Fil: Rabinovich, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; Argentin

Rabinovich, Jorge Eduardo

Philosophical Transactions of the Royal Society B Biological Sciences

English

PubMed

LAReferencia - Red Federada de Repositorios Institucionales de Publicaciones Científicas Latinoamericanas

rstb.royalsocietypublishing.orgInvited replyCite this article: Rabinovich J. 2016 Eco-logical niche modelling in triatomines – abususnon tollit usum: a reply to Gutiérrez (2016).Phil. Trans. R. Soc. B 371: 20160188.http://dx.doi.org/10.1098/rstb.2016.0188Accepted: 12 April 2016Author for correspondence:Jorge Rabinoviche-mail: jorge.rabinovich@gmail.com& 2016 The Author(s) Published by the Royal Society. All rights reserved.The accompanying comment can be viewed athttp://dx.doi.org/10.1098/rstb.2016.0027.Ecological niche modelling intriatomines – abusus non tollitusum: a reply to Gutiérrez (2016)Jorge RabinovichCentro de Estudios Parasitológicos y de Vectores (CONICET, CCT- La Plata, UNLP), Universidad Nacional de LaPlata, Bulevar 120s/n e/61 y 62, La Plata, Provincia de Buenos Aires B1902CHX, ArgentinaJR, 0000-0002-3792-742XIn [1], the vectorial risk of Chagas disease transmission was estimated from theclimatic suitability expected for Rhodnius prolixus and Triatoma infestans by year2050 in Argentina and Venezuela using ecological niche modelling (ENM), andGutierrez [2] claimed that the nature of the data and the study regions mightinvalidate the conclusions. Although this warning is fit and welcomed, in tria-tomines and for particular goals the use of ENM, though not complying withsome methodological prescriptions, can be justified.In [1], data quality controls were applied: R. prolixus was deleted from Boliviaand southern Amazonia, T. infestans was pooled with T. melanosoma, andextended in Bolivia; coordinates were deleted using a database with georefer-enced water bodies. Elevational false positives claimed by Gutierrez [2] are notsuch (by mistake, Medone et al. [1] reported the altitudinal limits given byCarcavallo et al. [3] instead of the ones of the data used). Other false positives men-tioned by Gutierrez [2] were R. prolixus in Costa Rica (though its presence therehas been confirmed [4,5]), and in Panama, the latter an historical taxonomic error.Lack of prediction errors from the use of confirmed occurrences was criticizedby Gutierrez [2]; however, MAXENT was not run with confirmed presence data:only predictions from range maps with confirmed presences were checked. Weapologize for not reporting the geographical projection of the models, whichwas the Americas, between 44 N and 247 S, and 234 E and 2125 E.The study regions should not include areas that cannot be accessed due todispersal problems, but there are some caveats in triatomines: (i) triatominesare detectable in restricted time/space-windows (time of day or year, type ofhabitat), affecting the pseudo-presence/pseudo-absence problem more than inac-cessible areas; (ii) sampling triatomines is biased: most sampling is associatedwith rural houses in detriment to sylvatic habitats (‘biased in environmental dimen-sions’ [6]), inducing more noise than the pseudo-presence problem; (iii) the use ofdomiciliated microhabitats dampens climatic effects [7,8], minimizing pseudo-presences (areas nearby confirmed presence have a higher possibility of beingpseudo-absence than pseudo-presence); and (iv) in addition to their own dispersalcapacities, triatomines are also transported by humans and animals, resulting inrange shifts that outweigh the pseudo-presence/pseudo-absence problems.The pseudo-presence and background problems raised by Gutierrez [2] arepossibly the strongest criticism to [1], regardless of the use of surveys and rangemaps to estimate richness results in under- and overestimation, respectively [9].Is not complying with the use of confirmed occurrences a serious violation intriatomines? In T. infestans using the average first nearest-neighbour distance(between confirmed occurrences and random coordinates from [3]) was29.1 km (26.4–31.9 km 95% CI; n ¼ 640). Checking the geographical autocorre-lation for six bioclimatic predictors on arbitrary ‘transects’ running alonglongitudes and ‘crossing’ through various latitudes, in plain and mountainousregions in Argentina, Colombia and Venezuela, there was a significant spatialautocorrelation of 100–300 and 50–80 km in the plain and mountainousareas, respectively. With the nearest-neighbour distances within the spatialrstb.royalsocietypublishing.orgPhil.Trans.R.Soc.B371:201601882autocorrelation scale of the bioclimatic variables the use ofrandom coordinates from the range maps would notinvalidate the conclusions of Medone et al. [1].The shapes of BAM diagrams [6,10] depend upon thespecies’ eco-physiological and behavioural characteristicsand the abiotic and biotic environments; triatomine BAM dia-grams (TBAM) are different to classical BAM diagrams fortriatomines are characterized by: (i) a high mobility (active þpassive) so factor M in the TBAM includes factors A and B(and is close to their union, A < B ); (ii) an eclectic diet, soB overlaps closely with A; and (iii) a selective microhabitatuse, so components A, B and M are extremely close to thegeographic area (G). Using the shape of the Americas as G,T. infestans shows a disjoint BAM, while R. prolixus shows amore ‘classical’ BAM, but for both species the M ‘envelope’encompasses practically the whole