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Outcome Indicator Framework for England's 25 Year Environment Plan: 2021 This document supports D6a: Abundance of priority species in England D6b: Distribution of priority species in England Technical background document Date: May 2021 Prepared by: Fiona Burns (RSPB); and Rob Boyd, Rob Cooke and Nick Isaac (UKCEH)
We are the Department for Environment, Food and Rural Affairs. We’re responsible for improving and protecting the environment, growing the green economy and supporting our world-class food, farming and fishing industries. We work closely with our 33 agencies and arm’s length bodies on our ambition to make our air purer, our water cleaner, our land greener and our food more sustainable. Our mission is to restore and enhance the environment for the next generation, and to leave the environment in a better state than we found it. © Crown copyright 2020 This information is licensed under the Open Government Licence v3.0. To view this licence, visit www.nationalarchives.gov.uk/doc/open-government-licence/ This publication is available at www.gov.uk/government/publications Any enquiries regarding this publication should be sent to us at 25YEPindicators@defra.gov.uk www.gov.uk/defra
Contents 1. Introduction ............................................................................................................ 4 2. D6a: Abundance of priority species in England ...................................................... 4 2.2. Species List...................................................................................................... 4 2.3. Data Sources ................................................................................................... 5 2.4. Indicator Methods .......................................................................................... 14 2.5. The D6a Indicator .......................................................................................... 15 3. D6b Distribution of priority species in England ..................................................... 18 3.1. Species List.................................................................................................... 18 3.2. Data Sources ................................................................................................. 18 3.3. Indicator Methods .......................................................................................... 21 3.4. The D6b Indicator .......................................................................................... 21 4. References ........................................................................................................... 22
D6. Abundance and distribution of priority species in England Technical background document, 2021 1. Introduction In 2018 the government published its 25 year environment plan for England: A collection of goals for improving the environment in England. A key commitment of this plan was to develop a set of indicators for monitoring progress towards these goals, and a framework for these indicators has now been established. The Outcome Indicator Framework comprises 66 indicators, split into 10 broad themes, spanning a range of issues from greenhouse gas emissions to the abundance of threatened species. This paper presents robust indicators of the status (abundance and distribution) of priority species in England, with species identified as conservation priorities being taken as a proxy for threatened species. Despite the relatively high quality and quantity of both data and analytical methods in the England, it should be recognised from the outset that any indicator on the status of priority species will be hampered by short comings in the availability of data. 2. D6a: Abundance of priority species in England 2.2. Species List The species considered for inclusion in the England Priority Species Indicator are those on the S41 list. Species on the S41 list are those on the 2007 UK Biodiversity Action Plan list that are present in England with the addition of Hen Harrier. There are a small number of taxa below the species level (i.e., sub-species) on the S41 lists. Such infra-specific taxa were only retained if the associated species was not included. This led to the removal of three sub-species and reduced the total taxa on the S41 list from 943 to 940. However, not all species on that list have suitable data available. The species in D6a are those species for which annual estimates of abundance are available, derived from national-scale monitoring schemes.
2.3. Data Sources Robust English population time-series were sought for as many priority species as possible to produce the England Priority Species Indicator – D6a. The majority of these data have previously been published and many are used as part of the England biodiversity indicator set currently; details of these analyses and the rules for species inclusion into the data sets are given in the following sections. Table 1 summarises the taxonomic coverage and data sources contributing to D6a. Tables 2 and 3 provide a summary of the abundance datasets included in the indicators. They show the analytical methods used to generate the species’ time- series in each dataset. Although these vary in detail, the underlying method is similar. These datasets are generated largely from data collected by national monitoring schemes. In these schemes, data are collected in a robust and consistent manner and the geographical coverage is good, with statistical approaches used to correct for biases in coverage. These datasets are ideal for producing population time-series for widespread species; however, in some cases the sample size is insufficient to generate time-series for cryptic, rarer or more range-restricted species. Each scheme has a set of criteria to determine whether time-series can be generated for each species and if they are sufficiently robust to be included in the published results of the scheme. Table 4 gives an overview of the quality of the data derived from each scheme. Further information about each monitoring scheme and the data analysis and results can be found in the references given at the end of this paper. Bird time-series are well documented, and several data sources are available (Table 3). Some bird species are represented in more than one dataset. The order of the rows in Table 3 shows the hierarchy used, from top to bottom, to ensure that the most appropriate and robust data for each species was included in the indicator. The majority of species time-series start around 1970 and the date of the last available update is 2018. The Rothamsted Insect Survey started in 1968, but to avoid over representing these time-series in the overall indicator, data were only used from 1970 onwards, and the time-series were expressed as a proportion of the 1970 value. Some datasets begin later than 1970, for example the butterfly time- series begin in 1976. However, the indicator method used is robust to the addition of species groups after the baseline year (see section 4). Table 4 highlights the robustness of the data obtained from the monitoring schemes, and Table 5 gives a summary of the relationship between the number of species on the S41 list and the number of these for which population time-series are available.
