Detecting trends in dragonfly data - Difficulties and opportunities - Arco van Strien Statistics Netherlands (CBS) Introduction

Statistics Netherlands’ involvement in monitoring National: Breeding birds Waterbirds Flora Amphibians Reptiles Butterflies Dragonflies Bats and a few other mammals Lichens Fungi Close co-oparation with NGO’s for each species group NGO’s responsible for field work (mainly by volunteers) Statistics Netherlands responsible for statististical analysis Introduction International: European Wild Bird Indicators European Grassland Butterfly Indicator European Bats (in development)

Structure of talk Difficulty 1: Statistical power Difficulty 2: Bias Three monitoring alternatives: No standardisation of field method Strong standardisation & analysis method TRIM Medium standardisation & analysis method Occupancy modeling Combining trends (European perspective) Conclusions Introduction

Difficulties in trend detection: Inability to detect existing trends (low statistical power) Even more worse: Biased trend estimates (increase or decline larger or smaller than in reality) Sufficient statistical power to detect trends No or negligible bias in trend estimates Monitoring schemes need to have: Introduction

Statistical power is low if: Yearly fluctuations in abundance are high Sites have different year-to-year changes Dragonflies have considerable fluctuations in abundance … Power

Statistical power is low if: Yearly fluctuations in abundance are high Sites have different year-to-year changes Sufficient sampling effort (= no. of sites; exact no. sites required depends on a.o. field method) Use longer detection period (= wait longer) Remedies: Power

Length of time series of smooth snake Coronella austria A longer detection period leads to more accurate trend estimates Years Power

Risk of bias is higher if: Sampling effort per site is not constant across years Detectability of species is not constant during the season and between years Inadequate sampling strategy applied e.g. dragonfly-rich areas oversampled Bias Sufficient statistical power to detect trends No or negligible bias in trend estimates Monitoring schemes need to have:

Increasing sampling effort leads to artificial increase Bias High sampling effort Low sampling effort Index of species

Risk of bias is higher if: Sampling effort per site is not constant across years Detectability of species is not constant during the season & years Inadequate sampling strategy applied e.g. dragon-rich areas oversampled Standardize sampling effort (field method) Take into account variation in detectability during the season (= multiple visits) Apply adequate sampling strategy (or adjust a posteriori any bias due to unequal sampling) Remedies: Bias

Structure of talk Difficulty 1: Statistical power Difficulty 2: Bias Three monitoring alternatives: No standardisation of field method Strong standardisation & analysis method TRIM Medium standardisation & analysis method Occupancy modeling Combining trends (European perspective) Conclusions Alternatives

Field methodData collectionExample no standardisation presence data e.g. per grid cell (e.g. 5x5 km2) Comparison of distribution between two periods strong standardisation count data per siteDutch dragonlfly scheme medium standardisation presence and absence data per grid cell or site ? Monitoring alternatives Alternatives

No standardisation of field method As in Atlas studies or studies to compile Red Lists No fixed sites Sampling efforts vary between years No prescription of field method No formal sampling strategy Collecting presence data only >>>> Statistical power low (only sensitive to pick up strong declines & increases in distribution) Risk of bias considerable due to not constant sampling efforts Statistical analysis: simple comparison of distribution data (or a statistical method) Alternatives

Strong standardisation of field method As in dragonfly scheme in the Netherlands Fixed sites (500 m - 1 km long) Yearly surveys Multiple visits per year (during the season) Detailed prescription of field method (fixed sampling effort per site) Sampling strategy: preferably (stratified) random choice of sites Collecting count data >>>> Statistical power high Risk of bias low Statistical analysis: TRIM TRIM

TRIM version 3 Poisson regression (loglinear models, GLM) for count data Pannekoek, J. & A van Strien, TRIM 3. Statistics Netherlands, Voorburg TRIM

TRIM: Trends and Indices for Monitoring data Specially developed by Statistics Netherlands for wildlife monitoring based on count data Statistical heart of wildlife monitoring data analysis Internationally accepted and in use in many European countries Easy to use Freeware Calculates yearly indices TRIM

