined effect on biodiversity. Further, we examine the effect size of LULCC and CC in driving changes in “currencies” of response to CC, such as distribution, abundance and survival. Methods We conducted a systematic literature review of articles published in the last 30 years that focused on CCVA and accounted for impacts of both CC and LULCC. Results Across 116 studies, 34% assumed CC and LULCC would act additively, while 66% allowed for interactive effects. The majority of CCVAs reported similar effect sizes for CC and LULCC, although they affected different CCVA currencies. Only 14% of the studies showed larger effects of CC than of LULCC. Another 14% showed larger effects of LULCC than CC, specifically for dispersal, population viability, and reproduction, which tend to be strongly affected by fragmentation and disturbance. Although most studies found that LULCC and CC had negative effects on species currencies, in some cases effects were neutral or even positive. Conclusions CCVAs that incorporate LULCC provided a better account of drivers of vulnerability, and highlight aspects of drivers that are generally more amenable to on-the-ground management intervention than CCVAs that focus on CC alone

Assessing the effects of complexity in cross-temporal transferability of species distribution modelling predictions using palaeobotanical data

Valoración de la transferencia temporal de los modelos de distribución de especies para su aplicación en nuestros días utilizando datos paleobotánicos Corilus avellana y Alnus glutinosa

Late Quaternary climate legacies in contemporary plant functional composition

The functional composition of plant communities is commonly thought to be determined by contemporary climate. However, if rates of climate‐driven immigration and/or exclusion of species are slow, then contemporary functional composition may be explained by paleoclimate as well as by contemporary climate. We tested this idea by coupling contemporary maps of plant functional trait composition across North and South America to paleoclimate means and temporal variation in temperature and precipitation from the Last Interglacial (120 ka) to the present. Paleoclimate predictors strongly improved prediction of contemporary functional composition compared to contemporary climate predictors, with a stronger influence of temperature in North America (especially during periods of ice melting) and of precipitation in South America (across all times). Thus, climate from tens of thousands of years ago influences contemporary functional composition via slow assemblage dynamics

Patterns and drivers of plant functional group dominance across the Western Hemisphere: a macroecological re-assessment based on a massive botanical dataset

Plant functional group dominance has been linked to climate, topography and anthropogenic factors. Here, we assess existing theory linking functional group dominance patterns to their drivers by quantifying the spatial distribution of plant functional groups at a 100-km grid scale. We use a standardized plant species occurrence dataset of unprecedented size covering the entire New World. Functional group distributions were estimated from 3 648 533 standardized occurrence records for a total of 83 854 vascular plant species, extracted from the Botanical Information and Ecology Network (BIEN) database. Seven plant functional groups were considered, describing major differences in structure and function: epiphytes; climbers; ferns; herbs; shrubs; coniferous trees; and angiosperm trees. Two measures of dominance (relative number of occurrences and relative species richness) were analysed against a range of hypothesized predictors. The functional groups showed distinct geographical patterns of dominance across the New World. Temperature seasonality and annual precipitation were most frequently selected, supporting existing hypotheses for the geographical dominance of each functional group. Human influence and topography were secondarily important. Our results support the prediction that future climate change and anthropogenic pressures could shift geographical patterns in dominance of plant functional groups, with probable consequences for ecosystem functioning

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Macro-scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample-size-correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species-rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second-order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections