Mapping species distribution using Google : a pilot study of the - - PowerPoint PPT Presentation

Mapping species distribution using Google™: a pilot study of the Pine Processionary Moth

• J. Rousselet, C.-E. Imbert, A. Dekri, J. Garcia, F.

Goussard, O. Denux, C. Robinet, F. Dorkeld⋆ A. Roques, & J.-P. Rossi⋆

URZF & ⋆CBGP, INRA, France

7-03-14 IUFRO Group Symposium “Entomological Research in Mediterranean Forest Ecosystems” May 11, 2012

Objectives

Assessing species distribution over large regions Documenting the presence/absence of the species Monitoring species expansion Climate change Biological invasion Question Can we take advantage of free databases such as Google Maps™ to improve our knowledge of species distribution ?

Methods

Comparing field data with in silico data derived from Google Maps™ Assessing possible scale effects by working at 2 contrasted spatial scales Subject species : the pine processionary moth Thaumetopoea pityocampa

Google Street View™ and the Google cars

Google™ = a set of services accessible from the web for free Google Maps™ and Google Earth™ = Mapping and geospatial resources Google Street = technology providing panoramic views from positions along many streets in the world The Google cars explore the streets and take pictures used to generate panoramic views

Google Street View™ and the Google cars

Google Street = technology providing panoramic views from positions along many streets in the world

Google Street View™ and the Google cars

Google Street = technology providing panoramic views from positions along many streets in the world

Study sites

A large study area of ca. 240 × 300 km (≃ 73 000 km2) A smaller study area of 22 × 22 km (484 km2)

Sampling for presence/absence

Studied area discretised in : 291 cells of 16 × 16 km size 121 cells of 2 × 2 km size

Sampling

Traditional sampling Each cell was explored by well trained people (car driving) and the presence of PPM nest recorded. All roads were run through. Sampling PPM using Google Maps™ and Google Street View™ Each cell was explored using the Google database by means of visual exploration of the available panoramic views.

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Each cell was explored using Google Maps™ and Google Street View™

Large scale study (cell = 16 × 16 km)

Field data Google-derived data

Large scale study (cell = 16 × 16 km)

Field data Google-derived data Evaluation field Google presence absence presence true positive=273 false positive=0 absence false negative=13 true negative=5 True positive rate (sensitivity) = 96% True negative rate (specificity) = 100% Accuracy = 95.5% Good performance

Short scale study (cell = 2 × 2 km)

Field data Google-derived data

Short scale study (cell = 2 × 2 km)

Field data Google-derived data Evaluation field Google presence absence presence true positive=3 false positive=0 absence false negative=63 true negative=49 5 % of cell with no data True positive rate (sensitivity) = 4.5% True negative rate (specificity) = 100% Accuracy = 45.2% Poor performance

Too many false negatives (i.e. too few true positives : 4.5%) Network of roads available within Google streetview™ → coverage decreases with increasing spatial resolution i.e. with smaller cells

Conclusions Google seems to provide reliable occurrence data for the PPM in

• ur large scale studies (low resolution : 16 × 16 km)

each cell contains many Google roads and many visible host trees Google seems not to provide reliable occurrence data in our small scale study (high resolution : 2 × 2 km) Many cells do not contain Google roads or only a few Higher probability for the pictures to be taken during summer/fall when there is no nest or nests cannot be seen

Conclusions This pilot study shows that in silico data such as Google Streetviews™ may be useful to study species distribution The performances appear to be scale-dependent The accuracy of Google-derived data may change according to landscape type → more exploratory surveys are needed !

Conclusions This pilot study shows that in silico data such as Google Streetviews™ may be useful to study species distribution The performances appear to be scale-dependent The accuracy of Google-derived data may change according to landscape type → more exploratory surveys are needed !