Guidelines for Using the IUCN Red List Categories and Criteria Version 15.1 (July 2022) - page 4

 

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Guidelines for Using the IUCN Red List Categories and Criteria Version 15.1 (July 2022) - page 4

 

 

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In summary, many of the issues related to time horizons are not specific to global climate change.
Although future versions of this document may provide further guidance on this issue, for the time
being, the horizons for each of the criteria should continue to be applied as they are currently
specified, regardless of the nature of the threatening factor, including global climate change.
12.1.2 Suggested steps for applying the criteria under climate change
There are a number of challenges in applying the criteria to species impacted by global climate
change, which have resulted in several misapplications of the criteria. A common mistake is
making arbitrary changes to thresholds or time horizons specified in the IUCN Red List Criteria
(see Akçakaya et al. 2006 for examples and details). An important characteristic of the Red List
is that threat categories are comparable across taxonomic groups. For this important standard to
be maintained, it is essential that the thresholds and time periods used in the criteria are not altered
(see section 12.1.1).
To assess species that might be impacted by climate change, the following steps are recommended
(Figure 12.1), as available data and information about the species permit.
1.
Assessors are encouraged to think systematically through the potential mechanisms of the
impact of climate change on the species (see section 12.1.3 below). The identification of
likely mechanisms of impact will help with defining key variables used in Red List
assessments in the context of climate change. This diagnostic process may be aided by
development of diagrammatic models.
2.
Assessors should identify and estimate or infer the values of all the parameters in the Red
List criteria relevant to the mechanisms of taxon change under climate change identified
in Step 1. These parameters include “very restricted distribution” and “plausibility and
immediacy of threat”
(section
12.1.4), "number of locations"
(12.1.5), "severely
fragmented populations" (12.1.6), "extreme fluctuations" (12.1.7), “continuing decline”
(12.1.8), and “population reductions” (12.1.8). Inferences about such variables can lead
to listing under criteria A, B, D2 or C2 (Figure 12.1).
3.
To incorporate future climate impacts on species more explicitly, assessors are encouraged
to make inferences about the magnitude of future population reduction (criteria A3 and
A4) and whether continuing decline (criteria B and C2) will occur due to climate change
(see section 12.1.8). Such inferences can be aided by developing models of (a) bioclimatic
habitat or
(b) population dynamics (see sections 12.1.9, 12.1.10, and 12.1.12). The
identification of likely mechanisms of impact will also help with developing such models.
The output of such models can lead to listings under criteria A, C1 or E (Figure 12.1).
4.
Finally, the results of the bioclimatic models can be used to determine the spatial structure
of stochastic population models, which are then used to estimate probability of extinction
for assessment under criterion E (discussed in detail in section 12.1.11). This allows
assessors to explicitly incorporate effects of future habitat shifts and habitat fragmentation,
future increases in climate variability (hence in extreme fluctuations), and dispersal
limitations and barriers. The output of such models can lead to listings under criteria A or
E (Figure 12.1). However, this approach requires substantial amounts of demographic
information that may not be available for most species.
Assessors should first complete Steps 1 and 2, and then complete as many of the remaining steps
as the available data and expertise allow. In the following sections, we discuss mechanisms of
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impact of climate change, applications of various definitions and criteria, and use of different
types of models for estimating population reductions and continuing declines. Although we
discuss particular criteria in this section, this does not mean that these are the only applicable ones.
As with any other threat, the taxon should be assessed against all the criteria as available data
permit.
Figure 12.1. Protocol for assessing extinction risks under climate change using the IUCN Red List Criteria
for threatened species (IUCN 2001). Letters and numbers in marginal boxes refer to respective Red List
Criteria. Numbers within central boxes refer to relevant sections of text in these Guidelines. Any
assessment must address all plausible threats (not just climate change), and should also evaluate eligibility
for listing under criteria A1, A2 and D1 (not shown).
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12.1.3 Mechanisms
Climate change can affect populations via many mechanisms; thinking about how this will occur
for given taxa can clarify the parameters and criteria relevant for a Red List assessment. Relevant
parameters for assessments under climate change include
“very restricted distribution”,
“plausibility & immediacy of threat”, “number of locations”, “severe fragmentation”, “continuing
decline”,
“extreme fluctuations”, and “population reductions”. The relevant criteria for future
effects of climate change include A3, A4, B1, B2, C1, C2, D2 (VU), and E (Figure 12.1).
The effects of climate change on taxa are analysed quantitatively through two main groups of
symptoms: changes in the taxon’s distribution and changes in the demography of the taxon which
is then included in population models. While range changes have been the most studied symptom
of species decline due to climate change (Pearson et al. 2002), changes in demography can also
lead to reductions in population abundance even when species distributions are projected to
increase under climate change. This is because births, deaths, emigration and immigration drive
population dynamics and these are demographic factors not necessarily directly linked to habitat
and range size (Thuiller et al. 2014). Demographic factors that could be affected by climate change
include vital rates
(e.g., survival, growth, fecundity, and dispersal), species interactions,
phenology, population responses to disturbance, and deposition and production of calcareous
structures and tissues (e.g., in corals) (Foden et al. 2013). Hence, when considering population
declines driven by climate change, it is important to consider the main mechanisms by which this
is likely to occur as this will highlight the most appropriate criteria for assessment under this
threat.
Changes in habitat can occur under climate change because climate is a predictor of habitat
suitability for many taxa. Changes in precipitation and temperature across space can shift,
fragment, contract or increase species ranges, leading to changes in EOO and AOO and the degree
of fragmentation. The ability of a population to track shifts or increases in suitable habitat will
depend on its dispersal capabilities (Foden et al. 2013). However, changes in climatic variables
can also expose organisms to conditions outside their range of physical tolerance, resulting in
reduced survival and fecundity, leading to reductions in population size (Deutsch et al. 2008). In
the case of corals, increased ocean temperatures or changes in pH can reduce or prevent
development of calcareous tissues, thus reducing survival and growth rates. Increased
temperatures can change predator-prey relationships, or food webs, by altering organisms’
behaviour such as movement and exposure times, with potential ramifications to both the predator
and prey or consumer and resource (Gilman et al. 2010). Phenology, or the timing of life-cycle
processes, can shift by climate change such that a mismatch occurs between, say, the timing of
flowering and the presence of pollinators (Memmott et al. 2007). And changes in the intensity and
frequency of environmental events, such as fire, drought, or floods can reduce populations that
have evolved under a different regime (Dale et al. 2001). For example, obligate seeding plants
that rely on seedbanks for post-fire seed regeneration will undergo population declines if fire
frequency is increased, because fewer seeds will be added to seedbanks between successive fires.
