UK Biodiversity Indicators 2014

This document supportsD1c. Biodiversity and ecosystem services:Status of pollinating insects

Technical document: Experimental statistic on the status of pollinating insects

Gary D. Powney, Tom A. August, Colin A. Harrower,
Charlotte Outhwaite, Nick J. B. Isaac

For further information on D1c. Biodiversity and ecosystem services: Status of pollinating insects visit http://www.jncc.defra.gov.uk/page-6851

For further information on the UK Biodiversity Indicators visit
http://www.jncc.defra.gov.uk/page-1824


D1c. Biodiversity and Ecosystem Services – experimental statistic on the status of pollinating insects – technical document – October 2014

Gary D. Powney, Tom A. August, Colin A. Harrower, Charlotte Outhwaite, Nick J. B. Isaac

INTRODUCTION

Humans derive a wide variety of benefits from the natural world (for example, regulation of air quality; improved agricultural yield; nutrient cycling etc.). Given the widespread decline in biodiversity, a robust system for reporting on the status of the organisms that provide such ecosystem services is urgently needed. Additionally, in order to report on progress towards Strategic Goal D “Enhance the benefits to all from biodiversity and ecosystem services” of the Aichi Targets from the Convention on Biological Diversity (http://www.cbd.int/sp/targets/), there is a need to develop an accurate metric of biodiversity and ecosystem service status. Temporal trends in such a metric can be used to monitor long-term change, and can assess the effectiveness of conservation strategies aimed at halting biodiversity loss and improving ecosystem service provision. Here we use pollination as a case study to present a novel ecosystem service indicator that utilises citizen science data to examine long-term trends in UK.

Pollination is a vital ecosystem service that benefits agricultural and horticultural production, and is essential for maintaining wild flower biodiversity. By improving the yield, quality and resilience of crops, insect pollination has been valued at £400 million per year to the UK economy (POST, 2010), and is responsible for 35% of global crop production (Klein et al., 2007). There is growing concern regarding the population status of insect pollinators and in turn the pollination service they provide (Potts et al., 2010; Garratt et al., 2014). As with most other areas of biodiversity, the main threats to pollinators include habitat loss, environmental pollution, climate change and the spread of alien species (Klein et al., 2007; Potts et al., 2010; Vanbergen & The Insect Pollinators Initiative, 2013). The widespread application of pesticides is also perceived as a major threat to pollinator diversity (Brittain et al., 2010). In order for governments to act upon these threats they need robust metrics on the national-scale status of pollinators and pollination. Deriving such a metric has previously been limited by the availability of suitable data and analytical techniques. With the increase in citizen science, the availability of large-scale biological record data has increased (Silvertown, 2009). Such data are collected without a standardized survey protocol and therefore extracting reliable trends from them can be difficult. However, with recent analytical advances it is now possible to estimate reliable trends from such data (van Strien et al., 2013; Isaac et al., 2014).

METHODS

Data sources

Occurrence records of bee species within 1km grid cells in the UK were extracted from the Bees, Wasps and Ants Recording Society (BWARS) biological records database. The time-period used for the indicator was 1980 to 2010, as this represents a core period of recording bee species in the UK. The indicator is based on 216 bee species and therefore covers the vast majority of the UK bee fauna. Species that had undergone taxonomic changes or had taxonomy issues during the time frame of the indicator were excluded from the analysis. The species included in the indicator are listed in appendix 1.

Indicator calculation

The data used to produce the indicator were not collected using a standardised protocol, but instead are a collection of unstructured biological observations collected by a large network of volunteer recorders. Such data tend to contain many forms of sampling bias and noise, making it hard to detect genuine signals of change (Tingley & Beissinger, 2009; Hassall & Thompson, 2010; Isaac et al., 2014). Recent studies have highlighted the value of Bayesian occupancy models for estimating species occurrence in the presence of imperfect detection (van Strien et al., 2013; Isaac et al., 2014). This approach uses two hierarchically coupled sub-models: an occupancy sub-model (i.e. presence verses absence), and a detection sub-model (i.e. detection verses non-detection). Together these sub-models estimate the conditional probability that a species is detected when present. The approach is based on capture-recapture theory, where replicated visits within a season are used to estimate detection probability (MacKenzie, 2006; van Strien et al., 2013). The complex Bayesian occupancy model of Isaac et al. (2014) was applied to all 216 species of bee, to estimate the annual proportion of sites occupied while accounting for variation in detection probability and to fit a linear model to the annual occupancy estimates. Each species-specific time-series was expressed as a proportion of the occupancy estimate in the first year (1980), additionally the time-series were scaled so that the occupancy estimate in the first year was set to 100 for each species. The annual index for the pollinator indicator were calculated as the geometric mean of these annual scaled occupancy estimates across all species. Each species was given equal weighting when calculating the mean. The 95% confidence intervals of the geometric mean were calculated via bootstrapping (Buckland et al., 2005). In each iteration (n = 10,000) a random sample of species were selected with replication and the annual geometric means were calculated. The 2.5 and 97.5 percentile from these annual geometric means were used as the 95% confidence intervals for the indicator. The direction of the slope of the linear model fitted to the annual occupancy estimates was used to classify species as increasing or declining.

