Accredited official statistics

Status of all-species: distribution

Updated 2 December 2025

Applies to England

© Crown copyright 2025

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This publication is available at https://www.gov.uk/government/statistics/england-biodiversity-indicators/status-of-all-species-distribution

Last updated: 2025

Latest data available: 2024

Introduction

This indicator measures change in the number of one kilometre grid squares across England in which all modelled species were recorded in any given year. This is referred to as the ‘occupancy index’ and is effectively equivalent to changes in the distribution of species for which data are available. This indicator is referred to as the ‘all-species’ indicator, as a complement to the all-species abundance indicator. The goal is to cover as many species as possible here, noting that this is not all species in England. The full list of modelled species groups are listed in Table 1. In England there are 5,440 species covered by this indicator. This indicator is identical to the priority species indicator for England, but with an expanded species list, not limited to priority species only. The indicator will increase when species become more widespread on average and decrease when species becomes less widespread on average.

This indicator should be read in conjunction with the all-species relative abundance indicator which provides data on those species for which abundance information is available.

Data for this indicator can be found in the published datafile.

Type of indicator

State Indicator

Type of official statistics

Official statistics in development – indicator under development: The biodiversity indicators project team would welcome feedback on the novel methods used in the development of this indicator. For more information, please visit the UK Statistics Authority’s website on Types of official statistics – UK Statistics Authority.

Assessment of change

As this is an official statistic in development, it has not been assessed.

Key results

This indicator shows the average change in the 5,440 species for which distribution trends are available in England.

By 2024, the index of distribution of all species in England increased to 127%, an increase of 27% of the 1970 value (Figure 1). Over this long-term period, 28% of species showed a strong or weak increase and 23% showed a strong or weak decline (Figure 2).

More recently, between 2019 and 2024, the distribution index declined from 130% to 127%, that is, by 3% of the 2019 value. Over this short-term period, 11% of species showed a strong or weak increase and 27% showed a strong or weak decline.

Figure 1: Change in distribution of 5,440 species in England, 1970 to 2024

Source: biological records data collated by a range of national schemes and local data centres

Notes about Figure 1

  • The line graph shows the smoothed trend (solid line) with variation around the line (shaded area) within which users can be 95% confident that the true value lies (credible interval). The width of the credible interval (CI) is in part determined by the proportion of species in the indicator for which data are available.

Source: biological records data collated by a range of national schemes and local data centres

Notes about Figure 2

  • The bar chart shows the percentage of species within the indicator that have increased, decreased or shown little change in distribution (measured as the proportion of occupied sites), based on set thresholds of change.

Further detail

The trends of the taxonomic groups included within a multi-species indicator are often obscured by its composite nature. Indicator lines have been generated for a number of sub groups using the same method so that the trends for these groups can be seen more clearly (see Figure 3). All groups show an increase in their distribution between the first and last year of the time series, with the Bryophytes showing the strongest increase (61% between 1970 and 2016), followed by the bees, wasps and ants (30% between 1980 and 2023), other taxa (20%% between 1970 and 2024) and moths (13% between 1970 and 2016). The moth indicator has not been updated this year with the latest available data due to issues with computational and model performance (see ‘Development work’ in the Technical annex for more details).

Figure 3: Change in distribution of 5,440 priority species, by taxonomic group, 1970 to 2024

Source: biological records data collated by a range of national schemes and local data centres

Notes about Figure 3

  • The graphs show the smoothed trend (solid line) with its 95% credible interval (shaded area) for each of the taxonomic groups included in the composite indicator. The width of the credible interval is in part determined by the proportion of species in the indicator for which data are available.
  • The figures in brackets show the number of species included in each measure.
  • Other taxa includes a number of insect groups, molluscs and spiders.
  • The indicator for bees, wasps and ants starts in 1980 and the indicator for moths ends in 2016.

There are 5,440 species for which robust quantitative time-series of the proportion of occupied sites available are included in the indicator. These 5,440 species include bees, wasps and ants (491); bryophytes (735); moths (739); and other taxa (3475). The other taxa include a number of insect groups, non-marine molluscs and spiders. See the Technical annex for more detail.

The relative change in distribution of each of these species is measured by the number of one kilometre grid squares across England in which they were recorded – this is referred to as the ‘occupancy index’. The occupancy index will increase when a species becomes more widespread; it will decrease when a species becomes less widespread.