12.1.4 Very restricted distribution and plausibility and immediacy of threat (VU D2)
Taxa that have very restricted distributions and become susceptible under climate change to a
threat that is plausible and liable to cause the entire population to rapidly become Critically
Endangered or even Extinct in the Wild will be eligible for listing as Vulnerable under criterion
D2. However, classification under criterion D2 is only permissible if the effects of climate change
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are such that the taxon is capable of becoming Critically Endangered or Extinct in a very short
time period after the effects of the threat become apparent.
Application of this criterion requires only knowledge of the species' distribution and an
understanding of the severity and immediacy of impacts of a plausible threat. For example, a
sessile terrestrial organism that is susceptible to salt would qualify for listing as VU D2 if it had a
very restricted distribution in a coastal location that is projected to become more exposed to salt
water or saltspray as a consequence of projected rises in sea-level and/or increased frequency of
coastal storms. More detailed examples are given below.
Example 1. A species that currently does not meet the area thresholds under criterion B may be
classified as VU D2 if bioclimate models (see section 12.1.12) predict that a range reduction could
correspond to a population reduction of 80% or more (and other information indicates that there
are few locations; see above). In this case, the start of the decline may not occur soon, but the
decline is plausible, and once it begins it is expected to cause a population reduction in a very
short period of time (e.g., within one to two generations or 10 years) so that the species will be
classified as CR A3c, so it now meets VU D2.
Example 2. A species of coral currently has restricted area of occupancy (less than 20 km2) but
does not satisfy the criteria for classification under criterion B. Climate change models predict
increases in ocean temperatures, greater than the typical seasonal variation, across the entire range
of the species. This temperature increase is expected to cause coral bleaching such that the area of
occupancy will be reduced to less than 10 km2 within 10 years of the start of bleaching. It is highly
uncertain when the temperature increase or the onset of the bleaching will occur, but there is a
reasonable chance that it will occur in the future. Once the bleaching starts, the species will meet
CR B2ab within a short time, so it now meets VU D2.
Example 3. A small mammal with an AOO >500 km2 occurs in a single location (see example of
Species 3 in section 12.1.5) where it is dependent on snow cover (for insulation and predator
avoidance during the winter). Climate change is expected to increase the probability of a series
of years with no or inadequate snow cover. If this occurs, the species is expected to decline by
80% or more within 1-2 generations due to mortality from exposure and predation. Although
having a number of years with no snow cover is a stochastic process and cannot be exactly
predicted, in this case the climate models indicate that it is a plausible event. The species meets
VU D2 because this plausible event, once it occurs, will cause the species to be listed as CR.
Example 4. A species has AOO <20 km2, but is not declining or under any specific threat or
experiencing extreme fluctuations. It is expected that future climate change will affect this
species, but the effects are expected to cause gradual and slow decline, which will not trigger any
criteria for CR or cause extinction within three generations. Thus, this species does not meet VU
D2.
Example 5. A fish species known only from a single oceanic archipelago, where it occurs from 1
to 30 m depth. It lives in small recesses on slopes and walls of rocky reefs. In this region, localized
declines, including the complete loss of at least one other endemic fish species, have occurred
after strong El Niño-Southern Oscillation (ENSO) events that result in shallow waters that are too
warm and nutrient poor for extended periods of time. The frequency and duration of ENSO events
in this region appears to be increasing. Given the restricted distribution of the species and its
specialized shallow water habitat, oceanographic environmental changes, such as those associated
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with future ENSO events, may cause the extinction of this species in a short period of time (as has
happened for a similar species). Thus, it meets VU D2.
12.1.5 Definition of "Location" under climate change (B1, B2, D2)
Using the number of locations in Red List assessments requires the most serious plausible threat(s)
to be identified (see section 12.1.3). In some cases, the most serious plausible threat will be
climate change, but it may not be correct to assume that species threatened by climate change
occupy a single location. In general, it is not possible to identify climate change as the main threat
(for purposes of defining locations) without knowing something about how the effects of climate
change are likely to be played out through the proximate causes or direct threats. For most species
susceptible to climate change, climate change itself (e.g., increasing temperatures or changes in
precipitation) is not the direct threat. Rather, the process through which climate change is expected
to affect species involves a large variety of threats or proximate causes—such as changes in fire
frequency, hydrology, species interactions, habitat suitability, diseases—that affect the species
vital rates (these proximate causes can be inferred using knowledge of species ecology and
predicted changes in relevant climatic variables). Thus, even when the ultimate cause of
endangerment is climate change, the locations occupied by a species should be defined (and
counted) in terms of these direct threats. Climate change should only be used to define the number
of locations when it is the direct threat (e.g., where survival rates are reduced by thermal stress
and are likely to be the principal direct cause of population declines or when suitable habitat is
reduced due to changes in temperature and precipitation).
In some cases, climate change may threaten different parts of a species' range through different
proximate factors, or not affect some parts at all (for example, part of the range may be expanding).
In such cases, the most serious plausible threats should be used to define locations in different
parts of the species range in accordance with section 4.11 (options a-d).
Examples of estimating the number of locations for species susceptible to climate change:
Species 1 is restricted to a single climatic zone affected by severe storms that cause episodes of
high mortality. The frequency of severe storms in the region is projected to increase by at least
20% over the next 100 years. A single severe storm is unlikely to affect the entire range of the
species, but two severe storms could cover the entire range. The species is correctly estimated to
occur at two locations based on severe storms as the proximate threat (the minimum number of
independent storms that could affect its entire range). It would be incorrect to interpret the species
as occupying a single location based on the single climatic zone occupied in which severe storm
frequency is projected to increase.
