Subpopulations,locations and fragmentation: applying IUCN red list criteria to herbarium specimen data

Despite the ecological and economic importance of plants, the majority of plant species and their conservation status are still poorly known. Based on the limited knowledge we have of many plant species, especially those in the tropics, the use of GIS techniques can give us estimates of the degree of population subdivision to be used in conservation assessments of extinction risk. This paper evaluates how best to use the IUCN Red List Categories and Criteria to produce effective and consistent estimates of subpopulation structure based on specimen data available in the herbaria around the world. We assessed population structure through GIS-based analysis of the geographic distribution of collections, using herbarium specimen data for 11 species of Delonix sensu lato. We used four methods: grid adjacency, circular buffer, Rapoport’s mean propinquity and alpha hull, to quantify population structure according to the terms used in the IUCN Red List: numbers of subpopulations and locations, and degree of fragmentation. Based on our findings, we recommend using the circular buffer method, as it is not dependent on collection density and allows points to be added, subtracted and/or moved without altering the buffer placement. The ideal radius of the buffer is debatable; however when dispersal characteristics of the species are unknown then a sliding scale, such as the 1/10th maximum inter-point distance, is the preferred choice, as it is species-specific and not sensitive to collection density. Such quantitative measures of population structure provide a rigorous means of applying IUCN criteria to a wide range of plant species that hitherto were inaccessible to IUCN classification.     

A new method to estimate areas of occupancy using herbarium data

The World Conservation Union (IUCN) Red List of Threatened Species is an important instrument to evaluate the conservation status of living organisms. However, Red List assessors have been limited by the lack of reliable methods to calculate the area of occupancy (AOO) of species, which is an important parameter for red list assessments. Here we present a new practical method to estimate AOO based on herbarium specimen data: the Cartographic method by Conglomerates (CMC). This method, which combines elements from the Areographic and Cartographic methods previously used to calculate AOO, was tested with ten cactus species from the Chihuahuan Desert Region. The results derived from this novel procedure produced in average AOO calculations 3.5 and 5.5 smaller than the Areographic and Cartographic methods, respectively. The CMC takes into account the existence of disjunctions in the distribution range of the species, producing comparatively more accurate AOO estimations. Another advantage of the CMC is that it generates results more harmonic with the current Red List criteria. In contrast, the overestimated results of the Areographic and Cartographic methods tend to artificially categorize the species, even extremely narrow endemics, in lower endangerment status.     

Scale dependency and area of occupancy: Tortula freibergii in north-west England

Abstract

Area of occupancy (AOO) is used commonly as a measure of species range size and the IUCN Red List Criteria provide thresholds of AOO for determining the extinction risk of species. Tortula freibergii is a rare moss globally and is considered to be a priority for conservation in the UK. This study provides the results of a comprehensive survey of the distribution of the species in north-west England, at a linear resolution of 0.5 km, which shows it to be considerably more widespread than understood previously. The data are used to create a 'species-area curve' that spans the scales of 0.1 to 10 km and examine how measures of AOO change according to the scale of measurement. AOO values declined sharply as the scale of measurement reduced, as a result of the linear and frequently fragmented distribution of the species. Implications for the application of the IUCN Red List Criteria and the monitoring of species are discussed.     

Using species distribution models for IUCN Red Lists of threatened species

Red Lists have been used for years globally and regionally in many countries to highlight species that need special attention because of the rarity or rapid decline of their populations. To ensure homogenized classification at the global and regional level, the International Union for Conservation of Nature (IUCN) defined categories of threat, and criteria to attribute the taxa to these categories. Nevertheless, the strict application of the criteria is not always straightforward, especially for invertebrates, because of the difficulties associated with precise estimates of the size and viability of their populations. This paper presents a method for the estimation of extent of occurrence (EOO) and area of occupancy (AOO) based on species distribution models using multivariate adaptive regression splines. To achieve this, presence data have been modeled against topographical and climatic explanatory variables. Predictions from the statistical distribution models have then been cut using the minimal convex hull around (EOO) or the watersheds in which (AOO) the species have really been observed in recent years. This allows us to delimit the EOO and AOO according to the IUCN criteria, and better take into account the ecological requirements of the species. Furthermore, the method allows for the use of historical data (e.g. from museum’s collections) and the direct comparison of historical and recent distributions of species. The method has been tested on six species of butterflies. The results show the possibility of using species distribution models to define the Red Lists status according to the IUCN guidelines, and shows that the results are consistent with previous Red Lists assessments.     

