Mary E. Pulsifer Ames

Species climate niche models (CNMs) have been widely used for assessing climate change impact, developing conservation strategies and guiding assisted migration for adaptation to future climates. However, the CNMs built based on species occurrence data only reflect the species’ realized niche, which can overestimate the potential loss of suitable habitat of existing forests and underestimate the potential of assisted migration to mitigate climate change. In this study, we explored building a fundamental climate niche model using widely available species occurrence data with two important forest tree species, lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.), which were introduced to many countries worldwide. We first compared and optimized three individual modeling techniques and their ensemble by adjusting the ratio of presence to absence (p/a) observations using an innovative approach to predict the realized climate niche of the two species. We then extended the realized climate niches to their fundamental niches by determining a new cut-off threshold based on species occurrence data beyond the native distributions. We found that the ensemble model comprising Random Forest and Maxent had the best performance and identified a common cut-off threshold of 0.3 for predicting the fundamental climate niches of the two species, which is likely applicable to other species. We then predicted the fundamental climate niches of the two species under current and future climate conditions. Our study demonstrated a novel approach for predicting species’ fundamental climate niche with high accuracy using only species occurrence data, including both presence and absence data points. It provided a new tool for assessing climate change impact on the future loss of existing forests and implementing assisted migration for better adapting to future climates.

The genus Viola (Violaceae) is among the 40–50 largest genera among angiosperms, yet its taxonomy has not been revised for nearly a century. In the most recent revision, by Wilhelm Becker in 1925, the then-known 400 species were distributed among 14 sections and numerous unranked groups. Here, we provide an updated, comprehensive classification of the genus, based on data from phylogeny, morphology, chromosome counts, and ploidy, and based on modern principles of monophyly. The revision is presented as an annotated global checklist of accepted species of Viola, an updated multigene phylogenetic network and an ITS phylogeny with denser taxon sampling, a brief summary of the taxonomic changes from Becker’s classification and their justification, a morphological binary key to the accepted subgenera, sections and subsections, and an account of each infrageneric subdivision with justifications for delimitation and rank including a description, a list of apomorphies, molecular phylogenies where possible or relevant, a distribution map, and a list of included species. We distribute the 664 species accepted by us into 2 subgenera, 31 sections, and 20 subsections. We erect one new subgenus of Viola (subg. Neoandinium, a replacement name for the illegitimate subg. Andinium), six new sections (sect. Abyssinium, sect. Himalayum, sect. Melvio, sect. Nematocaulon, sect. Spathulidium, sect. Xanthidium), and seven new subsections (subsect. Australasiaticae, subsect. Bulbosae, subsect. Clausenianae, subsect. Cleistogamae, subsect. Dispares, subsect. Formosanae, subsect. Pseudorupestres). Evolution within the genus is discussed in light of biogeography, the fossil record, morphology, and particular traits. Viola is among very few temperate and widespread genera that originated in South America. The biggest identified knowledge gaps for Viola concern the South American taxa, for which basic knowledge from phylogeny, chromosome counts, and fossil data is virtually absent. Viola has also never been subject to comprehensive anatomical study. Studies into seed anatomy and morphology are required to understand the fossil record of the genus.

Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson's r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.

Abstract. Statistical climate reconstruction techniques are fundamental tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (or PDFs) can be used in various environments and with different climate proxies because they rely on elementary calibration data (i.e. modern geolocalised presence data). However, the difficulty of accessing and curating these calibration data and the complexity of interpreting probabilistic results have often limited their use in palaeoclimatological studies. Here, I introduce a new R package (crestr) to apply the PDF-based method CREST (Climate REconstruction SofTware) on diverse palaeoecological datasets and address these problems. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) associated with an extensive range of climate variables (20 terrestrial and 19 marine variables) that enables its use in most terrestrial and marine environments. Private data collections can also be used instead of, or in combination with, the provided calibration dataset. The package includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment and thus be more easily integrated with existing workflows. It is hoped that crestr will be used to produce the much-needed quantified climate reconstructions from the many regions where they are currently lacking, despite the availability of suitable fossil records. To support this development, the use of the package is illustrated with a step-by-step replication of a 790 000-year-long mean annual temperature reconstruction based on a pollen record from southeastern Africa.

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Ferns and lycophytes are an excellent group for conservation and species distribution studies because they are closely related to environmental changes. In this study, we analyzed collection gaps, sampling biases, richness distribution, and the species conservation effectiveness of protected areas i…

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Background The knowledge of distributional dynamics of living organisms is a prerequisite for protecting biodiversity and for the sustainable use of biotic resources. Clematis sect. Fruticella s. str. is a small group of shrubby, yellow-flowered species distributed mainly in arid and semi-arid areas…