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Distribution of the invasive plant species heracleum sosnowskyi manden. in the komi republic (Russia)

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  • Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences

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Occurrences of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (northeastern part of European Russia) were recorded and published in the Global Biodiversity Information Facility (GBIF http://www.gbif.org) using the RIVR information system (http://ib.komisc.ru/add/rivr/en). RIVR stands for "Rasprostranenie Invasionnyh Vidov Rastenij" [Occurrence of Invasion Plant Species]. This citizen science project aims at collecting occurrence data about invasive plant species with the help of citizen scientists. Information can be added by any user after a simple registration (concept) process. However, the data published in GBIF are provided only by professional scientists. The total study area is approximately 19,000 km2. The GBIF resource contains 10894 H. sosnowskyi occurrence points, each with their geographical coordinates and photographs of the plants in the locus of growth. The preliminary results of species distribution modelling on the territory of European North-East Russia presented.
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Distribution of the invasive plant species Heracleum sosnowskyi Manden... 71
Distribution of the invasive plant species Heracleum
sosnowskyi Manden. in the Komi Republic (Russia)
Ivan Chadin1, Igor Dalke1, Ilya Zakhozhiy1, Ruslan Malyshev1, Elena Madi1,
OlgaKuzivanova1, Dmitrii Kirillov1, Vladimir Elsakov1
1 Institute of Biology of Komi Scientic Centre of the Ural Branch of the Russian Academy of Sciences, Kom-
munisticheskaya, 28, 167982, Syktyvkar, Komi Republic, Russian Federation
Corresponding author: Ivan Chadin (chadin@ib.komisc.ru)
Academic editor: Pavel Stoev|Received 14 November 2016|Accepted 27 February 2017|Published 9 March2017
Citation: Chadin I, Dalke I, Zakhozhiy I, Malyshev R, Madi E, Kuzivanova O, Kirillov D, Elsakov V (2017) Distribution
of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia). PhytoKeys 77: 71–80. https://
doi.org/10.3897/phytokeys.77.11186
Resource citation: Chadin I, Dalke I, Zakhozhiy I, Malyshev R, Madi E, Kuzivanova O, Kirillov D (2016) Occurrences
of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (Russia). v. 1.8. Institute of Biology
of Komi Scientic Centre of the Ural Branch, Russian Academy of Sciences. Dataset/Occurrence. http://ib.komisc.
ru:8088/ipt/resource?r=heraclueum_occurrence&v=1.8
Abstract
Occurrences of the invasive plant species Heracleum sosnowskyi Manden. in the Komi Republic (northeast-
ern part of European Russia) were recorded and published in the Global Biodiversity Information Facil-
ity (GBIF http://www.gbif.org) using the RIVR information system (http://ib.komisc.ru/add/rivr/en).
RIVR stands for “Rasprostranenie Invasionnyh Vidov Rastenij” [Occurrence of Invasion Plant Species].
is citizen science project aims at collecting occurrence data about invasive plant species with the help
of citizen scientists. Information can be added by any user after a simple registration (concept) process.
However, the data published in GBIF are provided only by professional scientists. e total study area
is approximately 19,000 km2. e GBIF resource contains 10894 H.sosnowskyi occurrence points, each
with their geographical coordinates and photographs of the plants in the locus of growth. e preliminary
results of species distribution modelling on the territory of European North-East Russia presented.
Keywords
Occurrence, human observation, Heracleum sosnowskyi, hogweed, invasive, geotagged photographs, Komi
Republic, European North-East Russia
PhytoKeys 77: 71–80 (2017)
doi: 10.3897/phytokeys.77.1186
http://phytokeys.pensoft.net
Copyright Ivan Chadin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DATA PAPER
Launched to accelerate biodiversity research
A peer-reviewed open-access journal
Ivan Chadin et al. / PhytoKeys 77: 71–80 (2017)
72
Project details
Project title
“Ecophysiological modelling of invasive plant species distribution. e case of Hera-
cleum sosnowskyi in the taiga zone of the European part of Russia
Funding
e project was supported by a grant of the Russian Foundation for Basic Research and
the Government of Komi Republic (Project No 16-44-110694).
Study area description
e Komi Republic is located in the north-east of the Russian Plain and the western
slopes of the northern Ural Mountains. It is a large and an important biogeographic
boundary that separates the ora and fauna of two continents – Europe and Asia.