Uncertainty in the species-specific annual occupancy estimates are incorporated into the overall indicator; details of how this was done are included in the Technical annex.

Relevance

Measures of distribution are less sensitive to change than measures of abundance (see all-species abundance indicator). Nonetheless, if a threatened species that has been declining starts to recover, its distribution should stabilise, and may start to increase. If the proportion of species in the indicator that are stable or increasing grows, the indicator will start to decline less steeply. If the proportion declines, it will fall more steeply. Success can therefore be judged by reference to trends in both indicators, as well as other information on other priority species for which there are insufficient data for inclusion in the indicator.

International/domestic reporting

This indicator will feed into the Outcome Indicator Framework (OIF), a set of indicators describing environmental change related to the ten goals within the 25 year Environment Plan. As part of the OIF, this data contributes towards the evidence base used to prepare the annual progress report for the Environmental Improvement Plan. This indicator will contribute to OIF indicator D4: Relative abundance and/or distribution of species.

Acknowledgements

Thank you to the many people who have contributed by collecting, collating and providing the data behind this indicator. Furthermore, we appreciate the many colleagues who have helped produce this indicator.

Technical annex

Background

The measure is a composite indicator of 5,440 species from 30 taxonomic groups (see Species List for a detailed breakdown of the species and groups in the indicator). While not all species in England are included in this indicator, it does provide a broader species context than the priority species indicator, and is more representative of wider species in general. The indicator covers a range of taxonomic groups and will respond to the range of environmental pressures that biodiversity policy aims to address, including land use change, climate change, invasive species and pollution. The short-term assessment of change can be used to assess the impact of recent conservation efforts and policy aimed at halting and reversing species declines. However, natural fluctuations (particularly in invertebrate populations) and short-term response to weather may have a strong influence on the short-term assessment.

Regardless of advances in statistical techniques and the increase in the number of biological records collected, there are likely to be species for which little monitoring or occurrence data are available. Reasons for this include rarity, difficulty of detection, or those for which monitoring methods are unreliable or unavailable. In order for the indicator to be more representative, a method of assessing the changing status of these remaining data poor species would need to be considered.

Data sources and species-specific time series

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 (for example, Szabo et al. 2010; Hill, 2012; Isaac et al. 2014). Of these methods, occupancy-detection models (Isaac et al. 2014) have been applied previously to produce the species occupancy trends behind biodiversity indicators. Occupancy-detection models comprise 2 hierarchically coupled sub-models: an occupancy sub-model (that is, presence versus absence), and a detection sub-model (that is, 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 1 x 1 km grid square scale and with a temporal precision of one day, together these represent a visit to a site on a given date. 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 Recording 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 1970 onwards were extracted, however, some datasets have different start years. The final year also varies between the taxonomic groups and reflects the varying lag times (from data collation to availability) across taxonomic groups.

The rules-of-thumb approach was used to exclude species occupancy models that were likely to be highly uncertain (Pocock et al. 2019). These rules of thumb include dropping rarely recorded species (less than 1 record in every 100 visits) if there were fewer than 3.1 records across the 10% of the best recorded years. More frequently recorded species were also excluded if there were fewer than 6.7 records across the 10% of the best recorded years (Pocock et al. 2019). These exclusion criteria are based on classification trees, selected to balance the rates at which species are excluded when not meeting precision thresholds and included when meeting the precision thresholds. These model quality tests were unavailable for the moth dataset, here species with fewer than 50 records across the UK (Outhwaite et al. 2019, Powney et al. 2019) were excluded.

Table 1: Summary of species’ time-series included in the Priority Species Bayesian measure

Taxonomic group Number of species in the indicator Last year of data
Ants 37 2023
Aquatic Bugs 90 2024
Bees 229 2023
Bryophytes 735 2016
Butterflies 57 2020
Carabids 243 2023
Centipedes 20 2016
Craneflies 200 2023
Dragonflies 49 2024
Empidid & Dolichopodid 360 2021
Ephemeroptera 51 2021
Fungus Gnats 197 2024
Gelechiids 99 2016
Hoverflies 248 2024
Ladybirds 48 2024
Leaf and Seed Beetles 210 2024
Longhorn Beetles 11 2023
Millipedes 32 2016
Molluscs 167 2016
Moths 739 2016
Neuropterida 55 2016
Orthoptera 45 2024
Plecoptera 28 2007
Rove Beetles 204 2022
Soldier Beetles 42 2024
Soldierflies 111 2024
Spiders 541 2018
Trichoptera 168 2024
Wasps 225 2023
Weevils 199 2024
Total 5440 -

We modelled species’ distributions for taxa with sufficient data using two occupancy–detection approaches. For each site–year, we estimated the probability that a site was occupied given variation in detection probability. From this the proportion of occupied sites, ‘occupancy’ was estimated for each year.