ConR: An R package to assist large‐scale multispecies preliminary conservation assessments using distribution data

The Red List Categories and the accompanying five criteria developed by the International Union for Conservation of Nature (IUCN) provide an authoritative and comprehensive methodology to assess the conservation status of organisms. Red List criterion B, which principally uses distribution data, is the most widely used to assess conservation status, particularly of plant species. No software package has previously been available to perform large‐scale multispecies calculations of the three main criterion B parameters [extent of occurrence (EOO), area of occupancy (AOO) and an estimate of the number of locations] and provide preliminary conservation assessments using an automated batch process. We developed ConR, a dedicated R package, as a rapid and efficient tool to conduct large numbers of preliminary assessments, thereby facilitating complete Red List assessment. ConR (1) calculates key geographic range parameters (AOO and EOO) and estimates the number of locations sensu IUCN needed for an assessment under criterion B; (2) uses this information in a batch process to generate preliminary assessments of multiple species; (3) summarize the parameters and preliminary assessments in a spreadsheet; and (4) provides a visualization of the results by generating maps suitable for the submission of full assessments to the IUCN Red List. ConR can be used for any living organism for which reliable georeferenced distribution data are available. As distributional data for taxa become increasingly available via large open access datasets, ConR provides a novel, timely tool to guide and accelerate the work of the conservation and taxonomic communities by enabling practitioners to conduct preliminary assessments simultaneously for hundreds or even thousands of species in an efficient and time‐saving way.     

Supporting Red List threat assessments with GeoCAT: geospatial conservation assessment tool

GeoCAT is an open source, browser based tool that performs rapid geospatial analysis to ease the process of Red Listing taxa. Developed to utilise spatially referenced primary occurrence data, the analysis focuses on two aspects of the geographic range of a taxon: the extent of occurrence (EOO) and the area of occupancy (AOO). These metrics form part of the IUCN Red List categories and criteria and have often proved challenging to obtain in an accurate, consistent and repeatable way. Within a familiar Google Maps environment, GeoCAT users can quickly and easily combine data from multiple sources such as GBIF, Flickr and Scratchpads as well as user generated occurrence data. Analysis is done with the click of a button and is visualised instantly, providing an indication of the Red List threat rating, subject to meeting the full requirements of the criteria. Outputs including the results, data and parameters used for analysis are stored in a GeoCAT file that can be easily reloaded or shared with collaborators. GeoCAT is a first step toward automating the data handling process of Red List assessing and provides a valuable hub from which further developments and enhancements can be spawned.     

Redlistr: tools for the IUCN Red Lists of ecosystems and threatened species in R

The International Union for the Conservation of Nature (IUCN) Red List of ecosystems and Red List of threatened species are global standards for assessing risks of ecosystem collapse and species extinction. However, misconceptions of the Red List assessment process, along with its technically demanding nature, can result in the misapplication of their criteria, leading to inconsistent and potentially unreliable assessments. To address this problem, we developed redlistr, an R package aiding in the production of consistent species and ecosystem Red List assessments. Redlistr's features include methods to calculate 1) area from spatial data, 2) range size metrics, 3) rates of change of distributions or populations, and 4) distribution or population at another time from these rates. A key feature of the package is the systematic approach used to eliminate geometric uncertainty when estimating area of occupancy. Here, we develop two case studies to demonstrate the functionalities of redlistr with typical workflows for both species and ecosystems. Redlistr was developed to be accessible to users with a broad range of experience in programming for spatial and temporal data analysis, and sufficiently flexible to allow users to parameterise functions and select equations to fit their purposes. The package specifically aims to assist researchers and conservation practitioners to conduct robust and transparent risk assessments of ecosystems and species under the IUCN Red List criteria but is also useful for other studies requiring analyses of range size, area change and calculations of rates of change.     

Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity

Recent attempts at projecting climate change impacts on biodiversity have used the IUCN Red List Criteria to obtain estimates of extinction rates based on projected range shifts. In these studies, the Criteria are often misapplied, potentially introducing substantial bias and uncertainty. These misapplications include arbitrary changes to temporal and spatial scales; confusion of the spatial variables; and assume a linear relationship between abundance and range area. Using the IUCN Red List Criteria to identify which species are threatened by climate change presents special problems and uncertainties, especially for shorter‐lived species. Responses of most species to future climate change are not understood well enough to estimate extinction risks based solely on climate change scenarios and projections of shifts and/or reductions in range areas. One way to further such understanding would be to analyze the interactions among habitat shifts, landscape structure and demography for a number of species, using a combination of models. Evaluating the patterns in the results might allow the development of guidelines for assigning species to threat categories, based on a combination of life history parameters, characteristics of the landscapes in which they live, and projected range changes.     