INDEX: the total (= sum of al sites) for a year divided by the total of the base year TRIM

Statistical characteristics of TRIM Produces yearly indices and overall trends per species Produces confidence intervals Include overdispersion & serial correlation in models Goodness-of-fit tests for comparing models Covariates to test trends between sets of sites Weight factors may be included to improve representativeness if sites are not randomly selected Imputation of missing values TRIM

Imputation of missing counts required to compute correct indices TRIM

Medium standardisation of field method To be developed, but think of: Preferably fixed sites Survey per site once every 2-3 years Multiple visits per year (during the season) Limited prescription of sampling effort per site, e.g. 1 hour field work Sampling strategy: preferably (stratified) random choice of sites Collecting presence/absence data per site per visit (or abundance categories) >>> Power not high Risk of bias low Statistical analysis: Occupancy modeling to adjust for bias due to limited standardisation Occupancy

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey & J.E. Hines, Occupancy estimation and modeling. Elsevier, Amsterdam. Occupancy modeling: Recent developments in statistical methods make it possible to estimate area of occupancy while taking into account the detectability of species (which may differ according to e.g. not constant sampling efforts) Based on absence/presence data from repeated visits (capture-recapture) Statistical method is in development Freeware (PRESENCE, MARK) Occupancy

Occupancy modeling uses capture histories per site to separate occupancy and detectability SiteVisit 1Visit Occupancy SiteVisit 1Visit area of occupancy 100% detection probability per visit 50% area of occupancy 50% detection probability per visit 100% Simple example

Statistical characteristics of Occupancy modeling Produces estimate of area of occupancy per year (or period), taking into account detectability of species Comparing area of occupancy per period >> trend Produces confidence intervals Allows missing values Covariates to allow for effect of e.g. temperature during visit, incompleteness of survey etc. Weighting procedure (if sites are not randomly selected) to be developed Occupancy

Structure of talk Difficulty 1: Statistical power Difficulty 2: Bias Three monitoring alternatives: No standardisation of field method Strong standardisation & analysis method TRIM Medium standardisation & analysis method Occupancy modeling Combining trends (European perspective) Conclusions Combining trends

European population trend of species A European population trend of species A A. van Strien, J. Pannekoek & D. Gibbons, Bird Study 48: Combining trends Yearly population size of species A in country 1 Yearly population size of species A in country 1 Combining TRIM results per country, weighted by population sizes, is well-developed Yearly population size of species A in country 2 Yearly population size of species A in country 2 Yearly population size of species A in country 3 Yearly population size of species A in country 3 Yearly population size of species A in country 4 Yearly population size of species A in country 4

Gregory R.D., van Strien, A., Vorisek P. et al., Phil. Trans. R. Soc. B. 360: Gregory, R.D., Vorisek, P., van Strien, A. et al., Ibis, 49, s2, Combining trends Example of combining TRIM results of countries: Pan-Euromonitoring Common Bird Monitoring project producing Farmland Wild Bird Indicator (EU biodiversity indicator)

European trend in occupancy area of species A European trend in occupancy area of species A Yearly occupancy area of species A in country 1 Yearly occupancy area of species A in country 1 Combining areas of occupancy per country, weighted by areas, appears possible (but needs to be developed) Yearly occupancy area of species A in country 2 Yearly occupancy area of species A in country 2 Yearly occupancy area of species A in country 3 Yearly occupancy area of species A in country 3 Yearly occupancy area of species A in country 4 Yearly occupancy area of species A in country 4 Combining trends

Scores of alternatives Field methodPower to detect trends Risk of bias Sampling effort needed no standardisation (presence data) strong standardisation (count data) medium standardisation (pres/absence data) Conclusions

Statistical enemies of monitoring: low power and bias Standardisation of sampling effort helps to increase power and to reduce bias Monitoring based on strong standardisation: high power & little bias. But it requires considerable sampling efforts If strong standardisation is not feasible, consider medium standardisation: lower power, but again little bias (if detection probabilities are taken into account) For both alternatives statistical methods are available Both alternatives enable to combine trends across countries Conclusions