  1. Bayesian occupancy model: Following van Strien et al. (2013) and Isaac et al. (2014), with prior specification refined after Outhwaite et al. (2018), with improvements based on Outhwaite et al. (2018), we employed 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).
  2. Frequentist model: Following Dennis et al. (2017), we fitted a logistic regression for each year independently to obtain annual occupancy estimates with 95% confidence intervals. To present a smoothed trend, we drew 1,000 values per year from the normal approximation implied by the occupancy estimate and confidence interval, applied a LOESS smoother across years within each iteration, and summarised the resulting curves (mean and 95% percentiles) as the annual smoothed trend.

We used the frequentist approach for the largest taxonomic group datasets (here, Butterflies only) to resolve memory and runtime barriers.

Methodology

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), as for the priority species abundance and priority species distribution indicator. 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 occupancy; 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 3.

The Freeman method combines the individual species abundance trends taking account of the confidence intervals around the individual trends. However, because the method is Bayesian, it produces credible intervals to show the variability around the combined index, as well as in the trends of individual species.

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.

Assessment of change

Species were grouped into one of 5 categories based on both their short-term (over the most recent 5 years of data) and long-term (all years) mean annual change in occupancy (Table 2).

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%).

The threshold values for each category were based on those of the wild bird indicator; whether an individual species is increasing or decreasing has been decided by its rate of annual change over the time period (long or short) of interest. If the rate of annual change would lead to an occupancy increase or decrease of between 25 per cent and 49 per cent over 25 years, the species is said to have shown a ‘weak increase’ or a ‘weak decline’ respectively. If the rate of annual change would lead to a population increase or decrease of 50 per cent or more over 25 years, the species is said to have shown a ‘strong increase’ or a ‘strong decline’ respectively. These thresholds are used in the Birds of Conservation Concern (PDF 1.6MB) status assessment for birds in the UK.

Category Threshold Long term change
Strong increase An increase of more than 2.81% per annum Equivalent to an increase of more than 100% over 25 years
Increase An increase of between 1.16% and 2.81% per annum Equivalent to an increase of between 33% and 100% over 25 years
No change Change is between +1.16 % and -1.14% per annum Equivalent to a change of between +33% and -25% over 25 years
Decrease A decrease of between 1.14% and 2.73% per annum Equivalent to a decrease of between 25% to 50% over 25 years
Strong decrease A decrease of more than 2.73% per annum Equivalent to a decrease of more than 50% over 25 years

Species list

For a full species list, please see the published datafile.

Development work

The method of fitting the species distribution models is currently implemented in sparta, a Bayesian R package (Lindskou et al., 2024). As the number and size of datasets used in these models grow, it has become increasingly unviable to run, even on High Performance Computing platforms. This particularly affects butterfly and moth datasets (15,900,392 and 28,081,910 records, respectively), but may soon become a problem for other groups such as dragonflies, bees and hoverflies. The UK Centre for Ecology and Hydrology (UKCEH) have explored a range of different options to address this problem, including splitting the model runs up into several chunks and trialling different software, such as NIMBLE (de Valpine et al., 2017). Ultimately, these methods were either slower than the original software or were too unstable for these purposes.

To address the critical challenge of generating indicators for the largest taxon groups, UKCEH investigated using the R package, occti, developed by Dennis et al., (2017). The occti method fits occupancy models using Generalised Linear Models (GLMs) separately by season, and offers a frequentist alternative to the Bayesian framework used in sparta (Dennis et al., 2017). It had been shown in previous research to produce occupancy estimates comparable to those from sparta, while offering faster computation times (Dennis et al., 2017). Completion times of occti model fitting for the larger datasets averaged around three hours - substantially faster than the sparta method.