On the plain territory of the Komi Republic, a pronounced latitudinal-nature zo-
nation occurs. e extreme north-east is taken by a subzone of the southern tundra.
e forest-tundra is a transition zone between the tundra and taiga. In the Pechora
Province, it has a width of 100–120 km forming the southern periphery of the territory
that has the Bolshezemelskaya tundra. e main type of vegetation in the Republic of
Komi is the boreal (taiga) forest. e taiga zone is divided into the following subzones:
Extreme northern, Northern, Middle, and Southern. e eastern edge of the Republic
in occupied by the Ural Mountains, where altitudinal zonation occurs with distinct
Mountain forest, Alpine tundra, and Cold deserts zones (Gorchakovskij 1975).
A large part of the republic has a climate similar to that of the Atlantic-Arctic
region with a cold temperate (boreal) climate (Brattsev et al. 1997). e territory is a
zone of excessive moisture, widespread marshes, and wetlands. e annual precipitation
exceeds the evaporation and decreases from south to north, from 700 to 550 mm. A
signicant dierence in the climate is observed across the length of the republic from
south to north and from west to east. e duration of the winter in the south of the
republic is 170–180 days and that in the north is 230–250 days. e average tempera-
ture in January (the coldest month) in the south is 15 °C whereas that in the north-east
is –22 °C. Summers are short and warm; the average temperature in July (the warmest
month) is approximately 10°C in the north-east and 17°C in the south. e prevailing
wind directions in winter are south and south-west, and north in summer. e monthly
average wind speed in the taiga zone is 3–4 m/s and that in the tundra area is 6.5 m/s.
Biological diversity of the Komi Republic region includes 929 fungi, 1217 vascu-
lar plants, 653 moss, 1020 lichen, 2,000 algae, more than 3,500 arachnid, more than
6,000 insect, 50 sh, six amphibian, ve reptile, 265 bird, and 57 mammal species.
ere are 237 forest, oristic, meadow, marsh, ichthyological, ornithological, and geo-
Distribution of the invasive plant species Heracleum sosnowskyi Manden... 73
logical reserves and natural monuments on the territory of Komi. e Pechora-Ilych
State Reserve and the Yugyd Va National Park occupy 13.5% of the total territory of
the republic (Ponomarev and Tatarinov 2012).
Design description
e project design combines an experimental approach and analysis of results of the
observations. e responses of H.sosnowskyi plants to the changes in the abiotic envi-
ronmental parameters were obtained by instrumental measurements of the morpho-
logical and physiological parameters (including CO2/H2O gas exchange, chlorophyll
uorescence, and heat dissipation) in the plants grown in climatic chambers and ex-
perimental plots. e data of the optimal and critical values of the environmental
factors (heat, light, rainfall, and soil) required for the survival and reproduction of the
plants were used for a joint analysis along with the geographically referenced data of
these factors. e results were arranged in a raster map showing the potential areas of
H.sosnowskyi. e resulting map was veried by a direct comparison with the data of
the eld observations of the habitats of this species and with the correlation simulation
of their geographical distribution.
Data published through
GBIF: http://ib.komisc.ru:8088/ipt/resource?r=heraclueum_occurrence
Taxonomic coverage
General taxonomic coverage description
e resource contains occurrence data only for one species – H. sosnowskyi Manden.
Taxonomic ranks
Kingdom: Plantae
Phylum: Tracheophyta
Class: Magnoliopsida
Order: Apiales
Family: Apiaceae
Genus: Heracleum
Species: Heracleum sosnowskyi
Common names: Sosnowskys hogweed, plants, vascular plants, owering plants, carrot
family, hogweed
Ivan Chadin et al. / PhytoKeys 77: 71–80 (2017)
74
Spatial coverage
General spatial coverage
e geographical coverage is essentially limited to the Komi Republic territory located in
the European part of Russia. Currently, all populations of H. sosnowskyi in this area are in-
vasive. is species was introduced into this region in the second half of the 20th century
as a forage crop. Since 2012 varieties of this species are excluded from the register of the
breeding achievements of the Russian Federation (Ocial bulletin 2012; http://gossort.
com/bullets/pdf/bull_176.pdf ). is species is also included in the “specialised catalogue of
weeds” (Information letter 2015; http://antibor.ru/sites/526a0b00d7e1e49744000002/
assets/56fa0dcdd7e1e4c087062929/pismo1-2.jpg).
