Nick Isaac Arco van Strien, Tom August, Gary Powney & David Roy - - PowerPoint PPT Presentation

What can be done to remove biases in volunteer-gathered biological records?

Nick Isaac

Arco van Strien, Tom August, Gary Powney & David Roy

Talk Outline

• The Problem
• Solutions?
• Testing the Solutions
• The Way Ahead
• Tools
• Applications

20 40 60 80 100 120 1970 1980 1990 2000 2010

Number of sites

Year

Problem: ad hoc recording is biased

Problem: ad hoc recording is biased

• in time
• in space
• detectability
• effort per visit

1 10 100 1000 10000 100000 1000000

1970 1980 1990 2000 2010

Number of records

Butterflies Bryophyte Orthoptera Myriapod Isopods Coleoptera Moths Bees Wasps

Effort Number of Species

Most lists are incomplete

For most groups, ~50% of ‘lists’ are single species For many groups, the prevalence of short lists varies systematically over time

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Proportion of all visits > 3 species 3 species 2 species Single species

Solutions?

• Aggregation
• Data Selection methods
• Correction for sampling effort
• Modelling the data collection process

Aggregation into Atlas periods

Selection methods

• Remove the bias, leave the signal
• The ‘well-sampled set’
• Threshold number of species
• Threshold number of years
• Untested assumption
• Loss of power?

Well-sampled sites for Dragonflies

Correction for sampling effort

• by time period
• Telfer’s Change index
• per year
• Ball’s ‘Reporting Rate’ method
• per visit
• Szabo’s ‘List Length’ method
• in space (per grid cell or neighbourhood)
• Hill’s ‘Frescalo’ method

Correction: 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-recorded White = well-recorded

Frescalo estimates the recording intensity of each grid cell

Hill’s Frescalo method

Frescalo estimates which species ‘should’ be in each grid cell if well-sampled Trends can be modelled as changes in ‘relative recording rate’

Occupancy: modelling data collection

Extant Extinct Occupancy (unobserved)

Separation of “state” and “data generation” processes into separate submodels permits (annual) estimation of

• ccupancy and detection

Observations Data generation process Year 1 Year 2 Year 4 Year 3 Year 5

Testing the solutions by simulation

• Generate records resembling NBN-type datasets
• 1000 sites, 25 species, 10 years
• Realistic scenarios of recorder behaviour
• Parameterized from UK and Dutch datasets
• Formally compare methods for estimating trends
• Type I error rate when no trend exists
• Power to detect genuine trend

Simulation results: Type I error rates

Isaac et al (in review) Methods in Ecology & Evolution

Simulation results

• Simple ‘correction’ models fail easily
• Frescalo performs well but subjective to apply
• Selection methods are robust but less powerful
• Occupancy most promising overall
• Least often wrong
• Most powerful overall
• … but a problem with spatially-biased sampling

Isaac et al (in review) Methods in Ecology & Evolution

The Way Ahead

• Occupancy + site-selection criterion?
• Pdetect ≈ ฀List Length, Julian Date,

Previously Recorded, …….}

• If we knew more about the bias, we could

model it

• A little bit of meta data would go a long way
• Visit-based records are crucial

Tools

• An easy way to record
• Great potential for

harvesting meta-data

https://github.com/BiologicalRecordsCentre http://bit.ly/18wTrrK

Applications

• Identifying drivers of change in native ladybirds
• Overview of trends in UK biodiversity
• Developing a biodiversity indicator

Identifying drivers of change

Declines in native ladybirds are attributable to the arrival of the invasive Harlequin ladybird Similar patterns across 8 native species in both GB & Belgium

Roy et al (2012) Diversity & Distributions, 18: 717–725

Mike Majerus davidkennardphotography.com

Trends in British Biodiversity 1990-2000

• Good news: Median change +2.4%
• Bad news: >1000 species would qualify as VU or worse

The Priority Species Indicator

Source: Biodiversity in Your Pocket 2013

20 40 60 80 100 120 1970 1975 1980 1985 1990 1995 2000 2005 2010 Index (1970 = 100)

95% Confidence interval max

95% Confidence interval min United Kingdom 10 20 30 40 50 60 70 80 90 100 Long term Percentage of species Decline Increase

20 40 60 80 100 120 1970 1975 1980 1985 1990 1995 2000 2005 2010 Index (1970 = 100)

95% Confidence interval max

95% Confidence interval min United Kingdom 10 20 30 40 50 60 70 80 90 100 Long term Short term Percentage of species Decline Increase

Conclusions

We shouldn’t remove the bias but model it Occupancy models are especially promising A little bit of meta-data would go a long way = a vast untapped resource

Simulated patterns of recording

• 1. Even recording: random sampling
• 2. Doubling intensity: number of visits doubles
• 3. Doubling with biased sampling wrt focal sites
• 4. Incomplete recording (growth in short lists)
• 5. Detection increasing: focal species becomes more

detectable

• 6. Non-focal declines

Robust to:

• Changes in effort over time
• Change in spatial pattern of recording
• Changes in community composition
• Temporally & spatially precise
• Can easily add covariates

The ‘well-sampled sites’ model

Assumptions/Caveats:

• Groups are recorded collectively, as an assemblage
• Effort per visit has not changed over time
• Detectability per visit is constant over time
• Well-sampled sites are representative

Well-sampled sites for Dragonflies