Table 1: Taxonomic breakdown of the England Priority Species Indicator D6a
Group Survey Species From To
Birds England breeding bird indicators 28 1970 2018
England wintering waterbird 6 1973 2017
indicator
Rare breeding bird panel 5 1981 2016
Seabird monitoring programme 4 1975 2017
SCARABBS 1 1994 2018
TOTAL 44 1970 2018
Butterflies UK Butterfly Monitoring Scheme 21 1976 2018
Mammals National Bat Monitoring Programme 5 1998 2018
Breeding Bird Survey 1 1998 2018
TOTAL 6 1998 2018
Moths Rothamsted Insect Survey 69 1970 2016
Priority moths – Butterfly 9 1991 2016
Conservation
TOTAL 78 1970 2016
TOTAL 149Table 2: Summary of the analysis methods and criteria for species selection for bird datasets
Monitoring Time Data Type Species selection method Analysis method
Scheme period
Seabird 1986- Unsmoothed Very small colonies and colonies where For the majority of species, a
Monitoring Panel 2018 index counting error is known, or suspected, to combination of SMP and census
(SMP) and exceed 5% are excluded from SMP time- data is used. The 2 census
Seabird series. The accuracy of time-series obtained estimates are used, with linear
censuses using the SMP sample was assessed by interpolation for the intervening
comparing them with data from 2 complete years. The SMP time-series is
censuses of all breeding seabirds in the UK. A anchored to the 2nd census
time-series was rejected as inaccurate where a estimate and used in all
discrepancy of more than 15% occurred subsequent years. For a small
between the SMP estimate and the census number of species, the census
figure (Thompson et al. 1997). data alone is used.
Time-series used Various Unsmoothed Various, depending on the original
in England index dataset, all those used are
breeding bird described below
indicators
Statutory Various Population These surveys are designed to be in depth Linear interpolation was used to
Conservation estimates surveys for a particular species and so have estimate annual values for years
Agency and from 2 or sufficient data to allow population trends to be between national surveys.
RSPB Annual more robustly estimated.
Breeding BirdScheme national
(SCARABBS) surveys
Common Bird 1970- Unsmoothed Unsmoothed population time-
Census/Breeding 2018 index series were generated from a log-
Bird Survey link linear regression with Poisson
(BBS) joint trends errors fitted to site x year data
(BTO 2014a).
Breeding Bird 1995- Unsmoothed Data from the BBS surveys were only included Unsmoothed time-series are
Survey (BBS) 2018 index for species for which the BBS methodology is estimated using a similar
appropriate, and which are recorded in on at procedure to the CBC/BBS joint
least 30 BBS squares per year of the survey trends described (BTO 2014a).
period.
Rare Breeding Various, Annual Species were removed where survey effort Linear interpolation was used to
Birds Panel ~1970 - estimate was thought insufficient to generate a reliable estimate any missing data.
(RBBP) 2017 trend. Additionally, species where individuals
were only infrequently present in the UK (taken
as species where the maximum count was 10
or less and the median was three or less),
were removed.
England 1968- Unsmoothed Derived from the Wetland Bird Survey (WeBS). As for BBS time-series
Wintering 2017 index For core species observers record quality of
Waterbird visit (visibility, areas missed) and poor-quality
indicator site visits are excluded. Only sites with a goodlevel of coverage are used (≥ 50% of possible
visits undertaken) Further details of analytical
methods are published (BTO 2017; Maclean &
Ausden 2006).
