Biodiversity Rapid Assessment Techniques:. MacKinnon Lists Biodiversity Rapid Assessment Techniques: MacKinnon Lists Aidan Maccormick University of St Andrews

The Conservation Challenge Good quality biodiversity information Protected areas design Species conservation assessments Monitoring population trends Many sites potentially important for conservation lack basic biodiversity information - E.g. Species lists, distribution, habitat relationships and distributions in abundance What’s the best method for efficiently and accurately collecting biodiversity data? - Collected relatively rapidly - Comparable between sites - Whole communities

Potential Solutions Directly count all individuals? - Theoretically the perfect solution but often practically impossible (size of sites, time, costs etc.) Count just individuals detected? - More practical but confounded by effort, changes in detectability over time etc. Standardise effort while counting? Encounter/capture rate: e.g. individuals per day, per hour, per trap etc - More comparable but still sensitive to observer expertise , changes in detectability over time etc.

Potential solutions Estimate absolute abundance & calculate population? - Possible and potentially accurate with various standard methodologies (Point Counts – Line Transects) - But very time consuming, some measurements can be problematic and often assumptions aren’t realistic so potentially inaccurate Estimate relative abundance of species detected? - Control for differences between observers - Control for differences in detectability - However reliability, consistency and repeatability have never been tested so are results comparable?

Mackinnon Lists Technique Developed for birds in tropical forests (MacKinnon and Philips 1993) Based on assembling list of the first 10 species detected, the next 10 species detected etc Relative abundance measured by proportion of samples (lists) a species occurs in Each 10 species lists is effectively a time and space independent sample of the animal community present Potential advantages: - Robust to differences between observers - Very quick - Throughout the day (can use in different periods of activity But untested

Testing MacKinnon Lists South America (Bolivia) 2001 Montane rainforest, August – September Test: Reliability as a technique for birds 6 observers (varying experience and skill) 2 time periods (early Spring Vs Summer) RESIDENT SPs Each observer walked 10km of transects ONCE in EACH time period

Results: Reliability? Within observers: How repeatable are estimates of abundance as detectability changes over time? Reliability between period 1 & 2 = 0.91 (Range 0.816 to 0.949) Between observers: How comparable are species estimates of abundance? Period 1 Reliability = 0.926 (0.906 to 0.943: 95% CI) Period 2 Reliability = 0.938 (0.921 to 0.952: 95% CI) Cronbach’s Alpha Reliability Analysis based on Inter-class Correlation Coefficients (ICCs) Values range from 0 to 1 (Perfect). >0.9 very high reliability (Cohen & Holliday 1996) Also Very Quick: 8 days of use gave us relative abundance estimates for 74% of birds recorded in total of 50 days.

Cautions with this methodology Cannot compare between species If species A is on 75% of lists it doesn’t mean it is more common than sps B which appears of 35% of lists - Differences in detectability Does not replace absolute abundance methods if exact population size is needed Вертишейка Коростель Белая лазоревка © Steve Round © Tom Gradwell © Askar Isabekov

Other taxonomic groups Butterflies - Either by visual identification along transects or a combination of visual identification and netting along transects (UK Butterfly Monitoring System) Amphibians & Reptiles - Ideal for use with nocturnal/diuranl Visual Encounter Surveys Fish - Could be used with trapping or netting Other terrestrial invertebrates - Could be used with trapping techniques (randomisation?)

Uses Should allow sites with highest abundances of a species to be identified Can compare abundance of each species between sites & over time (provided sites are not too different) Provides a thorough species lists for each site and habitat associations (GIS) Can provide rapid collection of data to allow conservation decisions to be made efficiently = 4% = 9% = 47% © BirdLife

Acknowledgements Co-authors on this project, Ross Macleod, Karl Evans, Sebastian Herzog, Steven Ewing & Rosalind Bryce Thanks to Armonia (the Bolivian Birdlife partner) for logistical support Many thanks to BP Conservation Programme, Royal Geographic Society, Thriplow Charitable Trust, Oxford University, Glasgow University, Gilchrist Educational Trust, SRGS, British Ecological Society & Russel Trust for essential financial support