Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 September 2021 | 13(11): 19448–19465
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7166.13.11.19448-19465
#7166 | Received 07 February 2021 | Final
received 01 June 2021 | Finally accepted 14 August 2021
Argentinian odonates
(dragonflies and damselflies): current and future distribution and discussion
of their conservation
A. Nava-Bolaños 1 ,
D.E. Vrech 2, A.V. Peretti 3
& A. Córdoba-Aguilar 4
1,4 Departamento de Ecología
Evolutiva, Instituto de Ecología,
Universidad Nacional Autónoma de México, Apdo. Postal 70-275,
Ciudad Universitaria,
México, D.F. 04510, México.
1 Biodiversity Institute,
University of Kansas, Lawrence, KS, USA.
1 Museo de Zoología,
Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma
de México, Apartado Postal 70-399, 04510 Ciudad de
México, México.
2,3 Instituto de Diversidad
y Ecología Animal, CONICET - Universidad Nacional de
Córdoba, Vélez Sarsfield 299 (5000), Córdoba,
Argentina.
1 anb@ciencias.unam.mx, 2 dvrech@unc.edu.ar,
3 aperetti@unc.edu.ar (corresponding
author), 4 acordoba@iecologia.unam.mx (corresponding author)
Editor: Anonymity requested. Date of publication: 26 September 2021 (online & print)
Citation: Nava-Bolaños, A., D.E. Vrech,
A.V. Peretti & A. Córdoba-Aguilar (2021). Argentinian odonates
(dragonflies and damselflies): current and future distribution and discussion
of their conservation. Journal of
Threatened Taxa
13(11): 19448–19465. https://doi.org/10.11609/jott.7166.13.11.19448-19465
Copyright: © Nava-Bolaños et al. 2021. Creative Commons Attribution 4.0 International
License. JoTT
allows unrestricted use, reproduction, and distribution of this article in any
medium by providing adequate credit to the author(s) and the source of
publication.
Funding: To PAPIIT-UNAM grants IN 203115 and IN206618 to ACA. To a CONACyT-CONICET grant 190552 to ACA and AVP. To Secretaría de Educación, Ciencia, Tecnología e Innovación de la Ciudad de México (SECTEI) for the support to ANB.
Competing interests: The authors declare no competing interests.
Author details: A.
Nava-Bolaños is a postdoctoral researcher at the Museum of Zoology
(Universidad Nacional Autónoma de México) conducting
global biodiversity analysis of pollinator species. Her research topics
include hybridization, climate change, reproductive isolation barriers, species
distribution, ecological niche models and insect conservation. D.E. Vrech is
a researcher at the Instituto de Diversidad y Ecología Animal (Universidad Nacional de Córdoba,
Argentina). His main research topics are behavioral ecology of arthropods, sperm
biology, evolutionary biology, and reproductive behavior. A.V. Peretti is a researcher at the Instituto de Diversidad y Ecología Animal
(Universidad Nacional de Córdoba, Argentina). His research lines include
the study of the reproductive biology of arthropods, and topics associated with
ecology, functional morphology, physiology and genetics. He has worked with
scorpions, spiders, and insects, mainly odonates. A. Córdoba-Aguilar is a researcher at
the Instituto de Ecología (Universidad Nacional Autónoma de México). His research topics are insect vector
control and insect conservation.
Author contributions: ANB, DV, AVP, and ACA planned the
paper, ANB and DV executed all analyses and all authors wrote and revised the
paper. ANB and DV contributed equally to the paper.
Acknowledgements: To PAPIIT-UNAM grants IN 203115
and IN206618 to ACA. To a CONACyT-CONICET grant
190552 to ACA and AVP. To Secretaría de Educación, Ciencia, Tecnología e Innovación de la Ciudad
de México (SECTEI) for the support to ANB.
Abstract: In terms of conservation,
Argentinian odonates have not been assessed using a
quantitative approach. One way to achieve this is by modelling their
distribution to gather the extent of occurrence. Thus, we modelled the current
and future (projected year, 2050) potential distribution of 44 odonate species that occur in Argentina as well as in
neighboring countries. Our models of current times indicate a fairly wide
distribution for most species but one exception is relevant for conservation
purposes: Lestes dichrostigma
has less than 30,000 km2 and falls in the ‘Near Threatened’ category
according to the IUCN Red List. Another seven species have less than or close
to 100,000 km2: Elasmothemis cannacrioides,
Erythemis credula, E. paraguayensis,
Heteragrion angustipenne,
H. inca, Lestes forficula, and Mecistogaster linearis. Future distribution estimates suggest that: a) 12 species will lose or
gain around 10%, four species will increase their distribution beyond 10% (up
to 2,346%), and 28 species will lose more than 10% (up to 99%). Although current protected areas
embrace most odonate species in Argentina, it is
still premature to conclude whether this situation will remain in the future
given the physiological tolerance and dispersal abilities of the study species
among other drivers of distribution.
