Iucn red list what is it

The IUCN Red List assesses the conservation status of species at a global level, drawing on expert knowledge from around the world. UN bodies, academic institutions and civil society organisations rely on the IUCN Red List to inform a range of global, regional and thematic assessments and reports. Recent examples include the:. Currently, the Index is available for five groups : birds, mammals, amphibians, corals and cycads. The RLI clearly demonstrates that the status of these five major groups continues to decline.

Read more about the science behind the assessments. The IUCN Red List aims to assess , species by and relies on donations to fund the assessment and reassessment of species. Contact us and see how you can get involved! Across all taxa, agriculture had the highest average impact probability, followed by hunting and trapping and then by logging Extended Data Fig. However, this probability of impact was elevated in some locations for birds and amphibians.

For birds, the higher probabilities were seen on the islands included in our models. For amphibians, they were located on the east coast of Australia, in the dry forests of Madagascar, in Europe and in North America, the latter being consistent with observational data on recorded chytrid outbreaks Our approach is also able to highlight where knowledge gaps about species distributions and threats most influence the certainty of our predictions by including the proportion of Data Deficient species in our analyses.

While not a perfect proxy for knowledge certainty, the proportion of Data Deficient species is likely to correlate with overall certainty in knowledge in a given region. It is therefore reasonable to assume that if particular regions are less well studied, there will also be less certainty about the distribution, conservation status and threats to species in that region. We show that the largest uncertainties in the estimated impact probabilities were observed in the Congo Basin for amphibians and across the Sahara and Central Asia for birds and mammals Figs.

These regions have previously been identified as data-poor 23 , and increased sampling would probably improve both our model predictions and our understanding of threats to species in these areas.

To identify areas of priority for threat mitigation, it is necessary to combine the estimates of the probability that a threat impact occurs with information on the spatial pattern of biodiversity importance.

We therefore developed conservation risk maps for each threat by multiplying our probability of impact with species richness Extended Data Figs. For each threat and taxonomic group, we then identified hotspot areas as the top decile Fig. The colours indicate whether an area falls within a threat hotspot for one or more taxon groups. This is the number of times a pixel falls into a hotspot region for any taxon or threat, so pixels with higher values fall in the 90th percentile for many taxonomic groups and threats.

Hotspots of the highest risk from agriculture, hunting and trapping, and logging were primarily located in the tropics. Conversely, hotspots of risk from pollution were found in Europe, driven by impacts on amphibians and mammals Fig. Except for the Australian east coast, invasive species risk hotspots showed distinct patterns for the three taxa. Amphibians and mammals were particularly threatened in different parts of the New World and Europe, while hotspots of risk for birds were found on islands consistent with existing syntheses 7 , 8 , 24 , along coastal areas and across eastern and southern Africa.

Existing global threat maps estimate the extent of pressures or changes to the natural world such as land use, human settlements and infrastructure 11 , 12 , These maps capture the intensity of some of the most important human pressures on the environment, but they do not measure how these drivers and processes affect species and habitats 13 and do not include all of the most important threats to biodiversity 5 , 26 , Our method, based on Red List data on threats to thousands of species, provides valuable complementary information.

To assess the difference between maps based on drivers and processes and maps based on impacts on species, we compared our maps of impact probability with the latest version of the Human Footprint We first created a new composite land-use impact probability layer as the mean of agriculture and logging for each pixel, to better compare with the land-use component of the Human Footprint.

We found a weak positive relationship between our measure of probability of impact from land use and the Human Footprint Fig. However, there were discrepancies, with the Human Footprint generally showing lower pressures from land use in wilderness areas and higher pressures in urbanized areas compared with our impact probability map Fig. This divergence was even more pronounced for hunting and trapping, with Human Footprint values relatively low across most of the tropical forests, where our maps suggest high impacts from hunting and trapping Fig.

The largest discrepancy was with climate change, for which some areas especially in the Arctic show a low Human Footprint but high impacts from climate change Fig. Our results indicate that current global descriptions of pressure potentially underestimate the impact of human threats to biodiversity, particularly in the most pristine areas that are likely to be of high importance for nature conservation 28 , 29 , However, given the constraints associated with the species-based threat assessment used in the Red List, it is also plausible that our approach could overestimate the probability of impact for areas that, in reality, have low levels of threat and might serve as refugia for species.

