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Application of GIS technology to biodiversity conservation Primjena

Application of GIS technology to biodiversity conservation
Primjena GIS tehnologije u zaštiti bioraznolikosti
T. Nikolić
Department of Botany, Division of Biology
Faculty of Science, University of Zagreb
Address: Marulićev trg 9a, HR-10000 Zagreb, Croatia
Phone: (++385 1) 489 8064; Fax: (++385 1) 489 8093
@-mail: [email protected]; URL:
U sklopu provedbe projekta:
Epigenetic vs. genetic diversity in natural plant populations:
A case study of Croatian endemic Salvia species
Croatian Science Foundation
General notes:
•GIS – Geographic Information System
•Term origin: Roger Tomlinson, in paper "A
Geographic Information System for
Regional Planning“ from 1968.
•IT supported system for collecting, storing,
managing, visualisation and analysis of the
different types of spatial data
Spatial data:
•wide spectrum of different information
about space with different origin:
topography, hidrology, geology, pedology,
vegetation, habitats, climate, urban objects,
•very wide palette: bussines (micro up to
mega), state administration and
governments (spatial planning, forestry,
minning, energetics, traffic, different
infrastructure, ..), educational system,
science, NGO’s, individuals, ....
•applications extremely numerous and
•specialist literature (books, magazines,
papers) and on-line materials swell with
geometric progression
•hughe number of scientific papers
•i.e. Web of Knowledge shortcut “GIS” in
title result with 12.241 scientific paper
•i.e. Web of Knowledge shortcut “GIS” in
topic result with 48.978
• searched on 25.09.2013., additionaly
• Google search = 44.400.000 results
Time frame
•conceptually similar applications from
the first half of the 19th century
•with IT support since the early sixties
20th century onwards, geometrical
progression and the development and
•Croatia - from the early eighties
(competition several vendors of tools
and services)
•on Universities, begenning ~ 20-30 year
•during the last six decades: from the
expensive and exclusive tools for the few
to the public and free on-line application
IT GIS support
Commercial tools for more or less money:
•AutoDesk – Products that interface with its flagship AutoCAD
software package include Map 3D, Topobase, and MapGuide.
•Bentley Systems – Products that interface with its flagship
MicroStation software package include Bentley Map and
Bentley Map View.
•ERDAS IMAGINE by ERDAS Inc – Products include Leica
Photogrammetry Suite, ERDAS ER Mapper, ERDAS
ECW/JP2 SDK (ECW (file format)) are used throughout the
entire mapping community (GIS, Remote Sensing,
Photogrammetry, and image compression) and ERDAS
•Esri – Products include ArcView 3.x, ArcGIS, ArcSDE,
ArcIMS, ArcWeb services and ArcGIS Server.
•IGiS an Indian GIS by ScanPoint Geomatics Ltd.
•Intergraph – Products include G/Technology, GeoMedia,
GeoMedia Professional, GeoMedia WebMap, and add-on
products for industry sectors, as well as photogrammetry.
•Luciad Products for high-end geospatial situational
awareness. Used primarely by defense & aeronautical
•MapInfo by Pitney Bowes Software – Powerful desktop GIS
MapInfo Professional is enhanced with many plug-ins
including MapInfo Drivetime for route analysis, MapInfo
Engage 3D for 3D and statistical analysis, MapInfo
MapMarker for Geocoding.
•IDRISI – GIS and Image Processing product developed by
Clark Labs at Clark University. Affordable and robust, it is
used for both operations and education.
Freeware – Sharewere sollutions:
•GRASS GIS – Originally developed by the U.S. Army
Corps of Engineers: a complete GIS.
•gvSIG – Written in Java. Runs on Linux, Unix, Mac OS X
and Windows.
•ILWIS (Integrated Land and Water Information System) –
Integrates image, vector and thematic data.
•JUMP GIS / OpenJUMP ((Open) Java Unified Mapping
Platform) – The desktop GISs OpenJUMP, SkyJUMP,
deeJUMP and Kosmo all emerged from JUMP.[3]
•MapWindow GIS – Free desktop application and
programming component.
•Quantum GIS (QGIS) – Runs on Linux, Unix, Mac OS X
and Windows.
•SAGA GIS (System for Automated Geoscientific Analysis)
–- A hybrid GIS software. Has a unique Application
Programming Interface (API) and a fast growing set of
geoscientific methods, bundled in exchangeable Module
•uDig – API and source code (Java) available.
And others ...