Table 3: Summary of the analysis methods and criteria for species selection for other taxonomic groups
Group Dataset and Time period Species selection method Analysis method
provider and Data
Type
Moths English moth 1968-2016, Data for 766 moth species were The Generalised Abundance Index (GAI)
trends from TRIM annual analysed using data from Rothamsted methodology proposed by Dennis et al. (2006)
Rothamsted index. Insect Survey light trap network was used to produce English abundance
Insect Survey (Harrower et al. 2019). The 766 trends. This methodology involves estimation
light trap species that were analysed are mostly of standardised annual flight periods curves for
network macro-moths as the majority of micro- each species. These flight curves are used to
(1968 to moths had to be excluded due to estimate the annual total abundance for each
2016) inconsistencies in their recording over site whilst correcting for gaps in the surveying.
the time period. Of the species Poisson regression models, with site and year
analysed 423 species produced explanatory variables, are then fitted to the
reliable trends based on expert estimated annual total abundance values to
assessment of the underlying data and determine the abundance trends and also
the analysis results. yearly abundance indices. Confidence intervals
were produced by bootstrapping (1,000
samples).Moths Butterfly ~2000-2016. Expert opinion (Mark Parsons – Site x year Log-linear Poisson regression
Conservation TRIM annual Butterfly Conservation) was used to models in TRIM (Pannekoek and van Strien
index. judge whether the number of sites 1996) were used.
monitored was sufficient to represent
the national time-series, given each
species’ distribution.
Bats National Bat 1997-2018 A power analysis determined that As BBS time-series (Barlow et al. 2015). In
Monitoring Unsmoothed across all surveys, a sample size of addition, mixed models are used to investigate
Programme index. 30-40 repeat sites (surveyed for more factors that could influence time-series (e.g.,
(Bat than one year) would give sufficient bat detector make, temperature). Over
Conservation data to calculate robust species time- dispersion is a problem for bat detector
Trust) series. This would provide 90% power surveys, where a single bat repeatedly flying
to detect a decline of 25% over 25 past the observer may give rise to a large
years (0.1 sig. level). Borderline cases count of bat passes. Based on the results of
are judged based on the quality of the simulations a binomial model of the proportion
time-series, primarily from the of observation points on each survey where the
confidence limits (Walsh et al. 2001, species was observed is used.
Bat Conservation Trust 2013).
Terrestri Breeding Bird Unsmoothed Data from the BBS surveys were only Unsmoothed time-series are estimated using a
al Survey (BTO) index included for species for which the BBS similar procedure to the CBC/BBS joint trends
Mamma methodology is appropriate, and which described (BTO 2014a).
ls are recorded in on average 30 BBS
squares per year of the survey period.Table 4: Assessment of robustness of monitoring schemes (based on a 2013 assessment at a UK scale) – Data quality = Red >
Orange > Blue
Group Dataset ~Effort Survey design Field method
Moths Rothamsted moth survey (1968-) 80 Consistent, Non-random Light trap
Butterflies Wider countryside butterfly survey 750 Consistent, Random Transect
(2007-)
UK butterfly monitoring scheme (1976-) 1,000 Consistent, Non- random Transect
Mammals National Dormouse Survey (1993-) 300 Consistent, Known sites Nest box search
Breeding bird survey (1995-) 2,400 Consistent, Random Transect
National Bat monitoring program 1,300 Consistent, Random Various, field/ roost
(1997-) counts
Mammals on Roads (2001-) 500 Consistent, Random Transect
Birds Breeding bird survey (1995-) 3,200 Consistent, Random Transect
Common bird census (1970-2000) 300 Consistent, Non-random Territory mappingSeabird monitoring programme, (1986 Species Consistent, Non-random or Total Colony counts
-) seabird censuses (1969, 85, 00) specific
Wetland bird survey (1970-) 3,000 Consistent, Non-random (or almost Site counts
total for some species)
Rare birds breeding panel (1970-) Species Some variation over time, all or most Site counts and
specific known sites individual records
SCARABBS (1974-) Species Consistent, stratified random, bespoke Various, transects
specific for speciesTable 5: Summary of species included in the Abundance of Priority Species Indicator – D6a
Higher group Group Species on Species on S41 with
S41 data
Vertebrates Amphibians 4
Birds 49 44
Fish 48
Mammals 34 6
Reptiles 8
Invertebrates Beetles 75
Butterflies 23 21
Dragonflies 2
Hymenoptera 31
Moths 142 78
True bugs 10
True flies 28
Riverflies 7
Other insects 4
Other Invertebrates 76
Plants Vascular plants 149
Bryophytes 77
Chromists Algae 15Fungi Fungi 64
Lichens 94
Total 940 149
2.4. Indicator Methods
To create the composite index, a hierarchical modelling method for calculating multi-
species indicators within a state-space formulation was used (Freeman et al. 2020). This
method offers some advantages over the more traditional geometric mean method: it is
robust, precise, adaptable to different data types and can cope with the issues often
presented by biological monitoring data, such as varying start dates of datasets, missing
values and zero counts. The resulting index is an estimate of the geometric mean
abundance, set to a value of 100 in the start year (the baseline). Changes subsequent to
this reflect the average change in species abundance; if on average species’ trends
doubled, the indicator would rise to 200, if they halved it would fall to a value of 50. A
smoothing process is used to reduce the impact of between-year fluctuations - such as
those caused by variation in weather - making underlying trends easier to detect. The
smoothing parameter (number of knots) was set to the total number of years divided by
three.