Our findings thus suggest that multiple approaches are needed, traversing drivers, processes and effects to better understand the multifaceted nature of human pressures on biodiversity. Additionally, while our threat maps represent the impacts on extant species due to threats from human drivers, they omit impacts from pressures that have already led to extirpations or extinctions.

For example, in Europe, where a large part of the original fauna has already been lost 31 , maps of accumulated drivers such as the Human Footprint might better represent the true extent of human impacts than the response of the remaining species to current threatening factors The grey lines indicate a linear relationship.

Our approach helps address important data and knowledge gaps in more direct measures of threats based on field assessments by using a globally consistent, robust, and high-quality dataset 16 , 33 , For hunting and trapping, pollution, and invasive species, all of which are implicated in dramatic population declines of native species around the world 7 , 35 , 36 , 37 , our approach provides in some instances the only way of mapping their impacts on biodiversity at regional to global scales 9.

Even for threats for which remotely sensed maps of human activity exist for example, agriculture and forest loss , our maps add additional information on where species seem to be adversely impacted by these activities. Regional analyses have also included information about species distributions to account for where threats are likely to affect most individuals 38 , but while valuable, such analyses still assume that threats are uniformly likely across the species range.

Our results show that patterns of impact often differ from patterns of occurrence of threatening processes or the number of species affected by a given threat Additionally, the effect of a threat varies with the specific context, so the same intensity of a threatening process can have different impacts in different places or on different species.

For example, forest loss affects a larger proportion of species in Southeast Asia, where little primary forest is left, than in the Amazon, where substantial forest remains despite high rates of loss in both places Our analysis and maps do not cover any plant or invertebrate groups, many of which are severely impacted by multiple threats 42 , 43 , 44 and whose hotspots of diversity and conservation importance do not always overlap with those of terrestrial vertebrates Our work is also limited in terms of its representation of freshwater taxa.

Additionally, our threat representation estimates the probability of a random species in a given location being affected by a threat. While we believe that this is closer to measuring the impact than mapping the drivers of threats, it does not capture the severity of the impact Thus, while our maps show that invasive alien species are not affecting very large numbers of species overall, the native species affected are often undergoing rapid population declines as a consequence 8 , 47 , 48 , particularly on many oceanic islands 7 , 8 , We acknowledge that it is possible that expert predispositions may influence assessments of some threats to some species on the Red List.

However, the Red List assessment process is explicitly designed to mitigate this risk by ensuring that assessments are grounded in evidence from peer-reviewed and other vetted sources, properly documented, applied in a consistent fashion and subjected to independent review see the Supplementary Information for a full description of the Red List assessment process. The current biodiversity crisis derives from current levels of action and resources being insufficient or misaligned to mitigate and reverse the increase in human pressures on the environment 3 , Thus, while the ultimate objective of conservation is to preserve biodiversity, understanding and addressing threats to nature is essential to ensure that action is targeted at the places most in need.

Our maps provide an important step towards a more complete understanding of the distribution and impact of threats. However, this does not suggest that these maps cannot be improved.

Indeed, a key strength of our approach is that it demonstrates a new way forward. The maps can help stimulate and inform models of how biodiversity is currently being impacted by a broader range of human activities than is typically considered. They can also help inform future red-listing assessments by providing a more systematic understanding of potential threats within the ranges of focal species. Additionally, other sources of data on anthropogenic pressures on biodiversity such as from acoustic monitoring, camera traps, drones and satellite imagery will be critical to help augment and improve our maps and develop more robust statistical synthesis of the impacts of threats to biodiversity.

There is substantial potential for these maps to drive conservation science and policy. However, given the resolution of the maps and the precision of the underlying data, we caution against using these maps to guide local conservation action. Their value is in illuminating global patterns and demonstrating an approach to mapping threat impacts as well as informing decisions within the context of international policy processes such as the Convention on Biological Diversity and Sustainable Development Goals, recognizing that understanding where different threats impact terrestrial vertebrate species is essential for designing effective conservation responses In Red List assessments, assessors assign those threats that impact the species.