Coordinates
59°22.48'N and 66°7.12'N Latitude; 48°56.24'E and 60°20.24'E Longitude
Temporal coverage
28 July 2012 - 23 August 2016
Methods
Method description
Photographs of plants were taken using consumer cameras. Videos were recorded with
a Car DVR Camera (video 1280×960 pixels at 30 frames/second), mounted on the car
windshield (height from the road surface was 170 cm). e survey was conducted at
speeds of 60–90 km/h. e GPS track was simultaneously recorded with GPS naviga-
tors. e time on the cameras and video recorders were synchronised with the time
displayed on the GPS navigation device.
All the images were geotagged by a GPS track log with “GPS Correlate” soft-
ware (v 1.6.1, https://github.com/freefoote/gpscorrelate) according to the methods
described in the OpenStreetMap Project documentation (Geotagging Source Photos
2016; http://wiki.openstreetmap.org/wiki/Geotagging_Source_Photos). e video
les were broken into frames (one frame per second) and the frames were saved as
“jpeg” les with the program FFmpeg (v 3.1.4 http://www.mpeg.org) followed by
geotagging of these les similar to that of the photographs. e array of images was
hand sorted into two groups: images that contained H.sosnowskyi plants and images
Distribution of the invasive plant species Heracleum sosnowskyi Manden... 75
without these plants. e coordinates of the photographs obtained from a Car DVR
Camera were corrected in the Quantum GIS Geographic Information System (QGIS)
program (v2.16.3 http://www.qgis.org, QGIS Development Team 2016) by shifting
the group of points on the side of the road. All geotagged H. sosnowskyi images were
uploaded to the online database “Occurrence of invasive plant species Heracleum sos-
nowskyi Manden.” (RIVR 2016).
Study extent description
e occurrence data of H.sosnowskyi were collected from an area of approximately
19,000 km2 (Figure 1). Most of the data were collected from the capital area of Komi,
Syktyvkar (61°39.95'N,50°49.53'E) as well as along the roads at a distance of 300km
from Syktyvkar, the directions of which coincide with the ow direction of the major
rivers Vychegda and Sysola belonging to the Northern Dvina basin. A separate clus-
ter of the data was collected from a 664 km (orthodromic) distance in the territory
and suburb of Inta city, located near the Arctic Circle (66° 1.87’N, 60° 8.72’E). A
pronounced sampling bias should be considered before using the data for the species
distribution modelling. Data were collected close to the settlements or the roads con-
necting them, which is a travel time bias (Fourcade et al. 2014). In the case of H.sos-
nowskyi, such a sampling bias may coincide with the actual factors determining the
dispersal of the plants of this species. In most cases, roadsides are the optimal habitats
for this species as they are open and well-lighted with adequate moisture due to the
roadside drainage systems. Moreover, the air ow creates favourable conditions for the
spread of the plants.
Sampling description
e occurrence data consist of the presence data only. Two methods were used for the
creation of occurrence records, which include the data collection along transects (7130
points) and mapping of H. sosnowskyi boundaries that were later converted to regular
points sample (3764 points). e regular points sample coordinates were generated us-
ing the QGIS Desktop software (v 2.16.3). e points were created with a 25 m point
spacing within polygon layers that indicated the H.sosnowskyi population boundaries.
e occurrences were labelled with a tag “Generated Regular Sample” written in the
“occurrence remarks” eld. e “associated media” eld contained the URL of the
locality map showing the generated point pattern with the scale bar and the north end
on top of the map.
Data along transects were collected by recording a video of H.sosnowskyi plants
growing along the roadsides and by taking photographs in the direction perpendicular
to the road at a distance of up to 5 km.
Ivan Chadin et al. / PhytoKeys 77: 71–80 (2017)
76
Figure 1. Study area. Red points indicate occurrences of Heracleum sosnowskyi described in the data paper.
Quality control description
e published data collected by professional scientists with sustainable skills for the
identication of H.sosnowskyi and its dierences from other similar species in its habi-
tats were published in GBIF whereas that collected by volunteers were accumulated in
the RIVR system. Before publication, data were checked for gross errors in georefer-
encing by visual inspection of the overlay points on the map with the borders of Rus-
sian regions in OpenStreet in the QGIS Desktop.
e presence of duplicate records was checked by running a special SQL script. e
records were counted as duplicated if three elds were the same: the coordinates, the date
of the event, and the le name of the photograph. For many data points (1080 of 10894
points, 10%), the same dates and coordinates were detected; however, they presented a se-
ries of photographs (2 to 13). ese data were saved in the system as they could be of interest
for the assessment of the landscape and the evaluation of plants in the H. sosnowskyi habitat.