Species 2 is restricted to three coastal freshwater wetlands potentially affected by saltwater
incursion associated with sea level rise. Two of the wetlands occur on the same floodplain, one at
a low-lying site 0.5 metres above sea level, and another perched on the upper floodplain five
metres above sea level. The third wetland also occurs at five metres above sea level, but in another
region where there is a very large inter-tidal range. Sea level is projected to rise, on average by
1.0 metre by year 2100. The low-lying wetland will certainly be affected by sea level rise. The
nearby perched wetland is very unlikely to be affected by sea level rise. The third wetland could
be affected by saltwater incursion during extreme spring tides under projected future climate, but
this is uncertain. Incursion by saltwater is the most serious plausible threat at the low-lying (first)
site and the distant (third) site with the high inter-tidal range. These two sites could be interpreted
as a single location if they are both threatened by the same regional sea-level rise. However, if sea
level rise could lead to different outcomes at the two sites they could be interpreted as two separate
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locations. For example, the same amount of sea level rise may inundate the first wetland but only
sporadically affect the third wetland, causing different types of impacts at the two wetlands (total
habitat loss in one and temporary population reduction in the other). If the independence of threat
outcomes at the two wetlands is uncertain, then a bounded estimate of [1-2] locations is
appropriate (see section 3). The second wetland is very unlikely to be affected by sea level rise,
and hence the most serious plausible threat for this wetland is not sea level rise. If this site is
subject to other threats, the most serious plausible one will govern how many locations are
represented at that site. For example, if the entire wetland is threatened by polluted runoff, then it
should be counted as a single location and the total number of locations for the species is [2-3].
Alternatively, if the second wetland is not threatened, then the number of subpopulations at that
site could be used as a proxy or the number of locations may not be applicable to the assessment
of the species (i.e., the subcriteria for number of locations cannot be met, see section 4.11).
Species 3 is restricted to the highest altitudes of two mountain ranges separated by a plain of 100
km. The two mountain ranges have a seasonal cover of winter snow that extends above a similar
threshold altitude (1,800 m above sea level), although the summits of their mountains are at
different elevations. Seasonal snow cover affects breeding success by providing insulation during
cold winters. The extent of snow cover is projected to decline stochastically over the next 30 years.
The most serious plausible threat is the risk of a year in which there will be very low or no winter
snow cover, which causes an episode of very high mortality in the species population. The chance
of this occurring in the same year on both mountain ranges is about 30%, based on correlation of
minimum snow extent over previous years. Despite their geographic separation, the two mountain
ranges are interpreted as a single location for the species because they may be affected by the
same ‘low-snow cover’ event.
12.1.6 Severe fragmentation (B1, B2, and C2)
If a taxon is not currently severely fragmented (see section 4.8), this cannot be used to meet the
severe fragmentation subcriteria (e.g., criterion B1a) even if there is evidence to infer that it may
become so under future climates. However, projected future fragmentation can be used to infer
continuing decline, if certain conditions are met. Continuing decline is recent, current or projected
future decline (see section 4.6). Severe fragmentation can for some species lead to local
extinctions of subpopulations inhabiting the smallest habitat fragments. If the population density
and the projected distribution of fragments justify a prediction of increasing rate of local
extinctions in the near future, this may be used to infer continuing future decline in population
size.
The same conditions may also allow inferring population reduction under criterion A3, but this
requires a quantitative prediction. Suppose that a bioclimatic model (see section 12.1.12) predicts
that EOO of a taxon will decline by 20% in the next three generations due to climate change.
Assuming that the population reduction will be at least as large as the EOO reduction (but see
section 12.1.8), this can be used to infer a 20% population reduction, but would not by itself meet
the VU threshold for A3. However, suppose that a population dynamic model predicts that
populations smaller than a certain size have 50% risk of extinction. If the bioclimatic model also
predicts that 40% of the population will be in fragments that support populations of this size or
smaller, then we can infer that the population will undergo a further 20% reduction due to
increased local extinction of smaller populations. Combined with the 20% reduction due to range
contraction, this result can be used to infer a total of 40% population reduction, listing the species
as VU A3.
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12.1.7 Extreme fluctuations (B1, B2, and C2)
One of the predictions of many climate models is an increase in the frequency of extreme weather
events (such as droughts, heat waves, etc.). This may increase population fluctuations to extreme
levels (see section 4.7). If a taxon is not currently experiencing extreme fluctuations, but is
predicted to do so in the future as a result of climate change, this prediction cannot be used to meet
the extreme fluctuation subcriteria (e.g., B1c). However, a projected future increase in population
fluctuations can be used to infer continuing decline, if certain conditions are met. Continuing
decline is recent, current or projected future decline (see section 4.6). Extreme fluctuations can
for some species lead to an increase in rate of local extinctions of subpopulations (especially if
combined with severe fragmentation; see above). If the population sizes and the projected increase
in fluctuations justify a prediction of increasing rate of local extinctions in the near future, this
may be used to infer continuing future decline in population size.
A prediction of future extreme fluctuations can also contribute to a VU D2 listing if projected
local extinctions could cause it to meet the criteria for CR in a very short period of time (see
above).
12.1.8 Inferring population reduction and continuing decline (A3, A4, B1, B2, C2)
Criteria A3 and A4 may be applied if a population reduction of a given magnitude may be inferred
from relevant evidence. Unless there are quantitative models enabling projections of suitable
habitat or population size under future climates, the evidence base will be indirect or
circumstantial (section 3.1). For example, if there is evidence of a strong relationship between
temperature and survival or temperature and breeding success, and there are projections of future
temperatures that suggest that they will rise rapidly enough to reduce the number of mature
individuals by at least 30% within the next 10 years or three generations, whichever is longer, then
this information may be used to apply criterion A3. Similar inferences may be used to infer the
direction of trends in the number of mature individuals, which may be used to infer continuing
declines under criteria B1, B2 and C2.