We designed our analytical framework with three considerations in mind. First, the threat location information is limited: for each species, the data only describe whether a species is threatened by a given activity anywhere within its range data on the timing, scope and severity of threats are available only for birds and are not spatially explicit.

Second, we wanted to compare the spatial patterns of threat against independent data on spatial distributions of human activities. Third, for many activities, the relationship between human activity for example, hunting or invasive species and diseases and biodiversity response is poorly understood. We therefore chose not to incorporate known patterns of human activity as explanatory variables in our models. In the absence of global datasets on the spatial patterns of the impact probability of each threat, we used a simulation approach to develop our models and assess the ability of different model parameterizations to reproduce our simulated threat.

This process had four steps Extended Data Fig. First, we simulated a continuous synthetic threat across sub-Saharan Africa. To test this, we generated a set of synthetic, continuous surfaces of threat intensity with different levels of spatial autocorrelation and random variation Supplementary Fig.

Threat intensity was modelled as a vector of random variables, Z , one for each pixel i , generated with a correlation structure given by the distance matrix between points weighted by a scalar value, r , indicating the degree of correlation equations 1 — 3.

The model included the following equations:. We chose the Afrotropic biogeographic realm sub-Saharan Africa as our geography within which to develop the modelling approach because it permitted more rapid iterations than a global-scale simulation while also retaining characteristics of importance for the model evaluation such as strong environmental gradients and heterogeneity in species richness. However, for the simulation, no information from the geography or overlapping species ranges was used, except the spatial configuration of the polygons.

Thus, the use of the Afrotropic realm was purely to avoid generating thousands of complex geometries for the purpose of the simulation. Using a real geography and actual species ranges ensures that our simulation contains conditions that are observed in reality for example, areas of high and low species richness also observed in the real world. Second, we wanted to simulate the red-listing process whereby experts evaluate whether a threat is impacting a species on the basis of the overall threat intensity within its range.

We assumed that the binary assessment of threat for a species is based on whether the level of impact across a proportion of its range is judged as significant. This step was intended to replicate the real red-listing process, where assessors define threats that impact the species on the basis of an assessment of the information available on threatening mechanisms and species responses. In practice, this was done by overlaying the real range maps for mammals over the four simulated threat surfaces and assessing the intensity of synthetic threat within each species range map.

We wanted to assign species impacts considering that species will be more likely to be impacted if a greater part of their range has a high threat intensity. Understanding how to set a threshold for what intensity would constitute sufficient threat to be assessed as affected is a complicated exercise.

We thus tested three thresholds to capture different assumptions. These thresholds were chosen after discussion with leading experts on the red-listing process. More specifically, we calculated the 25th, 50th and 75th percentiles of threat intensity across pixels within the species range.

We then used a stochastic test to convert these quantiles to binary threat class, C. For each species, we produced a set of ten draws from a uniform distribution bounded by 0 and 1. If over half of the draws were lower than the threat intensity quantile, the species was classified as threatened for that percentile. The above simulation assumes perfect knowledge of the threat intensities across the species range, which might not always be the case in the actual red-listing process.

In real life, certain areas within species ranges are less well known for a suite of different reasons. This layer was calculated as the proportion of species present in a given location that are categorized as Data Deficient—in other words, there is insufficient information known about the species to assess its extinction risk using the IUCN Red List Criteria Extended Data Fig.

Then, when calculating the 25th, 50th and 75th percentiles of threat intensity across each range, we weighted this calculation by one minus the proportion of Data Deficient species, so that more uncertain places those with a greater proportion of Data Deficient species contributed less to the calculation than locations where knowledge was more certain. This step produced, for each species, a threat classification analogous to the threat classification assigned by experts as part of the IUCN Red List process.

Six sets of threat classifications were produced for each synthetic threat surface, on the basis of the 25th, 50th and 75th percentiles with perfect C 0. Third, using all species polygons with assigned threat assessments from step 2 that is, affected or not affected , we fitted nine candidate models and predicted the estimated probability of impact for each grid cell. Then, in a fourth step, we compared the predicted probabilities of impact produced in step 3 with the original synthetic threat maps created in step 1 to test the predictive ability of our models.