Species distribution modelling
e described dataset was used for H. sosnowskyi species distribution modelling (SDM).
e SDM was performed for two plots. Plot 1 was a rectangular, limited by latitudes:
Distribution of the invasive plant species Heracleum sosnowskyi Manden... 77
61.0088°N, 62.1387°N and longitudes: 49.5013°E, 51.5941°E. e area of Plot 1 was
9 180 km2. e Plot 2 was a rectangular, limited by latitudes: 57.0000°N, 70.0000°N,
42.0000°E, 68.0000°E. e area of Plot 2 was 1 857 586 km2. All coordinates were
given in the WGS84 projection (EPSG: 4326).
Two groups of predictors were used. Group 1: the state of the earth’s surface, with
a spatial resolution of 1 second (≈ 30 m) per pixel (data was collected for Plot 1only):
VEG = vegetation cover map derived from classication of satellite images (20 classes);
ROAD = proximity map to the nearest road; AGRO = proximity map to the near-
est borders of agricultural areas. Group 2: bioclimatic variables are derived from the
monthly temperature and rainfall values obtained from WorldClim (Hijmans et al.
2005; http://www.worldclim.org/bioclim) with resolution of 30 second (≈ 1000 m)
per pixel (data was collected for Plot 1 and Plot 2): BIO1 = Annual Mean Temperature;
BIO2 = Mean Diurnal Range; BIO3 = Isothermality; BIO4 = Temperature Seasonal-
ity; BIO5 = Max Temperature of Warmest Month; BIO6 = Min Temperature of Cold-
est Month; BIO7 = Temperature Annual Range (BIO5-BIO6); BIO8 = Mean Tem-
perature of Wettest Quarter; BIO9 = Mean Temperature of Driest Quarter; BIO10
= Mean Temperature of Warmest Quarter; BIO11 = Mean Temperature of Coldest
Quarter; BIO12 = Annual Precipitation; BIO13 = Precipitation of Wettest Month;
BIO14 = Precipitation of Driest Month; BIO15 = Precipitation Seasonality (Coe-
cient of Variation); BIO16 = Precipitation of Wettest Quarter; BIO17 = Precipitation
of Driest Quarter; BIO18 = Precipitation of Warmest Quarter; BIO19 = Precipitation
of Coldest Quarter.
All data were obtained from open sources, either directly or as a result of raw data
processing in geographic information systems. e rights to use the Komi Republic
agriculture area map were acquired under a license agreement with the State Organiza-
tion “Syktyvkar Agrochemical Service Station”.
e presence data of H. sosnowskyi occurrences were obtained as a random sam-
ple of GBIF dataset described in this article. Five hundred randomly chosen presence
points were taken for modelling at Plot 1 and 1000 points for modelling at Plot 2.
Furthermore, 500 (for Plot 1) and 1000 (for Plot 2) randomly distributed points were
used as a background point.
SDM was performed with generalized linear multiple regression model in R (R
Core Team 2014) with dismo package (Hijmans et al. 2017).
Model tting with the predictors VEG, ROAD and AGRO showed statistically
signicant (p < 0.0001) relationship with the dependent variable (H. sosnowskyi pres-
ence in the given point). ROC analysis showed that AUC value for the regression
model was 0.92). ese results were supported by eld observations, invasion history
and ways of H. sosnowskyi seed dispersal. e plant occupies habitats with disturbed
soil cover, spreading rapidly along roads, due to the transfer of seeds by air ow, avoids
shaded and dry habitats (Fig. 2).
Model tting at Plot 1 and Plot 2 with bioclimatic predictors revealed a statis-
tically signicant relationship with eight predictors: BIO2, BIO4, BIO5, BIO6,
BIO7, BIO10, BIO12 and BIO17. e model with all these predictors showed
AUC value 0.99. Prediction with the model obtained within Plot 2 allowed to iden-
Ivan Chadin et al. / PhytoKeys 77: 71–80 (2017)
78
tify the putative northern H. sosnowskyi range boundary — 67.2000°N, within the
borders of the valley of the Pechora river (Fig. 3). According to the model, the values
of bioclimatic variables in the areas with maximum probability of H. sosnowskyi
presence were as follows (mean and standard deviation): BIO2: 8.3 ± 0.2 °C, BIO4:
112 ± 1 °C, BIO5: 21.2 ± 0.6 °C, BIO6: -21.9 ± 0.3 °C, BIO10: 3.6 ± 0.6 °C,
BIO12: 567 ± 24 mm.
e presence of H. sosnowskyi invasive plants in the northern forest-tundra sub-
zone (66.0000°N) was conrmed by eld observation on the territory of Inta city
(Komi Republic). H. sosnowskyi plants formed monostand and showed high enough
seed productivity (up to 12 000 seeds per plant) in this area.