IUCN受威胁物种红色名录进展及应用

IUCN受威胁物种红色名录已经成为世界上最全面的关于全球动物、真菌和植物物种灭绝风险状况的信息来源, 是生物多样性健康的关键指标, 是促进生物多样性保护和决策的有力工具。本文全面介绍IUCN受威胁物种红色名录(简称IUCN红色名录)的发展以及应用状况, 积极推动其在中国的物种评估和广泛应用。总结了IUCN红色名录从依赖于评估专家的主观意志决定物种濒危等级的濒危物种红皮书(Red Data Book)到IUCN受威胁物种等级和标准(3.1版)的客观量化和所有门类使用统一标准的过程。该等级体系可囊括全球所有物种, 其中“受威胁”的3个等级——极危(CR)、濒危(EN)或易危(VU)需使用5个标准进行量化评估, 对评估规范有非常严格的要求。该等级和标准体系不仅适用于全球层面, 同样也适用于地区层面物种评估, 只是在具体物种种群如果和周边其他地区(国家)存在种群交流情况时, 评估结果要进行调整。迄今为止, 全球层面使用该等级体系和标准评估了14万多种(其中在中国有分布的物种10,846种), 100多个国家和地方制定了地区/国家层面的红色名录, 中国红色名录评估了5.5万多种。IUCN红色名录已广泛应用于评估生物多样性变化速度; 为保护规划提供决策信息; 支持履行国际公约、修订国家/地区重点保护物种名录和自然保护地管理等; 指导资源有效合理分配和宣传教育等。广泛应用过程中, 讨论主要集中在获取数据的方法改进上; 另外, 一方面有专家认为标准存在缺陷需要完善, 另一方面有呼吁维持标准的长期相对稳定, 以便进行跨时间、跨区域、跨物种门类的比较。本文提出来了中国红色名录的持续机制和应用建议, 包括建立中国红色名录委员会、建立中国红色名录专业网站、发展评估专家队伍、建立中国红色名录评估更新机制, 以及加强国际协作、促进全球和中国红色名录的应用和发展。     

The use of spatial ecological modelling as a tool for improving the assessment of geographic range size of threatened species

We analysed endemic threatened tree and reptile species of Socotra Island (Yemen), characterised by different ecological requirements and spatial distribution, in order to evaluate the usefulness of spatial ecological modelling in the estimation of species extent of occurrence (EOO) and area of occupancy (AOO). Point occurrences for the entire species range were used to model their spatial distribution by Random Forest (RF) and Generalised Linear Model (GLM). For each species the suitability area (SA) was obtained by applying the 0% omission error criterion on the probability map, and compared or integrated with EOO and AOO area obtained by topological methods such as the minimum convex polygon (MCP), α-hull and 2 km × 2 km grid.RF showed a lower prediction error than GLM. Higher accuracy was achieved for species with higher number of occurrences and narrower ecological niche. SA was always greater than AOO measured with the 2 km × 2 km grid method. SA was greater than EOO, measured by both MCP and α-hull methods, for species with localised distribution, while it was smaller for widely distributed species. EOO-α-hull area was equal or smaller than that calculated by MCP depending on the spatial distribution of species. AOO measured considering the SA within the EOO-MCP was greater than that measured using the standard 2 km × 2 km grid. Conversely, AOO calculated considering the suitable area within the EOO-α-hull showed variable results, being smaller or greater than the 2 km × 2 km grid AOO depending on the ecological niche and spatial distribution of species. According to our results, SEM does not provide an effective alternative to topological methods for the estimate of EOO and AOO. However, it may be considered a useful tool to estimate AOO within the boundaries of EOO measured by the α-hull method, because it reduces some potential sources of inconsistency and bias.     

Least concern to endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria.     