The Red List threat assessment does not contain information on where in the range the impact occurs. Therefore, a species with a very small range provides higher spatial precision about the location of the impact, whereas a species with a large range may be impacted anywhere within a wide region.

To address this lack of precision in the impact location, we took the area of each species range to serve as a proxy for the spatial certainty of the impact information. The certainty that a species was impacted or not impacted in a given cell depended on its range size, R. The models we evaluated therefore incorporated R in different ways Supplementary Table 1. The models were fitted as a binomial regression with a logit link function.

For each pixel, the model predicts the probability of impact, P Th —in other words, the probability that if you sampled a species at random from those that occur in that pixel, the species would be impacted by the activity being considered. To account for uncertainties in the simulation of the threat assessment process thresholds for impact and perfect or imperfect knowledge , models were fitted to the six sets of threat codes C 0.

For each simulation, we ranked the different models according to their model fit as measured by the RMSE. We assessed these ranks across all simulations and sets of threat codes.

We evaluated the models on the basis of the ranks of RMSE, across the threat code sets and threat intensity maps. Rank distributions for each model are shown in Extended Data Fig. However, some models had greater predictive accuracy when evaluated using the RMSE.

The top four ranking models were, in order of decreasing summed rank, 1 inverse of cube root of range size as a weight, 2 inverse 2. The fact that these four models showed good model fit suggests that the best model structure had a measure of range size as a weight but that the model was not particularly sensitive to the transformation of range size.

The model was concordant across the set of simulated datasets with a relationship that was predominantly linear with r 2 between 0. The choice of the inverse cube root range size weight was based on the performance of this against eight other model types Supplementary Fig. We conducted a decomposition of variance in model performance using a binomial regression model, with RMSE as the dependent variable and model type, knowledge level and autocorrelation structure as the independent factorial variables.

This showed that knowledge about the threats underlying each species range and how that threat information is used in the assessment explained the vast majority For birds, further information on the scope of the threat was available as an ordinal variable describing the fraction of range that the threat covers.

We explored the use of scope in our models but concluded that to avoid arbitrary decisions about the scope of non-threatened species where they are either not threatened anywhere or threatened in only a small part of their range , and for consistency with other taxonomic groups, we would model birds using the same model structure as used for mammals and amphibians see the Supplementary Methods for further details.

Once the best-performing model was identified using the simulated data, we then used this model on the actual Red List threat and range data to develop threat maps.

This model produced threat maps for each taxonomic group amphibians, birds and mammals of the probability of impact, P Th , for each individual threat.

For a given pixel, threat and taxonomic group, this estimates the probability that a randomly sampled species with a range overlapping with that pixel is being impacted by the threat, while taking into account spatial imprecision in the Red List data. For each pixel and taxonomic group amphibians, birds and mammals independently, we then modelled the probability of impact, P Th,Activity for example, P Th,Logging for logging, P Th,Agriculture for agriculture or P Th,Pollution for pollution , for each of the six threats: agriculture, hunting and trapping, logging, pollution, invasive species and diseases, and climate change.

We focused on these as the six main threats as defined by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services 4 , but our methodological framework is flexible and could be expanded to other threats in the IUCN classification IUCN and the Red List Partnership are actively working to expand the taxonomic coverage of the RLI in particular for plants and invertebrates and its representivity in marine and freshwater ecosystems, and have set out an ambitious strategy to achieve this.

As a contribution towards this a sampled approach to Red Listing and the production of RLIs has been developed for taxonomic groups with many species, with baseline RLI values available for reptiles, dragonflies, fishes, monocot plants, dicot plants, ferns and their relatives, and mosses. The graphs and datasets for the latest global, regional, national and thematic RLIs are available via the Advanced Search filters select Red List Indices.

More publications about the Red List Index in addition to the ones cited above are available in the Resources and Publications section search for "Red List Index". Please enter your e-mail address and password below. To save searches and access a historical view of information you have downloaded you are required to register for an account. Assessment Process Page navigation.

The Red List Index RLI shows trends in overall extinction risk for species, and is used by governments to track their progress towards targets for reducing biodiversity loss. Email address. Remember me. Log in. Forgot your password? Register for an account To save searches and access a historical view of information you have downloaded you are required to register for an account.