Figure 2. e prediction map of Heracleum sosnowskyi habitats prepared with the species distribution
model based on vegetation cover map, nearest road proximity map, proximity map to the borders of agri-
cultural areas. e colour scale shows the probability H. sosnowskyi presence.
Distribution of the invasive plant species Heracleum sosnowskyi Manden... 79
Figure 3. e prediction map of Heracleum sosnowskyi habitats prepared with the species distribution
model based on bioclaimatic predictors. e borders of Plot 2 within which the model prediction was
made. e colour scale shows the probability H. sosnowskyi presence.
Datasets
Dataset description
Object name: Darwin Core Archive Occurrences of the invasive plant species Heracleum
sosnowskyi Manden. in the Komi Republic (European North-East Russia)
Character encoding: UTF-8
Format name: Darwin Core Archive format
Format version: 1.0
Distribution: http://ib.komisc.ru:8088/ipt/archive.do?r=heraclueum_occurrence
Ivan Chadin et al. / PhytoKeys 77: 71–80 (2017)
80
Publication date of data: 2016-10-19
Language: English
Licences of use: is work is licensed under a Creative Commons Attribution (CC-BY)
4.0 License.
Metadata language: English
Date of metadata creation: 2016-09-07
Hierarchy level: Dataset
References
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Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping Species Distributions with
MAXENT Using a Geographically Biased Sample of Presence Data: A Performance As-
sessment of Methods for Correcting Sampling Bias. PLoS ONE 9(5): e97122. https://doi.
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spatial Foundation Project. http://www.qgis.org
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ib.komisc.ru/add/rivr/en
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... Today Sosnowsky's hogweed is the most common in Northwestern Russia (Antipina and Shuiskaya, 2009;Geltmen et al., 2009), Komi Republic (Dalke et al., 2015;Chadin et al., 2017Chadin et al., , 2019, Central Russia (Vinogradova et al., 2010;Bogdanov et al., 2011;Budarin et al., 2014;Panasenko et al., 2014а;Shirokova and Ozerova, 2016;Panasenko, 2017), Siberia (Ebel et al., 2016(Ebel et al., , 2018, and the Far East (Smirnov and Korneva, 2010;Abramova et al., 2014;Chernjagina et al., 2014). The species is often found in many regions along roadsides and edges of field, on fallow lands and abandoned farms, in villages, along the edges of forests, in meadows, on waste lands and dumps, near houses, and in old gardens. ...
... A later investigation of the data accuracy was carried out by Barron et al. [9]. The use of geo data for the Russian regions is underway, an example can be found in the work [10]. Thus, we can name the geo data from OpenStreetMap as completely reliable. ...
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The development of environmentally safe and cost-effective methods for controlling invasive species Sosnowsky’s hogweed (Heracleum sosnowskyi Manden.) is an urgent issue for the European part of Russia. The article presents findings of an experiment on the effect of snow cover removal from the areas occupied by H. sosnowskyi in the early spring period (the beginning of March 2018) in the vicinity of the city of Syktyvkar (Komi Republic, Russia). The snow depth reached 100 cm on the intact plots; the sum of below-zero air temperatures measured at 6 a.m. constituted –448°C, with a minimum of –29.0°C during the experiment. The number of H. sosnowskyi plants of all age groups at the experimental plots (with removed snow cover) was shown to be significantly decreased. The median seedling density (pcs. per square meter) was equal to zero. Most of the surviving plants were located along the sides and in the corners of experimental plots. This can be explained by the higher temperature of soil on the borders of plots with an intact snow cover. The results of the experiment may be used for development of invasive plant eradication technology by removal of the snow cover. This technology can be suitable for kindergartens, schools, hospitals, and water protection zones, where the use of chemical methods of plant control is limited or prohibited. The obtained data set with respect to H. sosnowskyi monitoring is available in the repository of Zenodo.