Keywords: Argentina, global change, IUCN,
Odonata, potential distribution, status.
Resumen: En términos de conservación, los odonatos argentinos
no han sido evaluados usando un enfoque cuantitativo. Una manera de hacer esto es modelando
su distribución para obtener la extensión de la ocurrencia. En este trabajo modelamos
la distribución actual y futura
(año proyectado, 2050) de
44 especies de odonatos que
se distribuyen en Argentina
y países vecinos. Los modelos actuales indican una distribución
amplia para la mayoría de especies aunque existe una excepción
para propósitos de conservación:
Lestes dichrostigma
con menos de 30,000 km2 y que cae en la categoría
de “cercana a la amenaza” según la lista roja de la UICN. Otras siete especies tienen menos o cerca de 100,000 km2: Elasmothemis
cannacrioides, Erythemis credula, E. paraguayensis,
Heteragrion angustipenne,
H. inca, Lestes forficula y Mecistogaster
linearis. Las estimas futuras sugieren que: a) 12 especies perderán o ganarán alrededor de 10% de área, cuatro especies
incrementarán su distribución por más de 10% (hasta 2346 %), y 28 especies
perderán más del 10% (hasta
99%). Aunque las áreas naturales protegidas actuales albergan la mayoría de especies en Argentina, es aún prematuro concluir
que esta situación prevalecerá en el futuro dada la tolerancia fisiológica y capacidad de dispersión de las especies incluidas en este estudio
así como otros efectores de su distribución.
Introduction
Given their analytical strength,
species distribution models have been widely used to assess the potential area
where a species occurs as predicted by environmental variables (Peterson 2006).
Odonates have not been an exception to this practice
with at least 30 different studies in distinct world regions (reviewed by
Collins & McIntyre 2015). Such interest is partly understood on the basis
of the intrinsic threat that humankind has posed to freshwater bodies (e.g.
Sala et al. 2000) related to the direct dependence of odonates
on these bodies. Furthermore, a more recent analysis indicated that odonates can be used as the indicators of global change
given their practicality as study models (i.e. large body size), well-described
macro-ecological responses, key role as predators in aquatic and terrestrial
habitats and their trend of becoming field-animal models for
temperature-mediated responses (Hassall 2015). Paradoxically, our current
knowledge of the extinction risk for most odonates is
extremely limited. For example, the IUCN (2018) shows a shortage of species
with strong geographical biases, with country-based assessments frequently
lacking firm quantitative-supporting data (see for example, Paulson 2004). One
case is that of Argentina: 86 species are listed of which one is ‘Endangered’,
one is ‘Vulnerable’, two are ‘Near Threatened’, four are ‘Data Deficient’, and
78 are ‘Least Concern’ (IUCN 2018). This implies that a proper assessment is
badly needed for this country.
Distribution models of odonates have provided clues of how current distribution
will be affected by increases in temperature (reviewed by Collins &
McIntyre 2015). These studies have covered up to 25% of the total world odonate diversity, and have shown that in general there
will be shifts in distribution, with lotic species and narrow-distribution
species (e.g., endemic) showing a tendency to have their areas reduced (reviewed
by Collins & McIntyre 2015). In this paper, we have carried out an exercise
of calculating current and future distribution models for Argentinian odonates to supplement current studies of distribution
gathered from provincial records (e.g. Muzón et al.
2014, 2015; von Ellenrieder & Muzón
1999, 2008; von Ellenrieder 2009, 2010). Our analysis
is based on a fraction of the 271 species currently known to occur in Argentina
(Muzón & von Ellenrieder
1999; von Ellenrieder & Muzón
2008). Our aim is to use our assessment to guide the current IUCN risk
categories for Argentinian odonates based on criteria
A and B, that define extent of occurrence.