12.1.9 Inferring reductions from bioclimatic models (A3, A4)
Bioclimate envelope models (or bioclimate models) are often used to predict changes in a taxon’s
range as defined by climatic variables. Such models are also known as species distribution models
(SDM) or ecological niche models (ENM) that use climatic variables as predictor variables (see
section 12.1.12 for detailed guidance on developing these models). The results of bioclimate
envelope models will be a series of habitat suitability maps. In order to infer population reduction
(for use in criteria A3 or A4) from these maps, it is necessary to calculate the expected population
size from the current map and from the map for the time step that corresponds to three generations
in the future. If climate data are not available for the year that corresponds to three generations in
the future, it should be created by interpolation from the available layers.
Even if the current population size of the taxon is known, the same method of estimation should
be used for both the "current" and the "future" maps. This is because the quantity of interest is
the proportional change in population size, and using the same methods removes some of the
effects of the assumptions involved in making this conversion from habitat suitability (HS) to
population size.
The relationship between population reduction and habitat loss may not be linear (see section 5.8).
However, in the absence of more specific information, it is an allowable assumption. With this
assumption, the conversion from habitat suitability to population size will involve summing all
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the HS values in each map, and calculating the proportional change in three generations. One
important correction to this calculation is to use a threshold value of HS, to exclude from
calculation of proportional reduction any areas that are unlikely to support a population because
of low suitability. Another correction that should be made is to exclude patches that are too small
to support a viable subpopulation (because of demographic stochasticity or Allee effects), or too
isolated to be colonized by dispersers from occupied patches. Note that these corrections require
species-specific information, and must be made separately for each taxon.
For species with limited dispersal ability, it is important to examine the overlap between
successive habitat maps, projected at 1-generation intervals. The degree of overlap between each
successive pair of habitat maps determines the relationship between habitat loss and population
reduction. If there is little overlap, population reduction is likely to be larger than the projected
habitat loss.
Other types of correlative analyses of population size or density as a function of environmental
factors can also be used to infer population reductions. For example, the 2015 assessment of the
Polar Bear (Ursus maritimus) used statistical relationships between sea ice and population size,
combined with projected future decrease in sea ice, to calculate the range of plausible future 3-
generation population reduction amounts (Wiig et al. 2015).
Projected change in habitat can also be used to infer continuing decline in habitat quality (e.g.,
criterion B1b(iii)).
12.1.10 Inferring reductions from demographic change
As noted in section 12.1.3, climate change may lead to population reductions or continuing
declines through a range of demographic mechanisms. Understanding these can help to project
the direction and rate of population response. The tools that are used to inform these projections
will depend on the mechanism of response. In this section we briefly review the principal
mechanisms, alert assessors to appropriate means of inference and suggest suitable tools to inform
projection.
Some mechanisms are based on a direct ecophysiological relationship between a climate variable
and one or more vital rates of the population. For example, in some taxa quantified relationships
exist between fecundity and particular temperature variables for which projections can be derived
from the outputs of Global Circulation Models (e.g., Kearney and Porter 2009). Other vital rates
including survival, growth and dispersal may be affected. A range of plausible scenarios can be
constructed from uncertainty in both the species response and the climate projection to estimate
plausible bounds of population reduction. This method of projection will usually involve some
assumptions about rates of adaptation to new environmental conditions (Hoffmann and Sgrò
2011). In some cases, there may be sufficient data to use demographic models for this purpose.
Some mechanisms involve a relationship between calcification rates and ocean acidity for
organisms with calcified body parts (e.g., corals, molluscs) (Orr et al. 2005). Hence projections
of ocean acidification (with characterisation of uncertainty in trends) should permit inferences
about the continuing declines (criteria B and C) and projections of population reduction over
required time frames (criterion A). Again, this should be based on defensible assumptions about
rates of adaptation and should generate bounded estimates to represent the uncertainty in the
projections.
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A wide range of taxa have life history processes and vital rates that respond to regimes of fire,
flood or storms, and hence may undergo population reductions depending on how disturbance
regimes respond to climate change. It is possible to generate projections for indices of change in
the frequency, intensity and season of such disturbance events from Global Circulation Models
(e.g., Milly et al. 2002, Clarke et al. 2012, Zhao et al. 2015). Such projections, in combination
with models of the species responses to the disturbance should support inferences about
continuing declines and bounded estimates of population reduction over required time frames.
Changes in the frequency of heat waves and other extreme weather events could be treated in a
similar manner where they are key drivers of declines.
A fourth mechanism of response to climate change involves changes to species interactions. These
are challenging to predict, but it may be plausible to project the direction of change, as a basis for
inferring continuing declines, if the mechanisms are reasonably well understood. Examples
include population changes of a target species inferred from projected increases in the area of
spatial overlap between the habitat of the target taxon with those of its competitors, predators or
disease vectors. Another example involves continuing declines inferred from phenological
decoupling of mutualistic or facilitation interactions, or conversely phenological changes that
result in increased exposure to competitors, predators or diseases.
Quantitative estimates of population reduction may be derived for many of these estimates using
stochastic population models (e.g., Akçakaya et al. 2004). The parameterisation of these models
may be adjusted to reflect projected trends in vital rates under a range of future climate scenarios
based on regionally skilled Global Circulation Models (see section 12.1.12 for guidance on the
selection of these). All applications of such models should justify the parameter settings and
selection of scenarios used in projection. Recent developments allow the coupling of stochastic
demographic models to species distribution models projected to produce a time series of habitat
suitability maps under future climate scenarios (e.g., Keith et al. 2008). Alternative modelling
approaches are developing to achieve similar goals (e.g., Cabral et al. 2013). These not only allow
projections of future population reductions for assessment of criteria A3 and A4, but may produce
estimates of extinction risk over required time frames for assessment under criterion E (see section
12.1.11).
12.1.11 Estimating extinction risk quantitatively with coupled habitat and population models (E)
Because of its time horizon for VU of 100 years (regardless of generation time), criterion E can
be used to list species with short generation times that are predicted to be threatened by climate
change. However, the difficulties with using criterion E (see section 9) are increased when climate
change is the main threat, because of the need to take into account multiple types of stochastic and
deterministic changes in the taxon's environment, demography and habitat that are caused or
exacerbated by climate change.