of A and B regions. Thispeculiar TBAM justifies a large modelling background thatincludes almost all of the Americas.Range maps are scale-dependent abstractions of speciesdistributions [9], with resolutions less than 28 (200 km)usually overestimating the occupancy area of individualspecies [11]; as the purpose was to link environmental suit-ability to epidemiological risk, Medone et al. [1] preferredto err towards an overestimation rather than to an underesti-mation. Exceptions to the recommended methodology (suchas using confirmed occurrences in MAXENT) are regularlypublished when a specific objective justifies them [12,13].The large M area of T. infestans and R. prolixus is alsoinfluenced by the B region; triatomine presence is mainlydetermined by the host species (pathogens, parasites, preda-tors and competitors have a much smaller role), and a lack ofnegative interactions across G makes the B area smaller thanor equal to abiotic GA [6]. As the performance of MAXENT issensitive to the size of the background, this results in a con-undrum for triatomines, because the large M area wouldjustify using a large background.An ongoing analysis of three triatomine species [14], whereconfirmed occurrences for Panstrongylus geniculatus, P. megistusand T. guasayana (with 189, 593 and 90 coordinates, respect-ively) were compared with a random selection of range mapsfrom [3] using MAXENT and GARP software, showed no statisti-cally significant differences in the suitability predictionsbetween the two datasets using three methods of evaluation(average AUC curves from 10 replicates, threshold-dependentkappa and True Skill Statistics).Being the first application of a methodology to convertsuitability predictions into epidemiological risk, Medoneet al. [1] stated that results should not be taken as a policyguide. Owing to the short space available for this reply,details on the statistical tests and full arguments here usedwill appear in a full paper that is underway. Nevertheless, Iconsider that the observations and caveats posed in [2] arevalid in general, and I thank this author for pointingout some incomplete descriptions in [1]; I believe that hisobservations about potentially dangerous applications ofthe MAXENT software, if used inadequately, will be welcomedby readers and practitioners, particularly if they keep in mind‘abusus non tollit usum’ (misuse does not remove use).References1. Medone P, Ceccarelli S, Parham PE, Figuera A,Rabinovich JE. 2015 The impact of climate changeon the geographical distribution of two vectors ofChagas disease: implications for the force ofinfection. Phil. Trans. R. Soc. B 370, 20130560.(doi:10.1098/rstb.2013.0560)2. Gutiérrez EE. 2016 Ecological niche modelling requiresreal presence data and appropriate study regions: acomment on Medone et al. (2015). Phil. Trans. R. Soc.B 371, 20160027. (doi:10.1098/rstb.2016.0027)3. Carcavallo RU, Galı́ndez Girón I, Jurberg J, Lent H.1999 Geographical distribution and alti-latitudinaldispersion of Triatominae. In Atlas of Chagas’disease vectors in the Americas, vol. 3 (eds RUCarcavallo, I Galı́ndez Girón, J Jurberg, H Lent),pp. 747 – 792. Rio de Janeiro, Brasil: FIOCRUZ.4. Neiva A, Lent H. 1936 Notas e commentarios sôbretriatomideos. Lista de especies e sua distribuçãogeographica. Apuntes y comentarios sobre lostriatómidos. Lista de especies y su distribucióngeográfica. Rev. Entomol. (Rı́o de Janeiro) 6, 153– 190.5. Zeledón R. 2004 Some historical facts and recentissues related to the presence of Rhodnius prolixus(Stal, 1859) (Hemiptera: Reduviidae) inCentral America. Entomologı́a y Vectores 11,233 – 246.6. Peterson AT, Soberón J, Pearson RG, Anderson RP,Martı́nez-Meyer E, Nakamura M, Araújo MB. 2011Ecological niches and geographic distributions.Monographs in population biology (eds SA Levin,HS Horn), pp 1 – 376. Princeton, NJ: PrincetonUniversity Press.7. Heger TJ, Guerin PM, Eugster W. 2006 Microclimaticfactors influencing refugium suitability for Rhodniusprolixus. Physiol. Entomol. 31, 248 – 256. (doi:10.1111/j.1365-3032.2006.00514.x)8. Lorenzo MG, Lazzari CR. 1999 Temperature andrelative humidity affect the selection of shelters byTriatoma infestans, Vector of Chagas disease. ActaTrop. 72, 241 – 249. (doi:10.1016/S0001-706X(98)00094-1)9. Hurlbert AH, White EP. 2005 Disparity betweenrange map-and survey-based analyses of speciesrichness: patterns, processes and implications. Ecol.Lett. 8, 319 – 327. (doi:10.1111/j.1461-0248.2005.00726.x)10. Soberón J, Peterson AT. 2005 Interpretation ofmodels of fundamental ecological niches andspecies’ distributional areas. Biodivers. Inform. 2,1 – 10. (doi:10.17161/bi.v2i0.4)11. Hurlbert AH, Jetz W. 2007 Species richness,hotspots, and the scale dependence of range mapsin ecology and conservation. Proc. Natl Acad. Sci.USA 104, 13 384 – 13 389. (doi:10.1073/pnas.0704469104)12. Diniz-Filho JA, Bini LM, Rangel TF, Loyola RD, Hof C,Nogués-Bravo D, Araújo MB. 2009 Partitioningand mapping uncertainties in ensembles offorecasts of species turnover under climate change.Ecography 32, 897 – 906. (doi:10.1111/j.1600-0587.2009.06196.x)13. Lawler JJ, Shafer SL, White D, Kareiva P, Maurer EP,Blaustein AR, Bartlein PJ. 2009 Projected climate-induced faunal change in the WesternHemisphere. Ecology 90, 588 – 597. (doi:10.1890/08-0823.1)14. Rocha Leite G, Vezzani D, Wisnivesky C, Rabinovich J.In preparation. Niche prediction in triatomines:methodological and conceptual aspects.

