Nick IsaacBiological Records CentreCentre for Ecology & Hydrology

Interpreting biodiversity under diverse syndromes of recording behaviour

Nick IsaacBiological Records CentreCentre for Ecology & Hydrology

Extracting trends from biological recording data

Is biological recording fit for purpose?

• What is the purpose?

• What data are available?

• What are the problem issues?

• What tools might provide a solution?

• What is the purpose?• Describing species’ distributions• Detecting and attributing change over time• Identifying novelties

Is biological recording fit for purpose?

Mike MajerusWikipedia Commons

FERA

GBNSS

GBNSS

Biological records data

How do we interpret the gaps?

NBN lists 35 data sources:• Individual records• Regional recording projects• Co-ordinated national surveys

Published Atlases

The primary tool for understanding UK biodiversity

Authoritative summary of the current state of knowledge

A snapshot of species’ distributions

Perring, F H, & Walters, S M, eds 1962 Atlas of the British Flora. Thomas Nelson & Sons, London

Published Atlases

Stock & change in distribution

• Repeat atlases allow an assessment of change over time

• Prickly Lettuce (Lactuca serriola) has expanded northwest since 1970

Repeat atlases: plants & birds, butterflies

Biodiversity change using atlases

‘Square counts‘ on repeat atlases reveal which species are increasing vs decreasing

Greatest losses occurred among butterflies, then birds

Thomas, JA et al. (2004). Comparative losses of British butterflies, birds, and plants and the global extinction crisis. Science, 303(5665), 1879–81

Where are we now?

• Atlases provide a rather static view of biodiversity

• The unstructured nature of the data makes square counting unreliable

• Increasing demand for quantitative information

• New methods for estimating trends are being developed

Detecting and attributing change

Trends in the distribution of 8 common ladybirds

A majority show substantial negative response to arrival of Harlequin ladybird

Similar patterns in GB & Belgium

Roy, HE, Adriaens, T, Isaac, NJB et al. (2012). Invasive alien predator causes rapid declines of native European ladybirds. Diversity and Distributions, 18(7), 717–725

Mike Majerus

Past, present and futureBiodiversity IndicatorsAttributing changeDescribing change

Talk outline

• Extracting trends from Biological records data• Problems & possible solutions

• Comparison of candidate methods• Simulations of recording behaviour• Which methods are useful for detecting trends?

• Applications: which species are declining?• Trends in Odonata 1970-2011• Biodiversity Indicator

Recording intensity varies among taxa

Extracting trends from biological records

Recording intensity has increased over time

Telfer’s Change Index

Telfer, MG, Preston, CD & Rothery, P (2002). A general method for measuring relative change in range size from biological atlas data. Biological Conservation, 107(1), 99–109

• Compares two time-periods that differ in recording intensity &/or geographic coverage

Ball’s Visit Rate model

Ball, S, Morris, R, Rotheray, G, & Watt, K (2011). Atlas of the Hoverflies of Great Britain (Diptera, Syrphidae).

Most lists are incomplete

For most groups, ~50% of visits produce ‘incidental records’

Lists lengths are not constant over time

Mixed model

Most records come from a few recorders

Bryophytes: 18Myriapods: 11Moths: 102Orthoptera: 39

Spatial pattern of recording behaviour

Orthoptera 1970-2011: top 4 recorders made 14% of all visits

Hill’s Frescalo method

Hill, MO (2011). Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3(1), 195–205.

Red = under-recordedWhite = well-recorded

Frescalo estimates the recording intensity of each grid cell

Hill’s Frescalo method

By estimating recording intensity, Frescalo calculates the number of species that ‘should’ be in each grid cell.

Hill’s Frescalo method

Hill, MO (2011). Local frequency as a key to interpreting species occurrence data when recording effort is not known. Methods in Ecology and Evolution, 3(1), 195–205.

Occupancy modelling: a panacea?

van Strien, A, van Swaay, C, & Kéry, M (2011). Metapopulation dynamics in the butterfly Hipparchia semele changed decades before occupancy declined in the Netherlands. Ecological Applications, 21(7), 2510–2520

Gateshead birders

Talk outline

• Extracting trends from Biological records data• Problems & possible solutions

• Comparison of candidate methods• Simulations of recording behaviour• Which methods are useful for detecting trends?