New approaches that link outputs of global circulation models (GCMs, or climate models) to
species habitat models and metapopulation models can be used to estimate risks of extinction
(Keith et al. 2008, Anderson et al. 2009, Brook et al. 2009, Cabral et al. 2013) when adequate
data are available for developing both bioclimate models (see section 12.1.12) and population
models (see section 9). Preliminary findings from these studies showed that extinction risks under
climate change are subject to complex dependencies between species life history, distribution
patterns and landscape processes (Keith et al. 2008).
It is very important not to ignore other threats, which may interact with, or supersede, climate
change impacts when predicting species vulnerability to climate change. Approaches that focus
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on climate change alone may therefore lead to underestimation of extinction risks (Brook et al.
2009).
12.1.12 Using bioclimate models
Some of the guidance in the preceding sections refers to variables that may be calculated from
outputs of bioclimate envelope models (or, bioclimate models). Such models are also known as
species distribution models (SDM) or ecological niche models (ENM) that use climatic variables
as predictor variables. This section will summarize methodological guidance in the use of these
models for the purposes of Red List assessments. It is important to note that the use of these
models is not necessary for all assessments of species threatened with climate change. Future
versions of this document may include guidelines for other types of predictive modelling (such as
eco-physiological models) that may be useful for Red List assessments.
Bioclimate envelope models have been widely applied to explore potential impacts of climate
change on species distributions (for reviews of this field see: Guisan and Zimmerman 2000,
Guisan and Thuiller 2005, Heikkinen et al. 2006, Franklin 2010, Peterson et al. 2011; for a
practical introduction see Pearson 2007). These models commonly utilize associations between
environmental variables and known species’ occurrence records to identify climatic conditions
within which populations can be maintained. The spatial distribution that is suitable for the species
in the future can then be estimated under future climate scenarios. Advantages and disadvantages
of this modelling approach have been widely debated in the literature, and multiple uncertainties
make it essential that the model outputs are carefully interpreted (Pearson and Dawson 2003,
Hampe 2004, Araújo and Guisan 2006, Thuiller et al. 2008).
Bioclimate envelope models may provide useful information for Red Listing by identifying
species that are more or less likely to experience contractions in the area of suitable climate space
in the future and by estimating the degree to which potential distributions in the future might
overlap with current observed distributions. The guidelines here are intended as a list of
methodological issues that must be carefully considered in applications of these models for red
listing under climate change. It is important that methodologies are well justified within the
context of any particular study, and with respect to the biology of the taxon being assessed.
Assessments that rely on bioclimate models will be reviewed by the Standards and Petitions
Committee (SPC), so sufficient detail must be provided to allow the SPC to determine if the model
follows these guidelines.
Results of bioclimatic envelope models can be used in various ways to help with species
assessments under the Red List Categories and Criteria. These uses include inferring population
reduction under criterion A3 and continuing decline (see section 12.1.9), linking bioclimate and
demographic models for criteria E (section 12.1.11), inferring continuing decline from projected
increases in fragmentation (see section 12.1.6), and projecting plausible threats for use in criterion
D2 (see section 12.1.4). Although the interpretation of the results from these models for Red List
assessments relies on a number of assumptions, they do allow a tentative solution to the problem
of incorporating the long-term impacts of climate change. A number of alternative modelling
approaches are being developed to explore the relationship between climate change and species
endangerment (see section 12.1.11), which will allow more comprehensive guidelines for
assessing the risk of extinction due to climate change.
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Quality of species occurrence data
Bioclimate envelope models rely on observed occurrence records for characterizing species limits
of tolerance to climate predictors so it is essential that these data are of good quality. Confidence
in the accuracy of georeferencing and species identifications of occurrence records should be high.
It is important that georeferencing of occurrence records is accurate to a degree that is relevant to
the resolution of the environmental variables (e.g., accuracy should be within a few tens of metres
if the resolution of analysis is 1 km2). Ideally, occurrence records should be associated with
vouchered specimens and/or should have been identified by experts in the taxonomic group of
interest. Data extracted from distributed databases (e.g., GBIF, HerpNET) should be carefully
checked for accuracy, coverage and sampling intensity prior to use.
Occurrence data sampled from the whole range of the species should be included when calibrating
bioclimate models, even in the case of regional assessments. Excluding occurrences from outside
the region of interest reduces the model's ability for getting information on the full ‘climate
envelope’ of the species. If, for instance, the current environmental conditions of a set of
occurrence points in an area outside the region correspond to future projected conditions in some
part of the region, then excluding those points from the model decreases the model's ability to
correctly predict areas within the region that may become suitable in the future.
Selection of environmental predictor variables
Predictor variables need to be carefully selected. It is important to select variables that are
expected to exert direct influence on the distributions of species (e.g., minimum temperature of
the coldest month, maximum temperature of the warmest month, spring precipitation) through
known eco-physiological mechanisms, and avoid indirect variables (e.g., altitude, topographic
heterogeneity) (e.g., Guisan and Zimermann 2000). Variables such as elevation, latitude or
longitude may serve as useful proxies for current climatic conditions but they hinder the accuracy
of future predictions, because the relationships between these and climatic variables may change
in the future. In particular, including elevation in the model is likely to result in the
underestimation of the projected effects of future climate change. Often, there are several
candidate variables for modelling the distributions of species, but they tend to be correlated
amongst each other. When this is the case, it is often advisable to investigate the correlation
amongst them and select a reduced number of uncorrelated variables (to avoid problems of co-
linearity; Araújo and Guisan 2006). One possible approach is to use Principal Components
Analysis (PCA) to identify a reduced number of significant axes and then select a sub-set of
ecologically meaningful variables that are associated with each one of the significant axis. Note
that the number of predictor variables should not exceed the number of species occurrence records
that are used. As a general rule, no more than one predictor variable for every five observations
should be used. Some methods (e.g., Maxent, Phillips et al. 2006; Boosted Regression Trees, Elith
et al. 2008) select a parsimonious number of variables automatically in which case the above rule
would not apply. One reason to aim for parsimony in variable selection is to avoid overfitting of
the models, thus increasing generality.