Material
and Methods
Occurrence data of species
Presence of odonate
species was compiled from literature records, GBIF records (www.gbif.org as of
20 December 2017; GBIF Occurrence Download http://doi.org/10.15468/dl.mf6nh7),
and odonate specialists (Rosser Garrison, Natalia von
Ellenrieder, and Dennis Paulson). All data were
checked carefully for geographic accuracy by removing duplicates and records
with inconsistent georeferencing, for example coordinates on the sea, or
missing as recommended in the literature of data cleaning (Chapman 2005). Most
records were gathered by odonate experts, so we are
confident that identification bias should be minimal. Niche models were built
only when more than 10 records per species were available. Thus, the final data
set included 1,734 unique presences of 44 species (see Table 1) which were
those species with enough collecting data (range 11–158, see Table 1). The
database of records is available upon request.
Study area, background and
environmental predictors
We have modeled the potential
distribution of Argentinian species including cases outside the country’s
boundaries. Our study area included land between latitudes -55.08 and -21.55S, and
longitudes -75.30 to -53.13W. As bioclimatic variables, we used the WorldClim
1.4 (www.worldclim.org) data set (Hijmans et al.
2005) at 0.041666669 cell size. To establish a background and a set of
uncorrelated climatic variables, we intersected the variables with target group
points, and with 10,000 points randomly selected in the extension of the study
area (M). We eliminated some variables with an exploratory data analysis and
Pearson correlation analysis (values >0.7). Thus, we selected variables with
low correlation and high contribution to reduce the parametrization of the
models. After this, the final data set included uncorrelated variables which
had more biological importance for our study species, and contributed the most
according to the jackknife analysis. Variables were: mean diurnal range (bio
02), isothermality (bio 03), temperature seasonality
(bio 04), mean temperature of driest quarter (bio 09), mean temperature of
warmest quarter (bio 10), precipitation of wettest month (bio 13),
precipitation seasonality (bio 15), precipitation of driest quarter (bio 17),
precipitation of warmest quarter (bio 18), and precipitation of the coldest
quarter (bio 19).
Background selection
To choose the best background,
preliminary species distribution models were generated with Maxent 3.3.3k
(Phillips et al. 2006) with target group points (with 10,000 points randomly
selected in the extension of the study area, M), and with a special extent
delineating M for each particular species with ecoregions (World Wildlife Fund;
www.worldwildlife.org/ date accessed 20 January 2018). Models were constructed
by setting several parameters to default (‘Auto features’, convergence= 10-5,
maximum number of iterations= 500). However, we used random seed (with a 30
test percentage), 10 replicates, removed duplicate records, ran bootstrap
replicated type, with no extrapolation and no clamping. All this to find which
combination of settings and variables generated the best outcomes (highest area
under the curve, or AUC) while minimizing the number of model parameters, as
well as producing ‘closed’, bell-shaped response curves guaranteeing model
calibration (Elith et al. 2010). The best background
by the preliminary analyses was 10,000 points randomly selected in the
extension of the study area.
Training ecological niche models
Final models were built with
BIOMOD (Biodiversity Modelling) package in R software. This package is a
platform for predicting species’ distribution, including the ability to model
the distribution using various techniques and test patterns (Thuiller et al. 2009). We trained models using four widely
used algorithms: maximum entropy (Maxent), random forest (RF), generalized
boosting methods (GBM), and multivariate adaptive regression splines (MARS).
These models have shown good performance in terms of predictive power (Broennimann et al. 2012; Pliscoff
& Fuentes-Castillo 2011; Reiss et al. 2011). From individual models
obtained with these different algorithms, we generated a ‘consensus
model’. Such model combination is the
best logistic compromise to avoid either overfitting and overpredicting (Merow et al. 2014). In other words, this reduces biases and
limitations of using only individual models. Seventy percent of data was used
for training, and 30% for validation with 10 replicates. Final model validation
was performed with TSS (True Skill Statistics), average net rate of successful
prediction for sites of presence and absence (Liu et al. 2009), ranging from -1
to 1, where the more positive values indicate a higher degree of accuracy and
discrimination model (Allouche et al. 2006) (Table
1). Notice that the result of these models is not the area that species occupy
absolutely, because they do not consider population dynamics, dispersibility,
interactions with other species, and human impacts. However, these models
predict where species can be potentially found given their environmental
conditions. This assumes that the distribution known of each species provides
enough information to characterize its environmental requirements.
