Demographic analysis of an Israeli Carpobrotus population.

Carpobrotus species are harmful invaders to coastal areas throughout the world, particularly in Mediterranean habitats. Demographic models are ideally suited to identify and understand population processes and stages in the life cycle of the species that could be most effectively targeted with management. However, parameterizing these models has been limited by the difficulty in accessing the cliff-side locations where its populations are typically found, as well as accurately measuring the growth and spread of individuals, which form large, dense mats. This study uses small unmanned aerial vehicles (drones) to collect demographic data and parameterize an Integral Projection Model of an Israeli Carpobrotus population. We validated our data set with ground targets of known size. Through the analysis of asymptotic growth rates and population sensitivities and elasticities, we demonstrate that the population at the study site is demographically stable, and that reducing the survival and growth of the largest individuals would have the greatest effect on reducing overall population growth rate. Our results provide a first evaluation of the demography of Carpobrotus, a species of conservation and economic concern, and provide the first structured population model of a representative of the Aizoaceae family, thus contributing to our global knowledge on plant population dynamics. In addition, we demonstrate the advantages of using drones for collecting demographic data in understudied habitats such as coastal ecosystems.

The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: A global analysis.

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development and reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute <1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Herbaceous perennial plants with short generation time have stronger responses to climate anomalies than those with longer generation time.

There is an urgent need to synthesize the state of our knowledge on plant responses to climate. The availability of open-access data provide opportunities to examine quantitative generalizations regarding which biomes and species are most responsive to climate drivers. Here, we synthesize time series of structured population models from 162 populations of 62 plants, mostly herbaceous species from temperate biomes, to link plant population growth rates (λ) to precipitation and temperature drivers. We expect: (1) more pronounced demographic responses to precipitation than temperature, especially in arid biomes; and (2) a higher climate sensitivity in short-lived rather than long-lived species. We find that precipitation anomalies have a nearly three-fold larger effect on λ than temperature. Species with shorter generation time have much stronger absolute responses to climate anomalies. We conclude that key species-level traits can predict plant population responses to climate, and discuss the relevance of this generalization for conservation planning.

Phylogenetic and functional distinctiveness explain alien plant population responses to competition.

Several invasion hypotheses predict a positive association between phylogenetic and functional distinctiveness of aliens and their performance, leading to the idea that distinct aliens compete less with their resident communities. However, synthetic pattern relationships between distinctiveness and alien performance and direct tests of competition as the driving mechanism have not been forthcoming. This is likely because different patterns are observed at different spatial grains, because functional trait and phylogenetic information are often incomplete, and because of the need for competition experiments that measure demographic responses across a variety of alien species that vary in their distinctiveness. We conduct a competitor removal experiment and parameterize matrix population and integral projection models for 14 alien plant species. More novel aliens compete less strongly with co-occurring species in their community, but these results dissipate at a larger spatial grain of investigation. Further, we find that functional traits used in conjunction with phylogeny improve our ability to explain competitive responses. Our investigation shows that competition is an important mechanism underlying the differential success of alien species.

Population projection models for 14 alien plant species in the presence and absence of aboveground competition.

Plant population ecologists strive to understand how environmental drivers influence demographic vital rates and thus population dynamics. Hundreds of studies have collected demographic data and used matrix and/or integral projection models to quantify lifetime fitness and population dynamics of plants. However, most of these studies have focused on native plant species, and there is a need for more studies on alien plants. Further, few studies on alien plants have experimentally manipulated environmental drivers in order to understand the mechanisms that allow alien plant species to have positive population growth. A synthetic understanding of the population dynamics of alien plant species will only be achieved if ecologists collect demographic data on many plant species and environments and provide the demographic data and model structure in a data archive for future comparisons and meta-analyses. Invasive alien species are a social, economic, and ecological issue that has become increasingly important in an increasingly globalized world. Researchers continue to forecast impacts and prevent new introductions by seeking a robust understanding of drivers of invasive species success and failure. Researchers have hypothesized that competitive differences may play a key role in determining alien species success in novel environments. Studies that experimentally manipulate competitors while quantifying demography provide mechanistic insight into species' responses to competition. To date, nearly all field manipulations of competition that measure plant demography and population dynamics have focused on native plant species. The data we provide here aim to address this gap in our knowledge for alien plant species. We present raw data and population-projection models for 14 alien plant species in eastern Missouri, USA. We sampled under ambient conditions and with all individuals of nonfocal species removed from the community, allowing us to project population dynamics in the presence and absence of competition. We have also included the data quantifying how much biomass we removed at the plot level during each removal procedure and data from our germination experiment. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data.

spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.