Land-use masks
In addition to the climatic predictor variables, current and future land-use also constrains the
distribution of species. This is especially crucial for species whose bioclimatic envelope is
predicted to shift through human-dominated landscapes. Assessments that rely on climate data
alone are prone to over-predict areas of suitable habitat because climate may be suitable, but land
cover may be unsuitable (Pearson et al. 2004). A land use map can be used as a mask to exclude
such unsuitable areas from current and projected habitat. However, if land-use and climatic
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variables are likely to interact, then the land-use variables should be included in the model together
with the climatic variables, rather than used as a mask (Stanton et al. 2012).
Choosing an appropriate spatial resolution
Bioclimatic models have been fitted with data of varying resolutions, for instance ranging from 1
ha cells in Switzerland (Randin et al. 2009), to 2 degree latitude-longitude cells at a global level.
There is commonly a trade-off between the geographical extent of the study area and the resolution
of the data: studies across large areas are likely to use data at coarser resolutions than studies
across smaller regions. Similarly, it is often necessary to use data at finer resolution when
modelling the bioclimate envelope of restricted range species, whereas wide-ranging species may
be effectively modelled using data at coarser resolutions. Also, when modelling species across
regions with low spatial heterogeneity (e.g., flat terrain), coarser resolution data are less of a
problem than when models are used across areas of high heterogeneity (e.g., rugged terrain). It is
important to bear in mind, however, that analyses at coarse resolutions may not account for
microclimates that may be important for species persistence (Pearson et al. 2006, Trivedi et al.
2008, Randin et al. 2009).
Model selection
A large number of bioclimatic modelling techniques exist, and it has been shown that agreements
between predicted and observed distributions are often greater with models allowing complex
response curves (e.g., Elith et al. 2006). There is an ongoing debate as to whether more complex
models are more adequate for modelling species ranges under climate change (Araújo and Rahbek
2006), so it is difficult at this point to provide unequivocal guidelines with respect to the choice
of the modelling techniques. However, it is important that assessments of species range changes
are based on established methodologies that have been used and verified by several independent
research groups.
Assessing the robustness of model projections
Studies have shown that projections from alternative models can be so variable as to compromise
assessment of whether species potential distributions should contract or expand for any given
climate scenario (e.g., Araújo et al. 2005, Araújo et al. 2006, Pearson et al. 2006). Assessments
of the temporal trends in the sizes of species potential distributions should, therefore, include an
assessment of the robustness of the projections by comparing results of a range of bioclimatic
modelling techniques. We suggest that at least three modelling techniques should be compared
and be as independent as possible with regards to how they link the response and the predictor
variables (e.g., GAM and GLM are conceptually similar and tend to produce similar results).
Various strategies may be employed in cases when models forecast inconsistent trends. One such
strategy is to investigate the cause of the discrepancies. Typically, this would involve investigation
of the species response curves obtained with each one of the methods, evaluating if there is any
clear error, and then selecting the projections by the method producing more reasonable results.
This approach is useful for species with well-known ecologies where expert judgements can be
made and contrasted with the model outputs. The downside of the approach is that it involves
subjective judgement that may yield non-repeatable results. An alternative strategy is to run
ensembles of forecasts using a number of established approaches and then combine the individual
model projections through consensus methodologies (for a review see Araújo and New 2007). The
disadvantage here is that potentially significant ecological knowledge is not being used.
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Background/pseudo-absence in the species distribution data
Species distribution data may be either presence-only (i.e., records of localities where the species
has been observed) or presence/absence (i.e., records of presence and absence of the species at
sampled localities). Alternative modelling approaches have been developed to deal with each of
these cases. Some approaches that use presence-only data also utilize ‘background’ (e.g., Maxent,
Phillips et al. 2006) or ‘pseudo-absence’ (e.g., Elith et al. 2006) data. In these cases, model results
are sensitive to the extent of the study region from which background or pseudo-absence samples
are taken. It is therefore important to select an appropriate study region. In general, background
and pseudo-absence records should not be selected from areas where the species is absent due to
non-climatic factors, such as dispersal limitation or inter-species competition (because such
records provide a false-negative signal that will lead to poorer characterization of the species’
climatic requirements; Anderson and Raza 2010). Where possible, selection of the extent of the
study region should therefore take into account factors including the dispersal capacity of the
species and distributions of competitors.
Capturing entire species ranges and avoiding model extrapolation
It is necessary to include occurrence records from throughout the species range in order to avoid
artificially truncating response curves when modelling the species’ niche (Elith and Graham 2009,
Thuiller et al. 2004). For example, models based on data from only one country within a multi-
national species range will generally be unacceptable. It is possible that response curves could be
adequately characterized using part of the range provided that excluded localities do not represent
parts of the niche that are represented by other occurrence records, but such cases must be well
justified. Caution must also be exercised when extrapolating model results under future climate
scenarios (i.e., extrapolating in environmental space beyond the range of data used to build the
model; Pearson et al. 2006). Extrapolation should be avoided where possible (e.g., Pearson et al.
2002), or else the behaviour of the model (i.e., the shape of response curves when extrapolating)
should be known and well justified.
Model testing
Testing model performance is an important step in any modelling exercise. Multiple tests have
been employed to assess the performance bioclimate envelope models (e.g., AUC, Kappa, TSS;
Fielding and Bell 1997), but it is important to note that testing of bioclimate models remains
problematic for at least three reasons. First, models aim to predict the distribution of potentially
suitable climates, yet data against which this can be tested are not available (use of species absence
records is unsatisfactory because predictions of ‘presence’ in areas that are climatically suitable
but unoccupied for non-climatic reasons will be classified as model ‘errors’) (Peterson et al. 2011).
Second, performance of the models is usually inflated because studies use data for training the
models that are not independent from the data used for testing them (Araújo et al. 2005). Finally,
projections are made for events that have not yet occurred, so any attempts to test the models must
focus on examination of the internal consistency of the models rather than their predictive
accuracy (Araújo and Guisan 2006). So, although standard testing methodologies are an important
part of model building, it should be noted that the predictive skill of the bioclimatic models under
climate change remains untested.