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The development of environmentally safe and cost-effective methods of invasive species Sosnowski’s Hogweed (Heracleum sosnowskyi Manden.) control is a very actual task for the European part of Russia. The results of an experiment on the effect of snow cover removal on the areas occupied by H. sosnowskyi in the early spring period (the beginning of March 2018) in the vicinity of the city of Syktyvkar (Komi Republic, Russia) are presented. The snow cover reached a height of 100 cm on the intact plots, the sum of negative air temperatures measured at 6.00 a.m. constituted –448 °C, with a minimum of –29.0 °C during the experiment. The number of H. sosnowskyi plants of all age groups at the experimental plots (with removed snow cover) was shown to be significantly decreased. The median seedlings density (pcs. per square meter) was equal to zero. Most of survived plants were located along the sides and in the corners of experimental plots. This can be explained by the higher temperature of soil on plots borders with an intact snow cover. The results of the experiment may be used for development of invasive plant eradication technology by removing of the snow cover. This technology will be demanded on the territories of kindergartens, schools, hospitals, water protection zones, where the use of chemical methods of plant control is limited or prohibited. The obtained data set of H. sosnowskyi monitoring is available in repository of Zenodo http://doi.org/10.5281/zenodo.1404218 Key words: Heracleum sosnowskyi, biological invasion, frost resistance, snow cover, invasion management.
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В работе представлены результаты анализа конкурсной документации, договорных документов и технических заданий 477 закупок по ликвидации инвазии борщевика Сосновского (Heracleum sosnowskyi Manden.), проведённых в 18 субъектах Российской Федерации с 2011 по 2017 г. Согласно данным, размещённым на официальном сайте Единой информационной системы в сфере закупок, 95% контрактов было заключено для выполнения работ по уничтожению нежелательных зарослей H. sosnowskyi, остальные контракты были связаны с определением площади зарослей растений, разра-боткой методов их уничтожения, надзором за выполненными работами. Растения H. sosnowskyi были ликвидированы на площади около 18 тыс. га, картографирование зарослей проведено на площади 169 тыс. га. Общие затраты на выполнение контрактов составили 314 млн руб. Стоимость работ по коше-нию H. sosnowskyi составила около 30 тыс. руб./га, затраты на обработку зарослей гербицидами 14.5 тыс. руб./га (медианные значения). Стоимость услуг по картографированию одного гектара зарослей H. sosnowskyi составила 370 руб. Выявлена высокая вариабельность стоимости работ для контрактов, техническое задание которых предполагало уничтожение растений на территориях площадью менее 5 га. Наиболее масштабные работы по уничтожению H. sosnowskyi были выполнены в Ленинградс-кой, Московской и Вологодской областях, где средства на борьбу с инвазией заложены в бюджете регионов. В условиях ограниченного финансирования системную работу против зарослей H. sosnowskyi необходимо начинать с реализации пилотного проекта на территории одного-двух населённых пунк-тов, а затем распространять этот опыт на регион. Сведения о 477 контрактах, заключённых для лик-видации зарослей H. sosnowskyi размещены в репозитории «Zenodo». The analysis of 477 government contracts for the Heracleum sosnowskyi Manden. invasions eradication carried out in 18 Russia regions from 2011 to 2017 presented. According to the official data (http://zakupki.gov.ru) 95% of the contracts included works on the destruction of H. sosnowskyi plants, and the rest were connected with the determination of invaded areas, the development of methods for their elimination, and the supervision of the works carried out. Over seven years, H. sosnowskyi stands where mapped on an area of 169 000 hectares, and destroyed on an area of 18 000 hectares. The total cost of 477 government contracts amounted to 314 million rubles. About 90% of H. sosnowskyi stands was processed in the Leningrad, Moscow and Vologda regions, where funds for the fight against invasion where reserved in the regions budgets. The greatest variability of the work cost was between contracts with the areas subjected to processing less than 5 hectares. The median cost of mapping the H. sosnowskyi stands was about 370 rubles / ha. The mowing cost of H. sosnowskyi was about 30 thousand rubles / ha (median value), which was twice the cost of treating the stands with herbicides. Effective invasion management of H. sosnowskyi possible only with the knowledge of the key biological traits of the species. In the context of limited funding, systemic work on H. sosnowskyi populations control should start with a pilot project on the territory of one or two settlements, and then this experience should be spread to a larger region. Data on 477 government contracts used in the paper is freely available on the server Zenodo (https://zenodo.org/record/1257332#.WxFYEX8lGvF).
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MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one "virtual" derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.
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We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing.
Atlas Respubliki Komi po klimatu i gidrologii. Drofa, 115 pp
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