Each species in the indicator was weighted equally. When creating a species indicator
weighting may be used to try to address biases in a dataset, for example, if one taxonomic
group is represented by far more species than another, the latter could be given a higher
weight so that both taxonomic groups contribute equally to the overall indicator.
Complicated weighting can, however, make the meaning and communication of the
indicator less transparent. The main bias on the data is that some taxonomic groups are
not represented at all, which cannot be addressed by weighting. For this reason, and to
ensure clarity of communication, equal weighting was used.
To illustrate the interspecific variation in trends, bar-charts are published alongside the
indicator. These show the percentage of species showing different trends – strong
increase, increase, no change, decrease, strong decrease – over two time periods (Table
6). The long-term period is that since the start of the indicator (1970 in most cases)
although for species entering into the indicator in subsequent years the period is shorter
(the longest available trend is used, as long as it exceeds that used within the short-term
change measure). The short-term period is the last five years of data (e.g., currently 2013-
18). The five trend class thresholds are based on average annual rates of change over the
assessment period and are derived from the rates of decline used to assign species to the
red and amber lists of Birds of Conservation Concern (Eaton et al. 2009). Asymmetric
percentage change thresholds are used to define these classes as they refer to
proportional change, where a doubling of a species index (an increase of 100%) is
counterbalanced by a halving (a decrease of 50%).
14 of 24Table 6: Thresholds used to define individual species’ trends
Category Thresholds Threshold–equivalent
Strong increase Above +2.81% per annum +100% over 25 years
Increase Between +1.16% and +2.81% +33% to + 100% over 25 years
p.a.
No change Between -1.14% and+1.16% -25% to +33% Over 25 years
p.a.
Decrease Between -2.73% and -1.14% -50% to -25% over 25 years
p.a.
Strong decrease Below -2.73% p.a. -50% over 25 years
2.5. The D6a Indicator
2.5.1 Headline D6a Indicator
The headline abundance indicator was generated by combining 149 priority species’ time-
series (D6a) charting changes in relative species abundance using the multi-species
methods described in the preceding section (Figure 1). The final value is 18, indicating that
the average species abundance in 2018 had declined to just 18% of its value in 1970.
15 of 24Figure 1: Change in the relative abundance of priority species in England (D6a), 1970-2018
Notes:
1. The line graph shows the trend with its 95% credible interval (shaded area).
2. The bar chart shows the percentage of species within the indicator that have increased,
decreased or shown no change in abundance based on set thresholds of change (Table
6).
3. All species in the priority species indicator are present on Natural Environmental and
Rural Communities Act 2006 – Section 41 (England).
Source: Bat Conservation Trust, British Trust for Ornithology, Butterfly Conservation, UK
Centre for Ecology & Hydrology, Defra, Joint Nature Conservation Committee, People’s
Trust for Endangered Species, Rothamsted Research, Royal Society for the Protection of
Birds.
2.5.2 Assessment of change – headline indicator
The long-term assessment was made by comparing the change and 95% credible
intervals (CI) of the composite indicators between 1970 and 2018. The final value is 18
(95% CI: 17, 19). If the credible interval is entirely below 100 the time series would be
assessed as decreasing, if it was entirely above 100 the indicator would be assessed as
increasing, if the credible interval spanned 100 the indicator would be assessed as no
significant change. Therefore, both the long-term (1970 to 2018) changes are assessed as
decreases.
To assess the short-term trends, the same approach was applied to the most recent 5-
year (2013-2018) period. If the credible interval for the most recent year (2018) is entirely
below the value for 5-years previous (2013) the time-series would be assessed as
decreasing, if it was entirely above the value for 5-years previous the indicator would be
assessed as increasing, if the credible interval spanned the value for 5-years previous the
16 of 24indicator would be assessed as no significant change. Both the short-term (2013 to 2018)
changes are assessed as decreases.