Using appropriate metrics of species range changes
Bioclimate models may be useful to assess trends in the availability of suitable climate conditions
for species. There are two possible measures that are likely to be useful. One is based on
combining probabilities or suitability indices from the models, and the second is based on
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measuring the potential area occupied by the species after transforming probabilities
(or
suitabilities) into estimates of presence and absence. To make such a transformation, it is
necessary to use thresholds (see, for example, Liu et al. 2005). For instance, use of the lowest
presence threshold (e.g., Pearson et al. 2007) may be justified in cases with few occurrence
records, but balancing sensitivity and specificity may be more appropriate when a larger number
of presence/absence records are available. Sensitivity of conclusions to the selection of alternative
methods for defining thresholds should be examined. However, it should be noted that the
measures of change in climate suitability that are relevant to red listing are relative measures (of
proportional change in time) and these are, in principle, robust to alternative methods for defining
thresholds. The absolute areas (of range or potential habitat) should not be used as part of
assessments of species extinction risk under climate change because estimates of change from
bioclimate models are very sensitive to the thresholds used. Note that thresholds may also be used
when converting habitat suitability to population size (see section 12.1.9).
Future emission scenarios
Climate models are based upon socio-economic scenarios. Each of these scenarios makes different
assumptions about future greenhouse gas emissions, land-use and other driving forces.
Assumptions about future technological and economic developments are built into families of
‘storylines’, each of which describing alternative pathways for the future because there is no
theoretical basis for long-term socio-economic forecasting. The IPCC Fifth Assessment Report
(AR5) projected changes in the climate system using a set of scenarios called Representative
Concentration Pathways (RCPs). In order to account for uncertainty in predictions of future
climate change, studies should explore a range of plausible scenarios of climate change (e.g., the
RCP8.5 and RCP4.5 scenarios in IPCC 2013), and the broader the range of scenarios considered
the better. The set of scenarios selected should be justified. Furthermore, as emission scenarios
are revised in future, the relevant red list assessments based on them should be revised.
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14. Appendix: Summary of Changes to the Guidelines
Changes in version 15.1 (July 2022)
Section 3.2.3: Minor correction to the text “A precautionary attitude (i.e., low risk tolerance) will classify
a taxon as threatened unless it is highly likely that it is not threatened…” (the word “likely” replaces
“unlikely”)
Sections 5 and 5.4: Text changed to clarify that for criterion A “reversible” refers to the reduction and
“understood” and “ceased” refer to the causes of reduction.
Changes in version 15 (January 2022)
Section 3.1: Clarification of data quality categories.
Section 4.5.4: Emphasizing that it is incorrect to calculate a simple (unweighted) average of the 3-
generation reduction amounts of the different subpopulations.
New Section 4.5.6 on differentiating fluctuations from reduction.
Section 4.8: Clarification of habitat vs. population fragmentation.
Section 4.11: Further clarification of the definition of Location.
Section 5: Discussion of the reasons for scaling reductions with generation time and for calculating
reduction over 3 rather than fewer generations.
Section 10.1: Using uncertainty guidelines for NT.
Section 10.1: Discontinuing NT for conservation-dependent species (also deletion in Section 5.4).
Sections 11.1 and 11.3: Emphasizing the importance of, and further guidance on, the concept of "exhaustive
surveys."
Changes in version 14 (July 2019)
Section 4.11: Clarification of "rapidly" in the definition of location.
Section 11: New guidelines for listing taxa as EX or CR(PE) (or as EW or CR(PEW)).
Section 11.1: Use of EW for plant or fungal taxa represented by viable propagules in storage facilities.
Changes in version 13 (March 2017)
Section 2.3: Additional explanation of the basis for using the highest category of threat.
New Section 2.3.1 on the quantitative thresholds.
Section 4.3.1. Clarification of "reproduction" and biased sex ratios.
Section 4.4. Averaging generation length over all individuals; new paragraph on definition of "age".
Section 4.6. Documenting the location of declines in relation to the species' range.
Sections 4.10, 4.10.1, 4.10.3 Extensive edits to clarify issues of scale in estimating AOO.
Section 4.10.7. Clarification of scaling the estimated area of occupied habitat derived from habitat maps
for calculating AOO and EOO.
New Section 4.10.8 about the effect of sampling effort and detectability on estimates of AOO.
New Section 4.10.9 on the complementarity of AOO, EOO and number of locations.
Section 4.11. Clarification that assessments should consider all areas whether they are under threat or not.
Section 12.1.12. Clarification of the use of elevation in bioclimate models.
Changes in version 12 (February 2016)
Section 2.1.2: Text on applying the criteria in very small geographic areas.
Section 2.2: Use of the term "red-listed".
Section 2.2.1: Clarifying the 5-year rule for transfer between categories.
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Section 4.2: Clarifying subdivision.
Section 4.3.1: Text on suppressed individuals.
Section 4.3.2: Edits related to clonal colonial organisms.
Section 4.4: Additional explanation of the "pre-disturbance" generation time.
Section 4.5: Major restructuring and new text on calculating reductions. Also, the spreadsheet file
CriterionA_Workbook.xls is updated with additional tabs demonstrating basic calculations.
Section 4.5.3: This new section includes most of former section 5.8; the rest of former section 5.8 is merged
with 4.5.1.
Section 4.8: Clarification of habitat vs. population fragmentation.
Section 4.9: Additional explanation of the risk-spreading function of EOO.
Section 5: Additional explanation of the rationale of criterion A.
Section 5.1: New section on the basis of reductions, including a new table.
Section 5.2: This is the former section 5.1 (use of time caps).
Section 5.3: This is the former section 5.2 (how to apply A4).
Section 5.4: This is the former section 5.3 (the ski-jump effect), with a more descriptive title, and expanded
text (point (3) at the end).
Section 5.5: This is the former section 5.4 (severely depleted populations), with a more descriptive title,
and additional text and examples.
Section 5.6: This is the former section 5.5 (fisheries), now divided into two subsections, with additional
text discussing issues related to fisheries management.
Section 5.7: This is the former section 5.6 (was titled “Trees”).
