Forecasting the impacts of changing climate on the phenology of plant populations is essential for anticipating and managing potential ecological disruptions to biotic communities. Herbarium specimens enable assessments of plant phenology across broad spatiotemporal scales. However, specimens are collected opportunistically, and it is unclear whether their collection dates—used as proxies of phenology—are closest to the onset, peak, or termination of a phenophase, or whether sampled individuals represent early, average, or late occurrences in their populations. Despite this, no studies have assessed whether these uncertainties limit the utility of herbarium specimens for estimating the onset and termination of a phenophase. Using simulated data mimicking such uncertainties, we evaluated the accuracy with which the onset and termination of population-level phenological displays (in this case, of flowering) can be predicted from natural-history collections data (in the absence of other biases not evaluated here), and how attributes of the flowering period of a species and temporal collection biases influence model accuracy. Estimates of population-level onset and termination were highly accurate for a wide range of simulated species’ attributes, but accuracy declined among species with longer individual-level flowering duration and when there were temporal biases in sample collection, as is common among the earliest and latest-flowering species. The amount of data required to model population-level phenological displays is not impractical to obtain; model accuracy declined by less than 1 day as sample sizes rose from 300 to 1000 specimens. Our analyses of simulated data indicate that, absent pervasive biases in collection and if the climate conditions that affect phenological timing are correctly identified, then specimen data can predict the onset, termination, and duration of a population’s flowering period with similar accuracy to estimates of median flowering time that are commonplace in the literature.

Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species - referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data in Norway. To assess the differences between these datasets, we quantified a Community Temperature Index (CTI) from the co-occurrence data set and compared this with CTI obtained from presence-only data. We also examined the temporal trend in CTI (i.e., thermophilization) in both datasets. To do this, we first established a species-temperature relationship based on data before climate warming. In a preliminary analysis, we assessed the performance of this relationship using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing more species niches), and 3) a combined dataset merging 1) and 2). The transfer function including both datasets performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values in comparison to co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses, our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.

Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e., morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 31 species of birds (1,801 total genetic samples, 526 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variations within the species range.

Our knowledge of biodiversity hinges on sufficient data, reliable methods, and realistic models. Without an accurate assessment of species distributions, we cannot effectively target and stem biodiversity loss. Species range maps are the foundation of such efforts, but countless studies have failed to account for the most basic assumptions of reliable species mapping practices, undermining the credibility of their results and potentially misleading and hindering conservation and management efforts. Here, we use examples from the recent literature and broader conservation community to highlight the substantial shortfalls in current practices and their consequences for both analyses and conservation management. We detail how different decisions on data filtering impact the outcomes of analysis and provide practical recommendations and steps for more reliable analysis, whilst understanding the limits of what available data will reliably allow and what methods are most appropriate. Whilst “perfect” analyses are not possible for many taxa given limited data, and biases, ensuring we use data within reasonable limits and understanding inherent assumptions is crucial to ensure appropriate use. By embracing and enacting such best practices, we can ensure both the accuracy and improved comparability of biodiversity analyses going forward, ultimately enhancing our ability to use data to facilitate our protection of the natural world.

Climate change affects biodiversity in diverse ways, necessitating the exploration of multiple climate dimensions using standardized metrics. However, existing methods for quantifying these metrics are scattered and tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed “climetrics” which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are currently implemented, including 1) Standardized Local Anomalies; 2) Changes in Probabilities of Local Climate Extremes; 3) Changes in Areas of Analogous Climates; 4) Novel Climates; 5) Changes in Distances to Analogous Climates; and 6) Climate Change Velocity. For climate change velocity, three different algorithms are implemented and available within the package including; a) Distanced-based Velocity (“dVe”); b) Threshold-based Velocity (“ve”); and c) Gradient-based Velocity (“gVe”). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The climetrics R package is seamlessly integrated with the rts package for efficient handling of raster time-series data. The functions in climetrics are designed to be user-friendly, making them suitable for less-experienced R users. Detailed comments and descriptions in their help pages and vignettes of the package facilitate further customization by advanced users. In summary, the climetrics R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

A document by Martha Zillig. Click on the document to view its contents.

Climatic changes can affect species distributions, population abundance, and evolution. Such organismal responses could be determined by the amount and quality of available habitats, which can vary independently. In this study, we assessed changes in habitat quantity and quality independently to generate explicit predictions of the species’ responses since the Last Glacial Maximum (LGM) climatic changes. We built ecological niche models and distribution models for 21 reptile, mammal, and plant taxa from the Baja California peninsula inhabiting lowland or highland environments. Geological data suggests the CCSM global circulation model is a better representation of LGM climate for the Baja California peninsula. Significant niche divergence was detected for all clades within species, along with significant differences in niche breadth and area of distribution between northern and southern clades. Most clades showed a reduction in distribution area towards LGM. Further, niche marginality (used as a measure of habitat quality) was higher during LGM for most clades, except for northern highland species who experienced improvements in both. Our results suggest that changes in habitat quantity and quality can affect organismal response independently. This allows the prediction of genomic signatures associated with changes in effective population size and selection pressure that could be explicitly tested to support our models.