2.5.3 Change by taxonomic group
The headline indicator (Figure 1) masks variation within and between taxonomic groups.
Figure 2 shows trends for each taxonomic group within D6a. These were generated using
the same methods as the overall indicator.
Figure 2: Change in relative abundance of priority species in England (D6a) by taxonomic
group, 1970-2018
Notes:
1. The line graph shows the trends with their 95% credible intervals (shaded areas) for the
4 taxonomic groups included in the England Priority Species indicator.
2. The bar charts show the percentage of species within the indicator per taxonomic group
that have increased, decreased or shown no change in abundance based on set
thresholds of change (Table 6).
3. All species in the priority species indicator are present on Natural Environmental and
Rural Communities Act 2006 – Section 41 (England).
Source: Bat Conservation Trust, British Trust for Ornithology, Butterfly Conservation, UK
Centre for Ecology & Hydrology, Defra, Joint Nature Conservation Committee, People’s
Trust for Endangered Species, Rothamsted Research, Royal Society for the Protection of
Birds.
17 of 243. D6b Distribution of priority species in
England
3.1. Species List
As noted above, there are 940 species that are considered priorities for nature
conservation within England. D6a is limited to species for which annual estimates of
abundance are available, however biological records are available for a broader set of
taxa than those that have available abundance data. When analysed with occupancy-
detection models, these records provide evidence on changes in species’ distributions
over time. Species groups with suitable data span a wide range of taxonomic groups
including bryophytes, lichens, insects, freshwater invertebrates, and other invertebrates.
While the species included are characteristic of a broad range of habitat types across
England, there are plans to include more species as additional data become available.
3.2. Data Sources
Biological records are observations of species at a particular location and at a particular
time. Most records are made by volunteer recorders and, whilst these data may be
collected following a specific protocol, the majority of records are opportunistic. As the
intensity of recording varies in both space and time (Isaac et al. 2014), it can be difficult to
extract robust trends in species’ distributions from unstructured data. Fortunately, a range
of methods now exist for extracting signals of change using these data (e.g., Szabo et al.
2010; Hill, 2012; Isaac et al. 2014). Of these methods - occupancy-detection models - are
best-able to produce robust trends in occupancy (Isaac et al. 2014). Occupancy-detection
models comprise two hierarchically coupled sub-models: an occupancy sub-model (i.e.,
presence versus absence), and a detection sub-model (i.e., detection versus non-
detection). Together, these sub-models estimate the conditional probability that a species
is detected when present. One distinctive feature of occupancy-detection models is that
data need not be available for every year-site combination in order to infer a species’
occupancy (van Strien et al. 2013).
Occurrence records were extracted at the 1km grid square scale and with a temporal
precision of one day. Data were collated through the Biological Records Centre and
include data from the following recording schemes: Aquatic Heteroptera Recording
Scheme; Bees, Wasps and Ants Recording Society; British Arachnological Society Spider
Recording Scheme; British Bryological Society; British Lichen Society; British Myriapod
and Isopod Group - Millipede Recording Scheme & Centipede Recording Scheme;
Bruchidae & Chrysomelidae Recording Scheme; Conchological Society of Great Britain
and Ireland; Cranefly Recording Scheme; British Dragonfly Society; Empididae, Hybotidae
& Dolichopodidae Recording Scheme; Fungus Gnat Recording Scheme; Gelechiid
Recording Scheme; Grasshopper Recording Scheme; Ground Beetle Recording Scheme;
Hoverfly Recording Scheme; Lacewings and Allies Recording Scheme; National Moth
18 of 24Recording Scheme; Riverfly Recording Schemes: Ephemeroptera, Plecoptera and
Trichoptera; Soldierbeetles and Allies Recording Scheme; Soldierflies and Allies
Recording Scheme; Terrestrial Heteroptera Recording Schemes; UK Ladybird Survey;
Weevil and Bark Beetle Recording Scheme.
Data from between 1970 and 2016 were extracted as this represents the core period of
recording for many of the taxonomic groups. However, some datasets do not cover the
whole period. Since the 2018 indicator the Biological Records Centre has received
updates of the scheme data from some taxonomic groups (Table 7). This has enabled the
improvement of model estimates for certain years (particularly 2016). Note that
approximately 50 of the 105 moth species also appear in D6a.
Table 7: Summary of species’ time-series included in the Priority Indicator (D6b). Only
species in taxonomic groups for which the Biological Records Centre receives data are
presented.
