BioRisk 8: | | II 20 (201 3) Oe agar eh aaa ores
doi: 10.3897 /biorisk 8.4038 RESEARCH ARTICLE @ BioRisk

www.pensoftonline.net/biorisk

Supporting monitoring effects of genetically
modified organisms by GIS-technologies
and geodata — an overview

Winfried Schréder!, Gunther Schmidt!

| Lehrstuhl fur Landschaftsdkologie, Universitat Vechta, Postfach 15 53, 49364 Vechta, Germany

Corresponding author: Winfried Schroder (wschroeder@iuw.uni-vechta.de)

Academic editor: /. Settele | Received 25 September 2012 | Accepted 14 January 2013 | Published 8 August 2013

Citation: Schroder W, Schmidt G (2013) Supporting monitoring effects of genetically modified organisms by GIS-
technologies and geodata — an overview. BioRisk 8: 111-120. doi: 10.3897/biorisk.8.4038

Abstract

The approval of genetically modified organisms for deliberate release and placing on the market requires
environmental risk assessment and environmental monitoring. Methodological approaches and imple-
mentation of both tasks are still controversially discussed. This article analyses principles of environ-
mental monitoring of genetically modified organisms as published in the Guideline 4330 Part 1 of the
Association of German Engineers. Thereby, the article concentrates on the characterisation of the receiv-
ing environment affected by cultivation of genetically modified organisms and the representativeness of
monitoring systems to assess large-scale implications of the cultivation of genetically modified organisms.
Based on this, the article introduces statistical and geoinformatic measures as well as relevant geodata to

deal with these issues.

Keywords

Genetically modified organisms, geodata, environmental monitoring

Introduction

The authorization of genetically modified organisms (GMO) for deliberate release and
placing on the market follows specific regulations in the EU and its member states.
The implementation of relevant aspects of Environmental Risk Assessment (ERA) and
Environmental Monitoring (EM) is still under discussion. The controversy is about

Copyright Winfried Schroder, Gunther Schmidt. This is an open access article distributed under the terms of the Creative Commons Attribution License
3.0 (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

112 Winfried Schroder & Gunther Schmidt / BioRisk 8: 111-120 (2013)

how to regulate release of only those genetically modified plants (GMP) that do not
cause unacceptable risks to human health and the environment (Ziighart et al. 2013).
From basic ecological reasoning it is clear, that even a systematic and stepwise structured
risk assessment cannot cover all risk relevant questions, especially combinatory effects
emerging in areas of large extent and over long times. EM is essential to deal with unan-
ticipated and undesirable effects. Monitoring data help in detecting post-market effects
due to GMP cultivation. With regard to natural variability and together with baseline
data, EM data should be used to establish cause-relationships by differentiating whether
monitored environmental changes in, for instance, non-target population abundances
relate to GMP cultivation and/or are caused by other environmental or agronomic driv-
ers. Additionally, modelling approaches are a supplementary tool to analyse potential
combinatory effects of GMP cultivation (Reuter et al. 2008, 2012). In the described
context, ERA and EM should be linked closely. While ERA is testing hypotheses about
potential hazards and underlying processes experimentally as far as feasible, some ques-
tions remain which go beyond an experimental scale and, thus, require EM. Addition-
ally, EM should deal with the spatial validity and relevance of the hypotheses tested in
ERA. To reach this, according to the VDI guideline 4330 Part 1, GMP EM should rely
on sound sampling methodologies and on geographic information system (GIS) tech-
niques including spatial statistics and mapping techniques. In Germany, several research
projects dealt with these issues, and some of the main results are considered within this
article. In the following, it is shown that available geodata are useful to describe the re-
ceiving environment in the near and far vicinity of GMP fields. Statistical analyses and
classification of geodata are presented which serve to derive ecoregions or, e.g., climatic
and agricultural patterns, and, thereby, help for assessing the representativeness of run-
ning or planned GMP EM sites and for investigating adverse ecological effects of GMP
release on different spatial scales and for different agricultural regimes (Breckling et al.
2003; Graef et al. 2008; Reuter et al. 2010; Schmidt and Schroder 2011; VDI 2006).
These issues are explained in more detail by Schroder and Schmidt (2012).

Spatial estimation and mapping

According to the available data, geostatistical methods as well as multivariate statistics
can be used for analysing and mapping spatial data as derived by GMP EM. Geo-
statistics, for instance, comprise methods to calculate surface predictions from data
points (Krige 1984, Matheron 1971). To this end, spatial auto-correlation is exam-
ined by Variogram Analysis. Based on the calculated variogram model, several Kriging
methods can be used for spatial predictions (Olea 1999). Cross-validation quantifies
how well the spatial estimated values at locations without measurements fit with the
point measurements.

Multivariate Statistics such as cluster analysis or tree based models such as Clas-
sification and Regression Trees (CART) serve spatially differentiating the multiple
relationships between geodata stored in a GIS. Based on these relations, spatial predic-

Supporting monitoring effects of genetically modified organisms by GIS-technologies... 113

tions in time and space become possible (Hornsmann et al. 2008; Pesch et al. 2011;
Schréder et al. 2006a). In the context of GMP dispersal, Cluster Analysis can be used
to integrate measurement data from different meteorological networks with different
coverage in a GIS environment for defining representative climatic regions. Climatic
regions together with an ecological land classification were used to stratify the receiving
environment in order to select a representative number of sites for the modelling of

GMP dispersal (Schmidt and Schréder 2011).

Geodata

Application of models referring to the dispersal and persistence of GMP or to simu-
late, e.g., possible effects of GMP on non-target organisms and food webs as well as
planning and facilitating GMP EM with respect to coexistence issues in agricultural
landscapes depend on the availability of data on meteorology, land use, and local bio-
diversity. The following will give an overview of geodata for monitoring and modelling
adverse effects of GMP at the landscape level.

Landuse data can be obtained from either satellite images, GIS data collected dur-
ing field experiments, cadastral surveys provided by local land registries, vertical air-
photographs, or the Common Agricultural Policy notifications, each type of source be-
ing used at different scales and consequently provide different spatial and semantic reso-
lutions. Data on land use patterns and the cultivation of crops in Europe are available at
the Statistical Office of the European Communities (EUROSTAT, http://epp.eurostat.
ec.europa.eu). To some extent, data on field geometries providing detailed information
on agricultural land use can be obtained from the InVeKoS (Integrated Administration
and Control System) data base. In fact, due to legal restrictions and inconsistencies in
data harmonisation related to federal responsibilities, availability of these data is diffh-
cult (Schmidt and Schréder 2012). Based on remote sensing data, CORINE Landcover
maps are offered by the European Topic Centre on Land Use and Spatial Information
(EIONET, http://terrestrial.eionet.europa.eu/CLC2000) (Keil et al. 2005).

For analyses of GMP impacts in landscapes, meteorological data are needed as well.
These are, for example, data on precipitation, air temperature, sunshine duration, the
number of frost days, and wind conditions. Climate affects growth, persistence and dis-
persal of GM crops and their pollen and seeds. These data could be retrieved from me-
teorological stations. For instance, the German Weather Service operates about 4,400
precipitation sites, 660 stations for air temperature and 220 for solar radiation meas-
urements. For Europe, free data sets with a resolution of 10 arc minutes (~ 20 x 20 km)
are available at the Climatic Research Unit (CRU; http://www.cru.uea.ac.uk/-timm/
egrid/CRU_CL_2_0.html) in Norwich, UK (New et al. 2002). For modelling the pol-
len transport, phenological data on the flowering of GM crops should be considered,
too. It should be taken into account that global warming might have changed the tem-
perature induced beginning of oilseed rape and maize bloom (Englert et al. 2008). The
dynamics of GMO pollen transport can be described by compiling and processing data

114 Winfried Schréder & Gunther Schmidt / BioRisk 8: 111-120 (2013)

on wind direction and velocity. The wind direction influences the transport direction
of the pollen and, thus, potential exposition areas. Given a constant emission rate, the
wind velocity affects the range and the transport speed of air-borne pollen and leads
to a dilution (stretching), as with higher wind velocities a larger air volume passes the
source surface and the concentration per unit volume is reduced (Oke 1987).

Data on soil texture and soil types are available from the FAO: 1) Digital Soil Map
of the World (about 10 x 10 km?) (http://www. fao.org/nr/land/soils/digital-soil-map-
of-the-world/en) and 2) Harmonized World Soil Database (about 1x1 km?) (http://
www.fao.org/nr/land/soils/harmonized-world-soil-database/en). Data on the potential
natural vegetation which can be used for ecological land classifications can be obtained
from the Federal Agency of Nature Conservation (BfN) in Germany (Bohn et al.
2005). The PNV map stratifies Europe into more than 700 PNV units. The PNV can
be defined as the vegetation that would establish without human interference under
present climatic and soil conditions and is an integral indicator for the ecological con-
ditions in terrestrial ecosystems (Schroder et al. 2006a).

For detecting adverse effects on biodiversity a link between GMP and biodiversity
monitoring is imperative (SCBD 2000, 2004). With a widespread commercial release
of GMP, the extent of adverse biodiversity effects can be expected to become substan-
tial. For instance, biodiversity monitoring is able to detect the potential spread of GM
crops, the potentially enhanced mortality of non-target organisms, and it may also draw
a more general picture on potential effects on the countryside biodiversity. In Europe,
several biodiversity monitoring networks exist due to the Convention on Biodiversity
(CBD) which commits its signatory countries to identify and monitor their biodiversity.
However, these monitoring networks are poorly connected and data are usually available
only on local or national levels (Vieno and Toivonen 2005). Whereas the monitoring
of birds and butterflies is well established over long periods in some European countries
(e.g. in the UK > 30 years), allowing an assessment of changes at several trophic levels
and geographical scales (Thomas 2005), monitoring is not equally well established across
taxonomic groups relevant for GMP EM. Only few monitoring schemes for plants exist
(EuMon database, http://eumon.ckff.si/monitoring/search.php). As of September 2012,
EuMon database comprised 633 complete monitoring schemes (456 species schemes,
177 habitat schemes) covering approximately 4,000 species and addresses of 239 moni-
toring coordinators and institutions. Furthermore, the database contains information on
sampling methods. The reporting guidelines for biodiversity monitoring of the Europe-
an Habitats Directive suggest that an annual change as low as 1% should be detectable.
For GMP monitoring, this change must be extractable from the background noise of
sampling variability and population fluctuations. ‘This is only possible, if a considerable
amount of sites is frequently and accurately monitored and if reference areas, i.e. areas
without potential influence of GMP, are monitored at the same time with the same ac-
curacy. 15 (3.8%) of the monitoring schemes in the EuMon database enable detecting a
1% annual change, another eight schemes 5% annual change. Even though the EuMon
database is the largest collection of metadata on biodiversity monitoring available, it is
not comprehensive and might be confounded by biases (Schmeller and Henle 2008).

Supporting monitoring effects of genetically modified organisms by GIS-technologie.... 115

Besides the EuMon database, there are only few more data sources where information on
biodiversity or distribution of plant species — that may, for instance, serve as crossbreed-
ing partners of GMP — may be obtained: the Global Biodiversity Information Facility
(GBIF, http://www.gbif.org), the European Forest Genetic Resources Programme (EU-
FORGEN, http://www.euforgen.org/distribution_maps.html). In Germany, the Fed-
eral Agency for Nature Conservation (BfN) maintains the web application FloraWeb
(http://www.floraweb.de) where information on about 3,500 plant species are stored
containing details on, e.g., taxonomy, biology, and spatial distribution of plants in Ger-
many. A java applet allows for mapping selected plant species in a spatial differentiation
based on cadastre maps (scale 1 : 25,000; = 11 x 11 km’).

Selection of representative GMP monitoring sites by help of an ecologi-
cal land classification

The federal nature protection law (§ 6 BNatSchG), the environmental monitoring
concept of the Federal Ministry for the Environment, Nature Conservation and Nu-
clear Safety (BMU 2000) as well as the preamble of the administrative agreement
between the German government and the federal states on the exchange of environ-
mental data specify the following targets that should be complied with when carrying
out environmental monitoring: 1. The monitoring should be coordinated and based
on harmonised or standardised methods (Keune and Mandry 1996) so that the data
can be compared and used for statistical analysis and modelling. 2. The monitoring
data should allow for spatial extrapolation in order to bridge geographical gaps and for
supporting long-term research on environmental changes. The flow of data should be
efficient and the data should be available for scientists, especially for statistical testing
of hypotheses and modelling data.

Small plots and laboratory studies are unlikely to prove sufficient for GMP evalu-
ation. Therefore appropriate monitoring schemes covering areas of large extent and
modelling is needed to determine the impact on the landscape from GMP trait charac-
teristics (Craig et al. 2008). GMP ERA and EM should comprise the evaluation of the
characteristics of the GMP and its effect and stability in the environment, combined
with ecological characteristics of the environment in which the introduction will take
place. Thus, EM of GMP impacts should be implemented regarding description, expla-
nation and modelling of environmental changes potentially due to GMP cultivation.