Comparisons between the outputs from occti and sparta show that occupancy trends were broadly similar across the most common (frequently recorded) species. For rarer, less well-recorded reported species, trends diverged more markedly. A common theme (but not exclusively true for all species) was of much larger uncertainty in the occti model outputs compared to that of sparta. Across taxonomic groups, there were also differences. For dragonflies, sparta estimated a steeper increase in the occupancy index, while for hoverflies, sparta suggested a steeper decline than occti. Notably, occti indices had substantially wider 95% credible intervals. This is likely more realistic given the underlying uncertainty in these occupancy model outputs, and the intrinsic variability of the natural dynamics of these groups and in the raw data.

UKCEH also compared the overall composite indicators derived from the Freeman et al., (2021) method, applied to the 15 most and 15 least reported species (that had at least 50 records across all years). The results showed that the indicators for the most well-recorded dragonfly species were broadly similar between occti and sparta. However, all other indicators showed a high level of difference between occti and sparta, and that this was particularly noticeable for the least well-recorded species subsets. For the least reported species there were noticeably larger confidence intervals in the trends derived from the occti method.

Figure 4: A comparison of index scores derived from sparta (left) and occti (right). The first two rows show indices for the 15 most reported species in dragonflies (first row) and hoverflies (second row), while the third and fourth rows show indices for the 15 least reported species in dragonflies and hoverflies. All panels include 95% credible intervals.

Caveats

The Risk-Of-Bias In Temporal Trends (ROBITT) assessment framework (Boyd et al. 2022), was designed to identify and communicate potential sources of bias given the spatial, environmental, and taxonomic scope of an analysis. These risk-of-bias assessments use a range of metrics and figures to highlight patterns in the sample data, helping to judge whether the model outputs are sensible and reliable given the model assumptions and the underlying data.

Here, light-touch risk-of-bias assessments for all the updated insect datasets and the associated occupancy models in this indicator reveal a number of risks of bias. Notably, there are strong spatiotemporal and taxonomic patterns in sampled occurrence of species across the insect groups. These patterns vary across taxonomic groups, with some groups showing overall increases in recorder effort with the broad spatial pattern of recording remaining consistent, while others show large shifts in the spatial pattern of recording over time. For many groups, the data are strongly clustered in space, often with increased recording effort in southern England. There is widespread variation in the temporal pattern, many groups show a temporal increase in the number of 1 x 1 km records. However, this pattern is not always consistent, some groups have bursts of recording activity with distinct core periods of recording, this is potentially a result of targeted effort prior to the publication of a taxonomic group atlas.

Many groups have a substantial increase in available records in the most recent years. While the occupancy modelling approach used here is designed to control for variation in detectability, it is unclear how the model outputs are impacted by these large shifts in recording effort. For example, any change in the pattern in recording effort that correlates with a potential driver of species occupancy change (i.e. habitat destruction), is likely to cause bias in the underlying species trend outputs. It is likely that apparent changes in species trends may in part reflect variation in recorder activity rather than underlying species change.

As discussed above, occupancy models are useful for detecting species change, where they are designed to handle variation in detectability, such as that associated with an increase in recorder effort. While useful, these models are data hungry, requiring repeat visits to grid cells in the same year to estimate detectability. While no clear threshold for the number of repeat visits is available, a minimum of >4 repeat visits on average has been previously suggested. Here, the average number of repeat visits to a site across all groups was approximately 2.4 with the mean number of repeat visits to sites generally increased with time. As with other aspects of the data, the mean number of repeat visits varied across taxonomic groups ranging from 1.4 for the aquatic bugs and longhorn beetles to a high of 11.9 for the moths. The lack of repeat visits is therefore likely inhibiting the ability of the occupancy model to account for detection bias in the analyses, in turn influencing the ability to detect genuine change.

The performance of the Freeman et al. (2021) method is currently being explored in relation to the species abundance indicator. Many of the development plans for the abundance indicator are also relevant to the use of the Freeman et al. (2021) method in this indicator. The results and recommendations of that work will be considered in future versions of this indicator.

Finally, concerns were raised about the quality and reliability of the occupancy models for Lichens. Consequently, Lichen species were excluded from this indicator. Further work is needed to improve understanding of occupancy model performance using Lichen data. Similar concerns were noted regarding the bryophyte trends derived from occupancy modelling, which ultimately led to the development of an alternative modelling approach for bryophytes and lichens in the most recent State of Nature report.

Development plan

We are keen to hear feedback from users of these statistics, please send your feedback to: biodiversity@defra.gov.uk.