RESULTS

Figure 1 Change in the relative occupancy of bees in the UK between 1980 and 2010. Shaded region shows the 95% CI of the annual geometric means.

The indicator illustrates changes in bee species occupancy in the UK between 1980 and 2010. By 2010, the pollinator index had declined to 62 (95% CI = 54 and 70). More specifically, there was a steady decline in relative pollinator occupancy from 1980 to a low value of 65 in 2001. This was followed by a short recovery where the relative trend in occupancy reached 85 in 2006, then a rapid decline to 62 in 2010. The inter-annual variability in the index is likely to be partly explained by inter-annual variation in weather conditions, as bees tend to respond positively to temperature but negatively to rainfall. However, despite this inter-annual variation the overall trend remains downward. This indicator is a composite measure across 216 bee species and therefore covers the vast majority of the UK bee fauna. Between 1980 and 2010 approximately 70 per cent of the species included in this study had declined (regardless of magnitude, and whether the confidence intervals of the decline spanned zero).

FUTURE WORK

Bees are key pollinators and are presented here as an indicator of the overall trend in pollinators. Other taxonomic groups (e.g. hoverflies) can provide pollination services but are not yet included in the indicator. Future updates of the pollinator indicator will include trends from other taxonomic groups known to provide pollination services.

The development of a method to incorporate the uncertainty surrounding the species-specific annual occupancy estimates, into the overall indicator and its associated confidence intervals is an important future challenge. At present, uncertainty was only captured for the geometric mean across all species, the species-specific uncertainty in the annual occupancy estimates was not considered. As mentioned above, inter-annual variation in weather is likely to explain some of the inter-annual variation in the indicator. Future investigation into this relationship will examine the potential for producing an indicator that accounts for the short-term impact of weather.

Finally all species were given equal weight in the pollinator indicator, effectively the indicator assumes all species are equally valuable in terms of their contribution to pollination services. However, contribution to pollination is known to vary between species and is dependent on inherent life history and ecological characteristics of the species, but also on total population abundance (Breeze et al., 2011; Woodcock et al., 2013). Future work will examine the feasibility of weighting the geometric mean to take account of this variation in species importance as pollinators.

REFERENCES

Breeze, T.D., Bailey, A.P., Balcombe, K.G. & Potts, S.G. (2011) Pollination services in the UK: How important are honeybees? Agriculture, Ecosystems & Environment, 142, 137–143.

Brittain, C.A., Vighi, M., Bommarco, R., Settele, J. & Potts, S.G. (2010) Impacts of a pesticide on pollinator species richness at different spatial scales. Basic and Applied Ecology, 11, 106–115.

Buckland, S.T., Magurran, A.E., Green, R.E. & Fewster, R.M. (2005) Monitoring change in biodiversity through composite indices. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360, 243–54.

Garratt, M.P.D., Truslove, C.L., Coston, D.J., Evans, R.L., Moss, E.D., Dodson, C., Jenner, N., Biesmeijer, J.C. & Potts, S.G. (2014) Pollination deficits in UK apple orchards. Journal of Pollination Ecology, 12, 9–14.

Hassall, C. & Thompson, D.J. (2010) Accounting for recorder effort in the detection of range shifts from historical data. Methods in Ecology and Evolution, 1, 343–350.

Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. & Roy, D.B. (2014) Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5, 1052–1060.

Klein, A.-M., Vaissière, B.E., Cane, J.H., Steffan-Dewenter, I., Cunningham, S.A., Kremen, C. & Tscharntke, T. (2007) Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society, Biological Sciences, 274, 303–13.

MacKenzie, D.I. (2006) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Academic Press, Burlington, Massachusetts, USA.

POST (2010) Insect Pollination, London.

Potts, S.G., Biesmeijer, J.C., Kremen, C., Neumann, P., Schweiger, O. & Kunin, W.E. (2010) Global pollinator declines: trends, impacts and drivers. Trends in Ecology & Evolution, 25, 345–53.

Silvertown, J. (2009) A new dawn for citizen science. Trends in Ecology & Evolution, 24, 467–471.

Van Strien, A.J., van Swaay, C.A.M. & 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, 1450–1458.

Tingley, M.W. & Beissinger, S.R. (2009) Detecting range shifts from historical species occurrences: new perspectives on old data. Trends in Ecology & Evolution, 24, 625–633.

Vanbergen, A.J. & The Insect Pollinators Initiative. (2013) Threats to an ecosystem service: pressures on pollinators. Frontiers in Ecology and the Environment, 11, 251–259.