At the Heart of the Industrial Boom: Australian Snubfin Dolphins in the Capricorn Coast,Queensland, Need Urgent Conservation Action

The recent industrial boom along the Australian coastline has increased concerns about the long term conservation of snubfin dolphins along the Queensland coast. National assessment of the conservation status and management of the Australian snubfin dolphin is currently hindered by the lack of adequate biological and ecological information throughout most of its range. In response to the issue of determining the conservation status of species with broad ranges, the IUCN has provided a framework for assessing the threatened status of regional populations. In this study we assessed the conservation status of a small geographically isolated population of snubfin dolphins living in the Fitzroy River region, Queensland, Australia, against the IUCN criteria for regional populations. A review of all available sightings data and stranding information indicates that this is the southernmost resident population of snubfin dolphins in Australian waters. The Fitzroy River snubfin dolphin population is composed of less than 100 individuals, with a representative range and core area of less than 400 and 300 km² respectively. The area most often used by snubfin dolphins within the representative range and core area was estimated to be about 292 and 191 km², respectively. A decrease in representative range, core area and preferred habitat between 14 and 25% is projected to occur if a planned industrial port development were to occur. These results are robust to uncertainty and considering the low level of formal protection and future threats, a classification of this subpopulation under the IUCN Red List as “Endangered” is appropriate.     

Where are threatened ferns found? Global conservation priorities for pteridophytes

Recent efforts to improve the representation of plant species included on the IUCN Red List of Threatened Species through the IUCN Sampled Red List Index (SRLI) for Plants have led to the assessment of almost 1000 additional species of pteridophytes and lycophytes under IUCN Red List criteria. Species were selected at random from all lineages of pteridophytes and lycophytes and are taxonomically as well as ecologically representative of pteridophyte and lycophyte diversity. 16% of pteridophyte and lycophyte species are globally threatened with extinction and 22% are of elevated conservation concern (threatened or Near Threatened); of species of pteridophytes and lycophytes previously included on the Red List, 54% were considered threatened. Over half of pteridophyte and lycophyte species assessed for the SRLI use estimates of range size; therefore the method used to measure range may affect the Red List category assigned. We evaluated this using two alternative metrics for estimating range, species distribution modelling (SDM) and ecologically suitable habitat (ESH), for 227 species endemic to the Neotropical biogeographic realm. Differences between range estimates were small when ranges were small but increased with increasing range size. For 58 (25.6%) species alternative modelling techniques result in the species meeting the threshold for a different IUCN Red List category from using extent of occurrence. Modelling threatened species distributions also highlights priority areas for conservation in tropical and subtropical montane forests that are the most species-rich habitat for small-range pteridophyte and lycophyte species, but which are now increasingly subject to rapid conversion to agriculture.     

Application of the IUCN Red Listing system to setting species targets for conservation planning purposes

Biodiversity targets, or estimates of the quantities of biodiversity features that should be conserved in a region, are fundamental to systematic conservation planning. We propose that targets for species should be based on the quantitative thresholds developed for the Vulnerable category of the IUCN Red List system, thereby avoiding future listings of species in an IUCN Red List threat category or an increase in the extinction risk, or ultimate extinction, of species already listed as threatened. Examples of this approach are presented for case studies from South Africa, including threatened taxa listed under the IUCN Red List criteria of A to D, a species listed as Near Threatened, a species of conservation concern due to its rarity, and one species in need of recovery. The method gives rise to multiple representation targets, an improvement on the often used single representation targets that are inadequate for long term maintenance of biodiversity or the arbitrary multiple representation and percentage targets that are sometimes adopted. Through the implementation of the resulting conservation plan, these targets will ensure that the conservation status of threatened species do not worsen over time by qualifying for higher categories of threat and may actually improve their conservation status by eliminating the threat of habitat loss and stabilizing population declines. The positive attributes ascribed to the IUCN Red List system, and therefore to the species targets arising from this approach, are important when justifying decisions that limit land uses known to be detrimental to biodiversity.     

South African mouse shrews (<Emphasis Type="Italic">Myosorex</Emphasis>) feel the heat: using species distribution models (SDMs) and IUCN Red List criteria to flag extinction risks due to climate change

Five species of mouse or forest shrews (Myosorex) are endemic to South Africa, Lesotho and Swaziland, four of which (Myosorex varius, Myosorex cafer, Myosorex longicaudatus and Myosorex cf. tenuis) are associated with montane or temperate grassland, fynbos and/or forest habitats while a fifth (Myosorex sclateri) is associated with lowland subtropical forests. Due to their small size, specialised habitat, low dispersal capacity, high metabolism and sensitivity to temperature extremes, we predicted that, particularly for montane species, future climate change should have a negative impact on area of occupancy (AOO) and ultimately extinction risks. Species distribution models (SDMs) indicated general declines in AOO of three species by 2050 under the A1b and A2 climate change scenarios (M. cafer, M. varius, M. longicaudatus) while two species (M. sclateri and M. cf. tenuis) remained unchanged (assuming no dispersal) or increased their AOO (assuming dispersal). While temperate species such as M. varius appear to be limited by temperature maxima (preferring cooler temperatures), the subtropical species M. sclateri appears to be limited by temperature minima (preferring warmer temperatures). Evidence for declines in AOO informed the uplisting (to a higher category of threat) of the Red List status of four Myosorex species to either vulnerable or endangered as part of a separate regional International Union for Conservation of Nature (IUCN) Red List assessment.     