Development plans over the next few years:

  • We will update the remaining groups that were unable to be updated in this publication, such as moths.
  • We will incorporate any developments that arise from the development We will incorporate any developments that arise from the development plan of the species abundance indicators.
  • We will further explore differences between the outputs of the occti and sparta models to decide on an approach that suits all taxonomic groups and has a viable computation time.

References

  • Boyd, R. J., Powney, G. D., Burns, F., Danet, A., Duchenne, F., Grainger, M. J., Jarvis, S. G., Martin, G., Nilsen, E. B., Porcher, E., Stewart, G. B., Wilson, O. J. & Pescott, O. L. (2022). ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology. Methods in Ecology and Evolution, 13, 1497–1507. https://doi.org/10.1111/2041-210X.13857

  • Dennis, E. B., Morgan, B. J. T., Freeman, S. N., Ridout, M. S., Brereton, T. M., Fox, R., Powney, G. D. & Roy, D. B. (2017) Efficient occupancy model-fitting for extensive citizen-science data. PLOS ONE,12, 1-17.

  • de Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Lang, D. T., & Bodik, R. (2017). Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE. Journal of Computational and Graphical Statistics, 26(2), 403–413. https://doi.org/10.1080/10618600.2016.1172487

  • Eaton, M. A., Burns, F., Isaac, N. J. B., Gregory, R. D., August, T. A., Barlow, K. E., … Williams, J. (2015). The priority species indicator: measuring the trends in threatened species in the UK. Biodiversity, 16(2 to 3), 108 to 119. https://doi.org/10.1080/14888386.2015.1068222

  • Fewster, R. M., Buckland, S. T., Siriwardena, G. M., Baillie, S. R., and Wilson, J. D. (2000). Analysis of population trends for farmland birds using generalized additive models. Ecology, 81(7), 1970 to 1984. doi:10.1890/0012-9658(2000)081[1970:AOPTFF]2.0.CO;2

  • Freeman, S.N., Isaac, N.J.B., Besbeas, P. et al. A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data. JABES 26, 71 to 89 (2021). https://doi.org/10.1007/s13253-020-00410-6

  • Hill, M.O. (2012), Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3: 195-205. https://doi.org/10.1111/j.2041-210X.2011.00146.x

  • Isaac, N. J. B., August, T. A., Harrower, C. and Roy, D. B. (2013). Trends in the Distribution of UK native species 1970 to 2010. Preliminary report to JNCC. JNCC Report No 488.

  • Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. and Roy, D. B. (2014a). Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5(10), 1052 to 1060. https://doi.org/10.1111/2041-210X.12254

  • Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. and Roy, D. B. (2014b). Extracting robust trends in species’ distributions from unstructured opportunistic data: a comparison of methods. BioRXiv. https://doi.org/10.1101/006999

  • Lindskou, M., Tvedebrink, T., Eriksen, S., Højsgaard, S., & Morling, N. (2024). jti and sparta: Time and Space Efficient Packages For Model Based Prediction in Large Bayesian Networks. Journal of Statistical Software, 1-24. https://doi.org/10.18637/jss.v111.i02

  • Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D. and Isaac N. J. B. (2018) Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. Ecological Indicators, 93, pp.333 to 343

  • Outhwaite, C.L., Powney, G.D., August, T.A., Chandler, R.E., Rorke, S., Pescott, O.L., … Isaac, N.J.B. (2019) Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970 to 2015. Scientific Data 6, p.259.

  • Pocock, M. J. O., Logie, M. W., Isaac, N. J. B., Outhwaite, C. L., and August, T. (2019). Rapid assessment of the suitability of multi-species citizen science datasets for occupancy trend analysis. BioRxiv. doi: 10.1101/813626

  • Powney, G.D., Carvell, C., Edwards, M., Morris, R.K.A., Roy, H.E., Woodcock, B.A. and Isaac, N.J.B. (2019) Widespread losses of pollinating insects in Britain. Nature Communications, 10, 1018

  • Szabo, J.K., Vesk, P.A., Baxter, P.W.J. and Possingham, H.P. (2010), Regional avian species declines estimated from volunteer-collected long-term data using List Length Analysis. Ecological Applications, 20: 2157-2169. https://doi.org/10.1890/09-0877.1

  • Van Strien, A. J., van Swaay, C. A. M. and Termaat, T. (2013). Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. Journal of Applied Ecology, 50(6), 1450 to 1458. https://doi.org/10.1111/1365-2664.12158

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