Woodcock, B.A., Edwards, M., Redhead, J., Meek, W.R., Nuttall, P., Falk, S., Nowakowski, M. & Pywell, R.F. (2013) Crop flower visitation by honeybees, bumblebees and solitary bees: Behavioural differences and diversity responses to landscape. Agriculture, Ecosystems & Environment, 171, 1–8.

APPENDIX 1. The list of species included in the indicator

Andrena alfkenella
Andrena angustior
Andrena apicata
Andrena argentata
Andrena barbilabris
Andrena bicolor
Andrena bimaculata
Andrena bucephala
Andrena carantonica
Andrena chrysosceles
Andrena cineraria
Andrena clarkella
Andrena coitana
Andrena congruens
Andrena denticulata
Andrena dorsata
Andrena falsifica
Andrena flavipes
Andrena florea
Andrena fucata
Andrena fulva
Andrena fulvago
Andrena fuscipes
Andrena haemorrhoa
Andrena hattorfiana
Andrena helvola
Andrena humilis
Andrena labialis
Andrena labiata
Andrena lapponica
Andrena marginata
Andrena minutula
Andrena minutuloides
Andrena nigriceps
Andrena nigroaenea
Andrena nitida
Andrena nitidiuscula
Andrena niveata
Andrena ovatula
Andrena praecox
Andrena proxima
Andrena ruficrus
Andrena semilaevis
Andrena similis
Andrena simillima
Andrena subopaca
Andrena synadelpha
Andrena tarsata
Andrena thoracica
Andrena tibialis
Andrena trimmerana
Andrena varians
Andrena wilkella
Anthidium manicatum
Anthophora bimaculata
Anthophora furcata
Anthophora plumipes
Anthophora quadrimaculata
Anthophora retusa
Apis mellifera
Bombus barbutellus
Bombus bohemicus
Bombus campestris
Bombus distinguendus
Bombus hortorum
Bombus humilis
Bombus hypnorum
Bombus jonellus
Bombus lapidarius
Bombus magnus
Bombus muscorum
Bombus pascuorum
Bombus pratorum
Bombus ruderarius
Bombus ruderatus
Bombus rupestris
Bombus soroeensis
Bombus sylvarum
Bombus sylvestris
Bombus terrestris
Bombus vestalis
Ceratina cyanea
Chelostoma campanularum
Chelostoma florisomne
Coelioxys conoidea
Coelioxys elongata
Coelioxys inermis
Coelioxys mandibularis
Coelioxys rufescens
Colletes cunicularius
Colletes daviesanus
Colletes floralis
Colletes fodiens
Colletes halophilus
Colletes hederae
Colletes marginatus
Colletes similis
Colletes succinctus
Dasypoda hirtipes
Epeolus cruciger
Epeolus variegatus
Eucera longicornis
Halictus confusus
Halictus rubicundus
Halictus scabiosae
Halictus tumulorum
Heriades truncorum
Hoplitis claviventris
Hylaeus annularis
Hylaeus brevicornis
Hylaeus communis
Hylaeus confusus
Hylaeus cornutus
Hylaeus dilatatus
Hylaeus hyalinatus
Hylaeus incongruus
Hylaeus pectoralis
Hylaeus pictipes
Hylaeus signatus
Lasioglossum albipes
Lasioglossum angusticeps
Lasioglossum brevicorne
Lasioglossum calceatum
Lasioglossum cupromicans
Lasioglossum fratellum
Lasioglossum fulvicorne
Lasioglossum laevigatum
Lasioglossum laticeps
Lasioglossum lativentre
Lasioglossum leucopus
Lasioglossum leucozonium
Lasioglossum malachurum
Lasioglossum minutissimum
Lasioglossum morio
Lasioglossum nitidiusculum
Lasioglossum parvulum
Lasioglossum pauperatum
Lasioglossum pauxillum
Lasioglossum prasinum
Lasioglossum punctatissimum
Lasioglossum puncticolle
Lasioglossum quadrinotatum
Lasioglossum rufitarse
Lasioglossum semilucens
Lasioglossum smeathmanellum
Lasioglossum villosulum
Lasioglossum xanthopus
Lasioglossum zonulum
Macropis europaea
Megachile centuncularis
Megachile circumcincta
Megachile leachella
Megachile ligniseca
Megachile maritima
Megachile versicolor
Megachile willughbiella
Melecta albifrons
Melitta dimidiata
Melitta haemorrhoidalis
Melitta leporina
Melitta tricincta
Nomada argentata
Nomada baccata
Nomada conjungens
Nomada fabriciana
Nomada ferruginata
Nomada flava
Nomada flavoguttata
Nomada flavopicta
Nomada fucata
Nomada fulvicornis
Nomada goodeniana
Nomada guttulata
Nomada hirtipes
Nomada integra
Nomada lathburiana
Nomada leucophthalma
Nomada marshamella