Taxonomic group Number of Number of species Models
species on on S41 list with updated since
S41 list sufficient data for Outhwaite et
Priority Indicator al. (2019)
Ants 5 3 YES
Aquatic Bugs 1 1 NO
Bees 17 13 YES
Bryophytes 77 9 NO
Carabids 13 6 NO
Centipedes 1 0 NO
Craneflies 5 1 NO
Dragonflies 2 1 YES
Empidid & Dolichopodid 4 3 NO
Ephemeroptera 2 1 NO
Fungus Gnats 2 0 NO
Gelechiids 2 0 NO
19 of 24Hoverflies 5 1 NO
Ladybirds 0 0 NO
Leaf and Seed Beetles 11 4 NO
Lichens 86 16 NO
Millipedes 3 0 NO
Molluscs 7 4 NO
Moths 105 93 NO
Neuropterida 1 0 NO
Orthoptera 3 2 NO
Plant Bugs 1 0 NO
Plecoptera 1 1 NO
Shield Bugs 0 0 NO
Soldier Beetles 0 0 NO
Soldierflies 5 4 NO
Spiders 24 9 NO
Trichoptera 3 0 NO
Wasps 5 7 YES
Weevils 2 2 NO
Total 393 181
An occupancy-detection model, following van Strien et al. (2013) and Isaac et al. (2014),
with improvements based on Outhwaite et al. (2018), was applied to all species from those
taxonomic groups for which data were available. For each site-year combination, the
model estimates presence or absence for the species in question given variation in
20 of 24detection probability: from this the proportion of occupied sites, ‘occupancy’ was estimated
for each year. The models are analysed in a Bayesian framework, meaning that, in
addition to point estimates of occupancy, credible intervals (a measure of uncertainty) can
be generated for each species’ time-series based on multiple iterations (here 999) of
model fitting. A detailed description of the occupancy model can be found in Outhwaite et
al. (2019).
3.3. Indicator Methods
From the occupancy model for each species we extract the proportion of occupied sites
within England. We used only those species with at least 10 records in England and which
passed data availability thresholds (Pocock et al. 2019), to ensure reliable inference.
Although, due to the size of the dataset for moths, model quality tests were unavailable, so
only those moth species with greater than or equal to 10 records in England and greater
than or equal to 50 records across all regions (Outhwaite et al. 2019) were included. Given
these data requirements, 181 species contributed to the Priority Species Indicator - D6b
(Table 7).
To create the composite index, a new hierarchical modelling method for calculating multi-
species indicators within a state-space formulation was used (Freeman et al. 2020), as for
D6a above. The method produces an estimate of the annual geometric mean occupancy
across species. The resulting index is the average of the constituent species’ trends, set to
a value of 100 in the start year (the baseline). Changes subsequent to this reflect the
average change in species abundance; if on average species’ trends doubled, the
indicator would rise to 200, if they halved it would fall to a value of 50. A smoothing
process is used to reduce the impact of between-year fluctuations - such as those caused
by variation in weather - making underlying trends easier to detect. The smoothing
parameter (number of knots) was set to the number of years divided by three.
3.4. The D6b Indicator
3.4.1 Headline D6b Indicator
The headline distribution indicator was generated by combining 181 priority species’ time-
series (D6b) charting changes in the proportion of occupied sites using multi-species
methods (Figure 3).
As for D6a, species were grouped into one of five categories based on both their short-
term (over the most recent five years of data) and long-term (all years) average annual
change in occupancy (Table 6).
21 of 24Figure 3: Change in the distribution of priority species in England (D6b), 1970-2016
Notes:
1. The line graph shows the trend with its 95% credible interval (shaded area).
2. The bar chart shows the percentage of species within the indicator that have increased,
decreased or shown no change in abundance based on set thresholds of change (Table
6).
3. All species in the priority species indicator are present on Natural Environmental and
Rural Communities Act 2006 – Section 41 (England).
Source: Biological records data collated by a range of national schemes and analysed by
the UK Centre for Ecology & Hydrology.
3.4.2 Assessment of change – headline indicator
The long-term assessment of the composite indicators was made by comparing the
change and 95% credible intervals (CI) between 1970 and 2016. If the credible interval is
entirely below 100 the time series would be assessed as decreasing, if it was entirely
above 100 the indicator would be assessed as increasing, if the credible interval spanned
100 the indicator would be assessed as no significant change. The same approach is also
applied to a short-term period of the last five years of data (e.g., currently 2011-16).
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