A total of 224 models were
generated, whose performance was assessed by means of the AUC and TSS
statistics (Table 1), while minimizing the number of model parameters, and the
best presence/absence models using the ‘10 percentile-training presence’ are
shown. This threshold was used because we prefer to err in the side of caution
accepting that a 10% of our presences could be problematic (for a similar
rationale, see Sánchez-Guillén et al. 2013). The best models of current
climatic conditions of species were used to generate projections.
Future projections
The best models of current
climatic conditions of species were used to generate projections for the 2050
year assuming climatic change scenarios. The data for future projections were:
Global Climate Models (GCM) (CNRM-CM5, HadGEM2-ES, and MPI-ESM-LR) in WorldClim (http://worldclim.org/CMIP5v1; date accessed 12
December 2017), these climate projections were gathered from the Fifth
Assessment (CMIP5) (http://cmip-pcmdi.llnl.gov/cmip5/ date accessed 19/7/2017)
report of The Intergovernmental Panel on Climate Change (IPPC)
(http://www.ipcc.ch/). The representative concentration pathways used (RCP)
were 4.5 and 8.5, for year 2050. A RCP 8.5 is considered a pessimistic
scenario, where CO2 emissions would continue to rise while a RCP 4.5
is considered a more optimistic situation.
We estimated areas of potential distribution of odonate species occurring within Argentinian borders in km2, and calculated the percentage
of loss or gain of geographic areas with respect to current potential
distribution. 2050 distribution was represented by a consensus model where only
pixels-predicted-present by all models were considered as representing the
presence of the species. We estimated areas with a function with stringr and raster packages in R (R Core Team 2017).
Results
Table 1 shows the potential
current distribution (in km2) for each species, and the summary of
the performance of the best models (with TSS). This table also shows the
current IUCN Red List categories (as of 28 January 2018) and the new categories
we suggest based on our analysis of distribution area. From these data, only Lestes dichrostigma
Calvert, 1909
appears as ‘Near Threatened’ as
its estimated distribution area is 28,823 km2 (Figure 1). This as
well as other seven species deserve some attention given that their
distribution is less than- or close to 100,000 km2 (Figure 1): Elasmothemis cannacrioides
(Calvert,
1906), Erythemis credula (Hagen, 1861), Erythrodiplax paraguayensis
(Förster, 1905), Heteragrion angustipenne
Selys, 1886, H. inca
Calvert, 1909, Lestes
forficula Rambur, 1842 and Mecistogaster linearis
(Fabricius, 1777). Distributions of all species are included in
supplementary material Figure 1.
In regard to climate change
projections for the year 2050 the RCP 8.5 estimated the following: 12 species
would maintain their distribution with loss or gain of only around 10% of
change of their current distribution, four species would increase their
distribution beyond 10%, and 28 species would lose their area of their
distribution for more than 10% (Table 2). These changes, in general, were
fairly consistent with the scenario RCP 4.5 with three species keeping their
distribution for around 10% of change, 11 species increasing their distribution
beyond 10%, and 30 species losing their distribution for more than 10% (Table
2). These coincidences for both scenarios include, for example, Micrathyria tibialis Kirby, 1897 and Heteragrion angustipenne
Selys, 1886 which represent the extremes in terms
of gaining and losing area, respectively.
Discussion
One benefit species distribution
models can bring about is the conservation aspects. In this extent, our results
suggest that although most Argentinian species have relatively large
distributions, a few species deserve some attention. According to the current
IUCN Red List (IUCN 2018), the following species face some risk: Andinagrion garrisoni von
Ellenrieder & Muzón,
2006 and Progomphus kimminsi
Belle, 1973 (Near Threatened), Phyllogomphoides
joaquini Rodrigues Capitulo,
1992 (Vulnerable) and Staurophlebia bosqi Navás, 1927 (Endangered).
The remaining 82 are categorized as Data Deficient (4 species) or Least Concern
(78 species). The threatened four species were classified as such given the
paucity of collecting records and their restricted areas of distribution. We
were not able to locate enough collecting points for any of these four species.