•Web map Servers, moduls or independent DB MS,
Internet based services, specialistic tools and
modules (transformations, photogrametry, specific
analysis, data preparation, geostatistics, remote data
managing, etc. ...)
About data
•data initially available exclusively on market principles
•rapid liberalization particularly in the USA, then in the EU and
•global, continental, regional, country - datasets
•in Croatia the data liberalization - very slow and tortuous, with
negative consequences in all areas of application
•more recently - the National Spatial Data Infrastructure (NIPP –
Nacionalni informacijski sustav prostornih podataka).
About data
•physically available data (eg, maps,
photographs) are undergoing the process of
digitizing and the resulting digital raster and
vector (point, line, polygon) data
•digitally created information (eg digital
cameras – remote sensing, satellites,
•raster ↔ vector conversions
•± abundant attribute tables - tables of data
related to the pixels or vectors
•stored in ~ independent or integrated GIS
relational database (Spatial DBMS), often with
complex architecture
In biology s.l. – data/results presentation
• taxa distribution maps (dot maps, grid maps, polygon maps)
• protected area maps
• other, ~ similar
Example 1
dot maps for taxa
Example 2
protected areas
Example 3
Example 4
ecological network
Natura 2000 network (SINP)
Important Plant Areas (HBoD)
Land Cover (AZO)
Geological Maps (HGI)
other thematic GIS products
In biology s.l. – analytical approach, GIS added values
• taxa diversity maps
• taxa ecological profiling
• landcover diversity
• habitat diversity
• endemism centres
• hot spot biodiversity area
• conservation planning
• invasive taxa centres
• temporal analysis and trends
• predictive distribution maps
• gap analysis/modelling
• theoretical changes in A, B, C, ... –
expected impact on D, E, F, ..
• distribution modelling
• spatial modelling of genetic features
• etc.
Example 1
simple diversity map
Nikolić T. et al. (2013): Diversity, knowledge and spatial
distribution of the vascular flora of Croatia. // Plant
biosystems. DOI 10.1080/11263504.2013.788091.
Nikolić T. et al. (2013): Invasive alien plants in Croatia
as a threat to biodiversity of South-Eastern Europe:
distributional patterns and range size. // Comptes
rendus Biologies. DOI 10.1016/j.crvi.2013.01.003
Example 2
advanced diversity map
(IPA area determination)
Rarity indices
Criteria B1 species
Rarity indices
Criteria A species
Nikolić T., Topić J., Vuković N. ur. (2010): Botanički važna područja Hrvatske.
Prirodoslovno-matematički fakultet Sveučilišta u Zagrebu i Školska knjiga d.d., 1-529.
Example 3
biodiversity centres
planning (COAST project)
111 taxa of threated fauna, 15 stenoendemic and threated,
57 taxa from Bern list, 4 threatened taxa from see fauna,
maritime area of particular valuse for biodiveristy;
•cca 2500 taxa of vascular flora, 270 threatened taxa, all
taxa from Habitat Directive, 323 endemic taxa and
Posidonia oceanica;
•all habitats from habitat maps of Croatia 1:100.000 (9 ha),
surveying caves, all inland waters from maps 1:25.000,
special habitats (sandbanks, reefs, ..), Land Use data
1:100.000, DEM with grid size 200 m), etc.;
•already protected areas on national and local level, CRONen network, local administration proposals, spatial plans
•new and inovative complex biodiversity indices calculation
on MTB 4 map unit, final selection
Jelaska S. D. et al. (2010): Terrestrial biodiversity analyses in Dalmatia (Croatia): A complementary approach using
diversity and rarity. // Environmental management. 45 (2010) 3; 616-62.
Example 4
temporal analysis and trends
Main goal of this reserach was to define interactions of the
phenological dynamics of the deciduous beech forest and the
ecological factors defined by geomorphological parameters in
western and middle Dinaric Alps. Phenological dynamics data
have been derived from the spatial distribution of the MODIS
Enhanced Vegetation Index (EVI; 250m x 250m, 16 day
composite), as an indicator of photosynthetic activity for the
period of 2000 to 2011. Seven phenological parameters have
been calculated within TIMESAT software by Savitzky-Golay
filtering technique. Following phenological parameters have been
calculated for each season within pixel identified as deciduous
beech forest: start of season, end of season, length of season,
middle of season, rate of increase at the beginning of the season,
rate of decrease at the end of the season and large seasonal
integral. Spatial distributions of 63 geomorphological parameters
have been derived from the SRTM digital elevation model
(resolution 30 x 30m). The results indicate that the parameters of
phenological dynamics of the deciduous beech forest are highly
dependent on numerous gemorphological parameters as well as
on their interactions. The most significant geomorphological
predictors of phenological dynamics are altitude, latitude, index
of continentality (distance from the sea) and the indicators of
aspect, exchange of cold area and surface temperature.