Section 5.8: This is the former section 5.7 (loss of habitat and reduction).
Former section 5.8 is merged with parts of section 4.5 (see above).
Section 10.1: Definition of a "targeted taxon-specific or habitat-specific conservation or management
programme".
Section 10.4: New section on when it is not appropriate to use DD.
Section 12.1: Major restructuring; substantial new text and a new Figure. Note that many of the subsection
numbers within section 12.1 have been changed.
This appendix is expanded to cover all previous versions.
Changes in version 11 (February 2014)
Section 2.1.3: Substantial changes related to introduced taxa and subpopulations.
Section 2.1.4: New section on managed subpopulations.
Section 3.2.3: New guidance on setting the dispute tolerance and the risk tolerance values.
Section 4.4: New paragraph on using pre-disturbance generation length.
Section 4.9: Additional explanation on using minimum convex polygon for EOO.
Section 4.10.7: Expanded discussion on using habitat maps and models for EOO and AOO.
Section 11.1: New paragraph on using EW when none of the subpopulations are wild.
Changes in version 10.1 (September 2013)
Section 11.2.1: New paragraph added.
Minor corrections in sections 4.3, 4.5, and 13.
Changes in version 10 (February 2013)
Section 2: Table 2.1 and Figure 2.1 updated; minor changes to the last paragraph of section 2.1.2;
clarification of LC and NT categories and minor corrections in sections 2.2 and 2.3.
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Section 4.1: Clarification of the definitions of population and population size.
Section 4.2: Clarifying the relation between a species' mobility and the delineation of its subpopulations.
Section 4.6: The relation between continuing decline and "current population trend."
Section 4.11: The number of locations when there are two or more serious plausible threats.
Section 7: New paragraphs (third and fourth) clarifying the subcriteria i and ii of C2a.
Section 8: Minor change to the 2nd paragraph, clarifying "very short time period" in D2.
Changes in version 9.0 (September 2011)
Section 4.4: The guidelines for calculating generation length are revised substantially.
Section 4.5.1: Added text: "If populations fluctuate widely, or oscillate with periods longer than generation
time, fitting a time series longer than three generations may give a more representative estimate of the long-
term population reduction. However, regardless of the length of the time series fitted, the reduction should
be calculated for the most recent three generations. The model to be fitted should be based on the pattern
of decline, which may be inferred from the type of threat."
Section 4.6: Two new paragraphs (3rd and last), and addition to the 5th paragraph ("Note that …").
Section 5.5: Sentence modified: "If declines continued, there would be reason for concern; in this case a
new assessment, against all 5 criteria, may indicate that the taxon is still threatened."
Section 10.1: A new example added to the list of examples where an NT listing would be justified.
Section 10.3: Substantial revision to the 2nd DD tag, which is now named "Taxonomic uncertainty explains
lack of information."
This appendix added.
Changes in version 8.1 (August 2010)
Minor corrections, incl. to Table 2.1
Changes in version 8.0 (March 2010)
Section 2.3: Minor change to refer to the new section 12
Section 4.10.5: Several minor changes, mostly to equations to make them more clear.
Figure 4.4: New figure
Section 5: New paragraphs (third and fourth) to clarify subcriteria a and b.
Section 5: New sentence: "If any of the three conditions (reversible and understood and ceased) are not
met in a substantial portion of the taxon's population (10% or more), then A2 should be used instead of
A1."
Section 8: Changes in the first and third paragraph to clarify, and to give an example for "a very short time"
(within one or two generations).
Section 12: New section on Threatening Processes, including guidelines for applying the criteria to species
impacted by global climate change.
Changes in version 7.0 (August 2008)
Section 2.1.1: Expanded guidance on taxonomic scales, including newly described and undescribed
species, and subpopulations.
Section 2.2.1: Detailed definition of the reasons for transfer between categories.
Section 4: Additional guidance on calculating the number of mature individuals, generation time, future
reduction, EOO, and number of locations.
Section 10.3. Data deficient flags.
Section 11. New section on the extinct categories and the PE tag.
Changes in version 6.2 (Dec 2006)
Section 2.3: Changes to paragraph on comparison of criteria A-D vs E.
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114
Section 8: Minor changes to section on taxa known only from the type locality
Changes in version 6.1 (Aug 2006)
Minor changes, including version number on page 1.
Changes in version 6.0 (July 2006)
Section 4.3.2: Mature individual for colonial or modular organisms
Section 4.9: Clarification on EOO, including risk-spreading; discouraging exclusion of discontinuities or
disjunctions except in extreme circumstances, but encouraging it for calculating change in EOO; EOO of
migratory species.
Section 4.10: Further explanation of why a specific scale is necessary for AOO; new section on AOO based
on habitat maps and models
Section 4.11: Guidance on number of locations with different threats in different areas
Section 5: How to apply criterion A4; discussion of population data contradicting habitat data; description
of the workbook file (CriteriaA workbook.xls) accompanying the guidelines.
Section 6: Guidelines for applying Criterion B (numbering for subcriterion a)
Section 8. New guidelines and an example for applying Criterion D2
Section 10: Examples for when to use and when not to use NT and DD.
Changes in version 5.0 (April 2005)
Expanded sections on extreme fluctuations and severely fragmented; NT based on conservation
dependence
Changes in version 4.0 (March 2004)
New section on Transfer between categories.
Clarifications on continuing decline vs. reduction; criterion A basis; A1 vs. A2; A4.
Changes in version 3.0 (May 2003)
Additions to clarify issues related to taxa below the rank of variety, introduced taxa, generation length for
clonal plants, specifying criteria for NT; new examples and references, and numerous minor edits.
Changes in version 2.0 (Jan 2003)
First version that covered all criteria and definitions (48 pages).
Changes in version 1.1 (Dec 2001)
Minor additions such as clarifying that "non-overlapping" is not "isolated" (10 pages).
Version 1.0 (June 2001)
This first version was titled
Guidelines for Assessing Taxa with Widely Distributed or Multiple
Populations Against Criterion A” and became section 5.8 in version 2.

 

 

 

 

 

 

 

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