However, our work suggests that Lestes dichrostigma Calvert, 1909 deserves some
attention, as its area is above but close to 20,000 km2. Although
the remaining 43 species can be categorized as least concern, another five have
less than 100,000 km2 so we suggest their populations should be also
monitored: Elasmothemis cannacrioides (Calvert, 1906), Erythemis credula
(Hagen, 1861), Erythrodiplax paraguayensis (Förster,
1905), Heteragrion angustipenne
Selys, 1886, H. inca
Calvert, 1909, L. forficula Rambur, 1842, and Mecistogaster linearis
(Fabricius, 1777). Of course, several other population parameters
should be gathered to complement IUCN categorization for all species, for
example to detect the population reduction or less of variability. Notice that
future projections would not help most species we modelled as 28–30 species
would reduce their distribution dramatically in some cases. According to this,
some other species not in danger currently would face threat according to these
future scenarios: Acanthagrion hidegarda Gloger, 1967, Heteragrion angustipenne
Selys, 1886, Lestes dichrostigma
Calvert, 1909, Mecistogaster linearis
(Fabricius, 1777), and Rhionaeschna
viginpunctata (Ris, 1918). These five species may reduce
their area to less than 20,000 km2.
Essential to our present
estimates of area is the fact that 70% of Argentinian species are currently
present in protected areas (Muzón & von Ellenrieder 1999). However, given that global change will
lead to shifts in current distribution (Sánchez-Guillén
et al. 2016), a necessary step is to define whether current Argentinian
protected areas will still embrace future odonate
geographical distributions. A key issue here is to carry out more intensive
collections to construct models for the remaining 227 odonate
species that occur within Argentinian boundaries (von Ellenrieder
& Muzón 2008). Moreover, research should pay
attention to answer whether dispersal abilities can allow odonates
catch up with different habitats located at different temperature regimes (Bush
et al. 2014).
Related to global change
scenarios, it is not surprising to find an inter-specific variation in
projected responses to raising temperatures in odonates.
Our explanations for this are incomplete yet but may have to do with odonate physiological abilities that affect themoregulatory responses (e.g., Corbet & May 2008) and
development (especially at egg and larval stages; Pritchard & Leggot 1987). Given this, it is also not surprising that
the largest species turnover will occur at intermediate altitudes where drastic
changes in temperature currently occur (Maes et al.
2010). The case of Argentina is actually very relevant to this altitude
phenomenon given its sharp changes in elevation. Thus, special attention should
be given to these areas. Given the small number of records for most species, we
are far from ensuring a well-known distribution for a large number of Argentine
species, where field work, as well as the digitization of records, is advisable
to document regions that are poorly explored. One tool to help in this regard
is the use of repositories of citizen science photographs.
Apart from North America (Canada
and USA; Hassall 2012; Rangel-Sanchez et al. 2018) and Brazil (Nóbrega & De Marco 2011), our study adds a substantially
high number of odonate species with projected
distributions for America. Considering that there exist around 5,680 described odonate species, of which 25% had been modelled (Collins
& McIntyre 2015), our study makes a valuable global contribution for the
Southern Hemisphere. This importance can be seen not only in terms of
conservation as discussed above, but also in terms of biogeography given the
southerly location of our study species (currently, the southern extreme was
Brazil with mainly tropical species; De Marco et al. 2015; Nóbrega
& De Marco 2011). Thus our results can be used to understand
biogeographical patterns based on odonate ecology
(e.g., preference for lentic and lotic waters and global distribution; Hof et
al. 2006).
Table 1. Argentinian odonates species modeled, number
of records, potential distribution of species in km2, TSS values and
current and proposed IUCN categories.