Understanding bias in geographic range size estimates

Aim Estimates of geographic range size derived from natural history museum specimens are probably biased for many species. We aim to determine how bias in these estimates relates to range size. Location We conducted computer simulations based on herbarium specimen records from localities ranging from the southern United States to northern Argentina. Methods We used theory on the sampling distribution of the mean and variance to develop working hypotheses about how range size, defined as area of occupancy (AOO), was related to the inter‐specific distribution of: (1) mean collection effort per area across the range of a species (MC); (2) variance in collection effort per area across the range of a species (VC); and (3) proportional bias in AOO estimates (PBias: the difference between the expected value of the estimate of AOO and true AOO, divided by true AOO). We tested predictions from these hypotheses using computer simulations based on a dataset of more than 29,000 herbarium specimen records documenting occurrences of 377 plant species in the tribe Bignonieae (Bignoniaceae). Results The working hypotheses predicted that the mean of the inter‐specific distribution of MC, VC and PBias were independent of AOO, but that the respective variance and skewness decreased with increasing AOO. Computer simulations supported all but one prediction: the variance of the inter‐specific distribution of VC did not decrease with increasing AOO. Main conclusions Our results suggest that, despite an invariant mean, the dispersion and symmetry of the inter‐specific distribution of PBias decreases as AOO increases. As AOO increased, range size was less severely underestimated for a large proportion of simulated species. However, as AOO increased, range size estimates having extremely low bias were less common.     

Optimal allocation of Red List assessments to guide conservation of biodiversity in a rapidly changing world

The IUCN Red List is the most extensive source of conservation status assessments for species worldwide, but important gaps in coverage remain. Here, we demonstrate the use of a spatial prioritization approach to efficiently prioritize species assessments to achieve increased and up‐to‐date coverage efficiently. We focus on freshwater fishes, which constitute a significant portion of vertebrate diversity, although comprehensive assessments are available for only 46% of species. We used marxan to identify ecoregions for future assessments that maximize the coverage of species while accounting for anthropogenic stress. We identified a set of priority regions that would help assess one‐third (ca 4000 species) of all freshwater fishes in need of assessment by 2020. Such assessments could be achieved without increasing current investment levels. Our approach is suitable for any taxon and can help ensure that species threat assessments are sufficiently complete to guide global conservation efforts in a rapidly changing world.     

Grid origin affects scaling of species across spatial scales

Aim Distribution maps of species based on a grid are useful for investigating relationships between scale and the number or area of occupied grid cells. A species is scaled up simply by merging occupied grid cells on the observation grid to successively coarser cells. Scale–occupancy relationships (SORs) obtained in this way can be used to extrapolate species down, in other words to compute occupancies at finer scales than the observation scale. In this paper we demonstrate that the SOR is not unique but depends on where one positions the origin of the grid map. Innovation The effect of grid origin on SORs was explored with the aid of the Dutch national data base FLORBASE, which contains the observation records of all 1410 wild vascular plants in the Netherlands on a 1‐km square basis. For each species, we generated 2500 unique SORs by scaling up from 1 km, in steps of 1 km, to squares of 50 km. We computed the sensitivity of the SOR to the grid origin for each species, and subsequently analysed the factors that determined this sensitivity. The effect of grid origin on downscaling was demonstrated by means of a simple power function that we used to extrapolate down from both a 2‐km and a 5‐km grid, to the original 1‐km grid. It appeared that the position of grid origin could have a substantial effect on SORs. The sensitivity of SORs to the position of the grid origin depended on three characteristics of a species’ spatial distribution: rarity, degree of spatial clustering and the position of the distribution relative to the border of the investigated area. Rare species with a clustered distribution near the border were particularly highly sensitive. The dependence of SOR on grid origin caused unpredictable and non‐random errors in downscaled occupancies. Main conclusions In future, the whole bandwidth of scaled occupancies should be considered when testing and interpreting mathematical relationships between scale and occupancy. Moreover, downscaled occupancies should be interpreted cautiously.     

Uncertainty in predictions of range dynamics: black grouse climbing the Swiss Alps

Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species’ vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate‐induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual‐based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species’ responses to climate change.