The requirements mentioned above imply that the EM network should cover the
ecologically defined land classes (i.e. ecoregions being characterised by distinct environ-
mental conditions) in the respective country without gaps by a statistically adequate
number of EM sites. This ecological representativeness is crucial for the validity of the
EM sampling data (Cao et al. 2002; Tirler et al. 2003). Thus, monitoring and modelling
of GMP dispersal should be performed at locations which are representative for larger
areas with respect to those factors which potentially influence the dispersal or persistence
of GMP. Following this concept, ecoregions can be used to generalize modelling results

116 Winfried Schréder & Gunther Schmidt / BioRisk 8: 111-120 (2013)

calculated for specific agricultural and environmental conditions at single locations to
those areas where similar conditions exist, i.e. regions being part of the same ecoregions
(Schmidt and Schréder 2011). Additionally, GMP EM should take place in areas ex-
posed to GMP, preferably cultivated fields and their environment, but should include
also regions with no or unknown GMP exposure as reference areas. On a case-by-case
basis depending on the GMP characteristics, the selected indicators, checkpoints and
related analytical methods should consider relevant different spatial and temporal scales
(Graef et al. 2005; VDI 2006). The number of monitoring sites and regions needs to be
sufficient to support statistical analysis of results based on good scientific practice (Biih-
ler 2006; Stein and Ettema 2003). For each GMP, monitoring design and data analyses
should be based on appropriate scales of space and time and quality and quantity of data
to be representative and interpretable. Criteria for selecting monitoring sites and regions
include: representativeness of sites cultivated with specific GMP, with an emphasis on
regions repeatedly cultivated with GMP; representativeness of ecological regions con-
taining the spectrum of relevant indicators; availability of sites already monitored within
other environmental programmes; and areas with environmental conditions facilitating
spread or survival of GMP (Wilkinson et al. 2000; Ziighart and Breckling 2003).

In order to check the representativeness of existing EM networks which might be
appropriate for EM GMP or for establishing specific EM GMP networks, ecoregion-
alisations are appropriate measures. For Europe and Germany, ecological land classi-
fications were calculated by means of multivariate statistics and based on digital maps
depicting the spatial patterns of ecologically relevant land characteristics. For both,
Germany and some federal states, ecoregions were calculated by applying CART and
using surface maps on climate, altitude, soil, and potential natural vegetation (Graef
et al. 2005; Schroder et al. 2006a, 2006b). The according maps have a spatial resolu-
tion of 2 x 2 km? and 1 x 1 km/’, respectively. The land classification calculated for
Europe by means of CART (Hornsmann et al. 2008) subdivides the whole territory
into ecoregions mapped in a grid with a cell size of about 20 x 20 km?. Data used for
calculating the ecoregions are maps on the potential natural vegetation (PNV, Bohn et
al. 2005), on altitude (Global Land One-kilometer Base Elevation GLOBE, Hastings
et al. 1998), on soil texture (Digital Soil Map of the World DSMW, FAO 1996) as
well as on monthly averages on air temperature, sunshine duration, relative humidity,
and precipitation (Global Climate Dataset CL 2.0, New et al. 2002). The PNV was
set as the target variable whereas the above mentioned maps on altitude, soil texture,
and climate were chosen as predictors. In order to obtain a concise amount of ecore-
gions the most detailed map depicting the spatial pattern of about 200 ecoregions was
reduced to 40 ecoregions.

Conclusions

The GMP EM is an important element of the regulatory framework for GMO in

Europe and needs to be conducted according to scientifically sound methods and qual-

Supporting monitoring effects of genetically modified organisms by GIS-technologies... 117

ity criteria to generate data which have to be robust and conclusive. ‘The selection of
parameters, methods, experimental designs, locations and the timeframe for GMP EM
needs to ensure that adverse effects of GMP can be detected reliably and as early as
possible. To reach this end, guidelines such as VDI 4330 Part 1 are needed attempting
to harmonize and standardize the GMP EM design.

VDI 4330 Part 1 describes that the environmental effects of GMP may vary with
the characteristics of different receiving environments in terms of, e.g., climate, soils,
land use patterns or geographic distribution of wild relatives of certain GMP. There-
fore data derived by ERA or EM should be collected in those regions which are rep-
resentative for respective ecological and agronomic characteristics which potentially
could influence the spread and impacts of GMP. Thus, spatially differentiated moni-
toring schemes are needed, in particular with regard to biodiversity (e.g. non-target
organisms) and ecological processes and functions (e.g. soil functions) in which these
organisms are involved. Exposure assessment is crucial for GMP EM aiming to assess
whether relevant parameters, e.g. certain non-target species, have to be investigated
during monitoring. In combination with an effect assessment, the exposure assessment
allows the evaluation of species which may be at risk. Geodata, ecological land classifi-
cation, spatial estimation and GIS techniques in combination with dynamic modelling
are fundamental to address effects on a landscape scale and long-term implications, to
analyse and evaluate the appropriateness of existing monitoring programs or data for
GMP EM, to design adaptations or extensions of the scope of GMP EM if they are
inappropriate to address the specific requirements for GMP EM.

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