Species |
Records |
Current area (km2) |
TSS |
Current IUCN status |
Suggested IUCN status |
Acanthagrion aepiolum Tennessen, 2004 |
23 |
206259 |
0.90 |
N/A |
LC |
A. cuyabae
Calvert,
1909 |
55 |
1136583 |
0.86 |
LC |
LC |
A. floridense
Fraser,
1946 |
47 |
166257 |
0.89 |
N/A |
LC |
A. gracile (Rambur, 1842) |
43 |
865415 |
0.85 |
N/A |
LC |
A. hidegarda
Gloger,
1967 |
27 |
112352 |
0.90 |
N/A |
LC |
A. lancea
Selys, 1876 |
48 |
645339 |
0.87 |
N/A |
LC |
Elasmothemis cannacrioides (Calvert, 1906) |
12 |
79208 |
0.83 |
N/A |
LC |
Erythemis attala (Selys in Sagra,
1857) |
70 |
368120 |
0.89 |
LC |
LC |
E. credula (Hagen, 1861) |
16 |
67990 |
0.86 |
N/A |
LC |
E. peruviana (Rambur, 1842) |
72 |
1056558 |
0.86 |
LC |
LC |
E. plebeja
(Burmeister,
1839) |
94 |
1523637 |
0.84 |
LC |
LC |
E. vesiculosa (Fabricius, 1775) |
132 |
2228200 |
0.81 |
LC |
LC |
Erythrodiplax fusca (Rambur, 1842) |
22 |
173798 |
0.90 |
LC |
LC |
E. paraguayensis (Förster, 1905) |
11 |
40995 |
0.80 |
LC |
LC |
E. umbrata
(Linnaeus,
1758) |
59 |
184811 |
0.90 |
LC |
LC |
Heteragrion angustipenne Selys, 1886 |
14 |
74209 |
0.84 |
N/A |
LC |
H. inca Calvert, 1909 |
13 |
102730 |
0.82 |
N/A |
LC |
Ischnura capreolus (Hagen, 1861) |
139 |
734839 |
0.88 |
N/A |
LC |
I. fluviatilis
Selys, 1876 |
158 |
1714797 |
0.83 |
LC |
LC |
I. ultima Ris, 1908 |
34 |
11808573 |
0.90 |
N/A |
LC |
Lestes dichrostigma Calvert, 1909 |
11 |
28823 |
0.80 |
LC |
NT |
L. forficula Rambur, 1842 |
14 |
72423 |
0.83 |
N/A |
LC |
L. spatula Fraser, 1946 |
30 |
504657 |
0.88 |
N/A |
LC |
L. undulatus
Say, 1840 |
34 |
195329 |
0.89 |
LC |
LC |
Mecistogaster linearis (Fabricius, 1777) |
13 |
71030 |
0.82 |
N/A |
LC |
Miathyria marcella (Selys in Sagra, 1857) |
44 |
4166276 |
0.87 |
LC |
LC |
Micrathyria hesperis Ris, 1911 |
19 |
7900041 |
0.87 |
N/A |
LC |
M. hypodidyma
Calvert,
1906 |
33 |
653996 |
0.88 |
N/A |
LC |
M. longifasciata
Calvert,
1909 |
48 |
416857 |
0.89 |
LC |
LC |
M. tibialis Kirby, 1897 |
11 |
184013 |
0.80 |
LC |
LC |
Orthemis ferruginea (Fabricius, 1775) |
13 |
1401215 |
0.79 |
LC |
LC |
Pantala flavescens (Fabricius, 1798) |
17 |
387339 |
0.85 |
LC |
LC |
Perithemis mooma Kirby, 1889 |
15 |
829042 |
0.83 |
N/A |
LC |
Rhionaeschna absoluta (Calvert, 1952) |
133 |
934413 |
0.86 |
N/A |
LC |
R. bonariesis
(Rambur,
1842) |
158 |
1417407 |
0.84 |
N/A |
LC |
R. confusa
(Rambur,
1842) |
52 |
261179 |
0.88 |
N/A |
LC |
R. diffinis
(Rambur,
1842) |
40 |
226574 |
0.89 |
LC |
LC |
R. pallipes
(Fraser,
1947) |
26 |
142412 |
0.89 |
N/A |
LC |
R. planaltica
(Calvert,
1952) |
51 |
163524 |
0.89 |
LC |
LC |
R. variegata
(Fabricius, 1775) |
41 |
365158 |
0.88 |
N/A |
LC |
R. viginpunctata
(Ris, 1918) |
47 |
155497 |
0.90 |
N/A |
LC |
Tramea darwini Kirby, 1889 |
16 |
321819 |
0.85 |
LC |
LC |
Uracis fastigiata (Burmeister, 1839) |
17 |
760515 |
0.85 |
N/A |
LC |
U. imbuta
(Burmeister,
1839) |
22 |
830556 |
0.84 |
N/A |
LC |
Table 2. Absolute (in km2)
and relative changes in suitable area per Argentinian odonate
species according to different climatic changes scenarios. Losses are shown as
negative values while gains are shown as positive values.