Temporal variability of the phenological parameters (calculated
for each pixel in twelve year period) are statistically significantly
correlated with numerous geomorphological parameters. Similar
results are indicated for the temporal trends of phenological
parameters. The temporal trends could be related to climate
changes (global warming) and/or with the impact of the Sun
activity (as periodical variable that has pseudo-linear correlation
during the period of this research).
Mesić Z. (2012): Impact of the geomorphological parameters to phenological dynamics in the dominant layer of
the beech forest of the western and middle Dinaric Alps. Doctoral Thesis, Faculty of Science, University of
Zagreb, 1-129.
Example 5
predictive distribution maps
Subas. Omphalodo-Fagetum distribution modelling – different climatic changes scenarios
Floristic data were collected in 2002. on 151 localities (Jelaska 2006), climate data (Antonića i sur. 2000, 2001) intepolated on
300 m rasterk layers (calculated: mean annual temperature, total annual precipitation, total winter precipitation (december –
february), total summer precipitation (june – august). Prediction model (classification tree according to Breiman i sur. (1984),
indices of diversity, ordination analysisi fo gradients, etc.)
Example 5
predictive distribution maps
Asarum europeum distribution modelling – different climatic changes scenarios
Nikolić T., Kušan V., Jelaska S. D. (2006): Utjecaj globalne promjene klime na kopnene ekosustave. U sklopu: 2.
nacionalnog izvješće o utjecaju globalnih klimatskih promjena. MZOPU, Zagreb.
Example 6
influence of habitat conditions on
macrophyte growth dynamics
The influence of habitat conditions on macrophyte growth
dynamics was monitored during 2007, 2008 and 2009 on 75
sites in watercourses of three regions in lowland Croatia:
Baranja, Lonjsko polje and Bosut River Basin. The areas with
higher species richness and elevated nutrient conditions were
indicated according to species composition and habitat
conditions. In catchments with highly agricultural land use with
shallow, maintained watercourses and anthropogenic
disturbance development of amphibious and terrestrial
species were recorded. The spread of free-floating species
were associated with higher urban land use and higher
nutrient concentrations. Submerged aquatic vegetation was
found in deeper watercourses with the higher transparency.
Amphibious and terrestrial macrophytes have been dominated
during drought conditions and free-floating species during
rainy periods of high water. Seasonal changes of
dominate species were controlled by nutrient enrichment,
competitive advantages and other habitat conditions.
Kočić A. (2013): Influence of habitat conditions on
macrophyte growth dynamics in Croatian lowland
watercourses. Doctoral Thesis, Faculty of Science,
University of Zagreb, 1-124.
Example 7
spatial modelling of genetic features
saptial distribution of the Fraxinus
angustifolia genetic variablity in Europa
Populations occurring in areas of overlap between the current and
future distribution of a species are particularly important because
they can represent “refugia from climate change”. We coupled
ecological and range-wide genetic variation data to detect
such areas and to evaluate the impacts of habitat suitability
changes on the genetic diversity of the transitional Mediterraneantemperate tree Fraxinus angustifolia. We sampled and genotyped
38 natural populations comprising 1006 individuals from across
Europe. We found the highest genetic diversity in western and
northern Mediterranean populations, as well as a significant west
to east decline in genetic diversity. Areas of potential refugia that
correspond to approximately 70% of the suitable habitat may
support the persistence of more than 90% of the total number of
alleles in the future. Moreover, based on correlations between
Bayesian genetic assignment and climate, climate change may
favour the westward spread of the Black Sea gene pool in the long
term. Overall, our results suggest that the northerly core areas of
the current distribution contain the most important part of the
genetic variation for this species and may serve as in situ
macrorefugia from ongoing climate change. However, rearedge
populations of the southern Mediterranean may be exposed to a
potential loss of unique genetic diversity owing to habitat suitability
changes unless populations can persist in microrefugia that have
facilitated such persistence in the past.
Temunović M. et al. (2013): Identifying refugia from climate change using coupled ecological and genetic data in
a transitional Mediterranean-temperate tree species. Molecular Ecology (2013) 22, 2128–2142.
New Flora Croatica Database modules GIS supported:
• taxon ecological shaping-up
• biodiversity analyst
• GIS spatial data repository
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