• Applications: which species are declining?• Trends in Odonata 1970-2011• Biodiversity Indicator

Recorder behaviour

Estimate trendsRaw data

Simulations

How can we estimate trends?

Simulations

Aims:

1. To compare the performance of different methods for estimating range change under realistic scenarios of recorder behaviour

2. To discard methods that are inappropriate

3. To derive rules of thumb for when other methods are appropriate

Simulation overview

• 1000 sites (no spatial information)

• 1 focal species + 25 others

• Focal species occupies 50% sites

• Impose different patterns of recording

• Run for 10 years

• Estimate trends using different methods

Simulation patterns of recording

• A: Control scenario: even recording• Equal probability of sites being visited

• B: Increasing recording intensity• Growth in number of visits

• C1: Incomplete recording (even)• A fixed proportion of Visits produce short lists

• C2: incomplete recording (increasing)• Proportion of short lists increases over time

Type I Error Rates

Type I Error Rates

AEven

RecordingChange Index 0.027

nRecords 0.024

Visit Rate 0.046

MM2sp 0.061

MM3sp 0.058

MM4sp 0.058

Frescalo 0.040

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

Change Index 0.027 0.026 0.033 0.037

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

nRecords 0.024 0.993 0.042 0.609

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

Visit Rate 0.046 0.060 0.059 0.675

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

MM2sp 0.061 0.079 0.053 0.195

MM3sp 0.058 0.079 0.060 0.089

MM4sp 0.058 0.073 0.066 0.049

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

Frescalo 0.040 0.164 0.036 0.060

Type I Error Rates

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

Change Index 0.027 0.026 0.033 0.037

nRecords 0.024 0.993 0.042 0.609

Visit Rate 0.046 0.060 0.059 0.675

MM2sp 0.061 0.079 0.053 0.195

MM3sp 0.058 0.079 0.060 0.089

MM4sp 0.058 0.073 0.066 0.049

Frescalo 0.040 0.164 0.036 0.060

Power to detect a genuine decline

AEven

RecordingChange Index 0.574

nRecords 0.642

Visit Rate 0.739

MM2sp 0.665

MM3sp 0.649

MM4sp 0.615

Frescalo 0.612

Power to detect a genuine decline

AEven

Recording

BIncreasing Intensity

C1 Incomplete

even

C2Incomplete increasing

Change Index 0.574 0.461 0.37 0.316

nRecords 0.642 0 0.449 0.979

Visit Rate 0.739 0.606 0.507 0.985

MM2sp 0.665 0.424 0.319 0.685

MM3sp 0.649 0.408 0.271 0.463

MM4sp 0.615 0.363 0.211 0.208

Frescalo 0.612 0.768 0.34 0.308

Simulations: Conclusions

• The simulation provides a framework for comparing methods under a range of recording scenarios

• The Mixed model method performs best so far (Frescalo & Occupancy results pending)

• In the best recording scenario, a decline of 30% was detected in 60% of simulated datasets

Talk outline

• Extracting trends from Biological records data• Problems & possible solutions

• Comparison of candidate methods• Simulations of recording behaviour• Which methods are useful for detecting trends?

• Applications: which species are declining?• Trends in Odonata 1970-2011• Biodiversity Indicator

Odonata trends 1970-2011

• Broad agreement between methods

• 14/32 species show significant increases under both methods

• 2/32 show significant decreases under both methods

Odonata trends: winners

Wikipedia Commons

Small red-eyed Damselfly(Erythromma viridulum)

Scarce chaser(Libellula fulva)

Emperor Dragonfly(Anax imperator)

Odonata trends: losers

Variable damselfly(Coenagrion pulchellum)

Blue-tailed Damselfly(Ischnura elegans)

Common Blue Damselfly(Enallagma cyathigerum)

Odonata Indicator

Biological Recording for the 21st Century

• We have the tools to model biodiversity change using unstructured biological records

• This is only possible if records continue to be submitted to the database!

• We could be smarter about data collection

• We’re only just beginning to exploit the potential of biological recording data• Indicators, Red Listing, ecosystem service provision,

targeting Agri-environment schemes

AcknowledgmentsTom AugustColin HarrowerDavid Roy, Helen Roy, Michael Pocock, Gary Powney, Chris PrestonMark HillArco van Strien