Species |
2050 (km2) RCP4.5 |
2050 (km2) RCP8.5 |
2050 (%) RCP4.5 |
2050 (%) RCP8.5 |
Acanthagrion aepiolum Tennessen, 2004 |
95025 |
77268 |
-53.93 |
-62.54 |
A. cuyabae
Calvert,
1909 |
1085251 |
1128738 |
-4.52 |
-0.69 |
A. floridense
Fraser,
1946 |
124121 |
148521 |
-25.34 |
-10.67 |
A. gracile (Rambur, 1842) |
511056 |
459049 |
-40.95 |
-46.96 |
A. hidegarda
Gloger,
1967 |
7430 |
7418 |
-93.39 |
-93.40 |
A. lancea
Selys, 1876 |
334559 |
328591 |
-48.16 |
-49.08 |
Elasmothemis cannacrioides (Calvert, 1906) |
26652 |
20123 |
-66.35 |
-74.59 |
Erythemis attala (Selys in Sagra,
1857) |
1040509 |
1672709 |
182.65 |
354.39 |
E. credula (Hagen, 1861) |
104121 |
181602 |
53.14 |
167.10 |
E. peruviana (Rambur, 1842) |
3475030 |
3977046 |
228.90 |
276.42 |
E. plebeja
(Burmeister,
1839) |
2875597 |
3578859 |
88.73 |
134.89 |
E. vesiculosa (Fabricius, 1775) |
6394736 |
8249237 |
186.99 |
270.22 |
Erythrodiplax fusca (Rambur, 1842) |
203928 |
185469 |
17.34 |
6.72 |
E. paraguayensis
(Förster, 1905) |
29488 |
30549 |
-28.07 |
-25.48 |
E. umbrata
(Linnaeus,
1758) |
1107621 |
1462042 |
499.33 |
691.10 |
Heteragrion angustipenne Selys, 1886 |
2709 |
566 |
-96.35 |
-99.24 |
H. inca
Calvert,
1909 |
27552 |
18234 |
-73.18 |
-82.25 |
Ischnura capreolus (Hagen, 1861) |
5444382 |
6676849 |
640.89 |
808.61 |
I. fluviatilis
Selys, 1876 |
1087445 |
1034849 |
-36.58 |
-39.65 |
I. ultima Ris, 1908 |
1438693 |
1637097 |
-87.82 |
-86.14 |
Lestes dichrostigma Calvert, 1909 |
1497 |
1456 |
-94.81 |
-94.95 |
L. forficula Rambur, 1842 |
61821 |
78055 |
-14.64 |
7.78 |
L. spatula Fraser, 1946 |
297025 |
323398 |
-41.14 |
-35.92 |
L. undulatus
Say, 1840 |
177025 |
181143 |
-9.37 |
-7.26 |
Mecistogaster linearis (Fabricius, 1777) |
5896 |
2538 |
-91.70 |
-96.43 |
Miathyria marcella (Selys in Sagra, 1857) |
8903701 |
9675724 |
113.71 |
132.24 |
Micrathyria hesperis Ris, 1911 |
1325471 |
1539839 |
-83.22 |
-80.51 |
M. hypodidyma
Calvert,
1906 |
360230 |
360273 |
-44.92 |
-44.91 |
M. longifasciata
Calvert,
1909 |
301298 |
304006 |
-27.72 |
-27.07 |
M. tibialis Kirby, 1897 |
3288689 |
4500751 |
1687.21 |
2345.89 |
Orthemis ferruginea (Fabricius, 1775) |
856545 |
573823 |
-38.87 |
-59.05 |
Pantala flavescens (Fabricius, 1798) |
345606 |
358468 |
-10.77 |
-7.45 |
Perithemis mooma Kirby, 1889 |
586843 |
671876 |
-29.21 |
-18.96 |
Rhionaeschna absoluta (Calvert, 1952) |
775879 |
740279 |
-16.97 |
-20.78 |
R. bonariesis
(Rambur,
1842) |
713468 |
711143 |
-49.66 |
-49.83 |
R. confusa
(Rambur,
1842) |
211253 |
216912 |
-19.12 |
-16.95 |
R. diffinis
(Rambur,
1842) |
262980 |
259209 |
16.07 |
14.40 |
R. pallipes
(Fraser,
1947) |
70805 |
75227 |
-50.28 |
-47.18 |
R. planaltica
(Calvert,
1952) |
45782 |
44497 |
-72.00 |
-72.79 |
R. variegata
(Fabricius, 1775) |
295227 |
300756 |
-19.15 |
-17.64 |
R. viginpunctata
(Ris, 1918) |
89497 |
89484 |
-42.44 |
-42.45 |
Tramea darwini Kirby, 1889 |
343101 |
337055 |
6.61 |
4.73 |
Uracis fastigiata (Burmeister, 1839) |
223876 |
175053 |
-70.56 |
-76.98 |
U. imbuta
(Burmeister,
1839) |
416894 |
126006 |
-49.81 |
-84.83 |
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