Clive Errol Lord

Aim Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses.InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed.Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

The number of naturalised and invasive woody plant species has increased rapidly in recent decades. Despite the increasing interest in tree and shrub invasions, little is known about the invasion ecology of most species. This paper explores the global movement of species in the genus Melaleuca (Myrtaceae; here including the genus Callistemon). We assess the global introduction history, distribution and biogeographic status of the genus. Various global species occurrence databases, citizen science (iNaturalist), and the literature were used.Seventy-two species [out of 386 Melaleuca species; 19%] have been introduced to at least 125 regions outside their native range. The main regions of global Melaleuca introductions are Southeast Asia, the southern parts of North America, south-eastern South America, southern Africa and Europe. The earliest record of a Melaleuca species outside of the native range of the genus is 1789. First records of Melaleuca species outside their native range were most commonly recorded in the 1960s, with records from all over the world. The main reasons for Melaleuca introductions were for use in the tea tree (pharmaceutical value) and ornamental horticulture industries. Melaleuca introductions, naturalizations and invasions are recent compared to many other woody plant taxa. Experiences in Florida and South Africa highlight the potential of Melaleuca species to spread rapidly and have significant ecological impacts. It is likely that the accumulating invasion debt will result in further naturalization and invasion of Melaleuca species in the future.

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.

Loading...