Ecological niche modelling in triatomines–abusus non tollit usum: A reply to gutiérrez (2016)

In [1], the vectorial risk of Chagas disease transmission was estimated from the climatic suitability expected for Rhodnius prolixus and Triatoma infestans by year 2050 in Argentina and Venezuela using ecological niche modelling (ENM), and Gutierrez [2] claimed that the nature of the data and the study regions might invalidate the conclusions. Although this warning is fit and welcomed, in triatomines and for particular goals the use of ENM, though not complying with some methodological prescriptions, can be justified.Centro de Estudios Parasitológicos y de Vectore

El Servicio de Difusión de la Creación Intelectual

rstb.royalsocietypublishing.orgInvited reply
Cite this article: Rabinovich J. 2016 Eco-
logical niche modelling in triatomines–abusus
non tollit usum: a reply to Gutie´rrez (2016).
Phil. Trans. R. Soc. B 371: 20160188.
http://dx.doi.org/10.1098/rstb.2016.0188
Accepted: 12 April 2016Author for correspondence:
Jorge Rabinovich
e-mail: jorge.rabinovich@gmail.com& 2016 The Author(s) Published by the Royal Society. All rights reserved.The accompanying comment can be viewed at
http://dx.doi.org/10.1098/rstb.2016.0027.Ecological niche modelling in
triatomines–abusus non tollit
usum: a reply to Gutie´rrez (2016)
Jorge Rabinovich
Centro de Estudios Parasitolo´gicos y de Vectores (CONICET, CCT- La Plata, UNLP), Universidad Nacional de La
Plata, Bulevar 120s/n e/61 y 62, La Plata, Provincia de Buenos Aires B1902CHX, Argentina
JR, 0000-0002-3792-742XIn [1], the vectorial risk of Chagas disease transmission was estimated from the
climatic suitability expected for Rhodnius prolixus and Triatoma infestans by year
2050 in Argentina and Venezuela using ecological niche modelling (ENM), and
Gutierrez [2] claimed that the nature of the data and the study regions might
invalidate the conclusions. Although this warning is fit and welcomed, in tria-
tomines and for particular goals the use of ENM, though not complying with
some methodological prescriptions, can be justified.
In [1], data quality controls were applied: R. prolixuswas deleted from Bolivia
and southern Amazonia, T. infestans was pooled with T. melanosoma, and
extended in Bolivia; coordinates were deleted using a database with georefer-
enced water bodies. Elevational false positives claimed by Gutierrez [2] are not
such (by mistake, Medone et al. [1] reported the altitudinal limits given by
Carcavallo et al. [3] instead of the ones of the data used). Other false positivesmen-
tioned by Gutierrez [2] were R. prolixus in Costa Rica (though its presence there
has been confirmed [4,5]), and in Panama, the latter an historical taxonomic error.
Lack of prediction errors from the use of confirmed occurrences was criticized
by Gutierrez [2]; however, MAXENT was not run with confirmed presence data:
only predictions from range maps with confirmed presences were checked. We
apologize for not reporting the geographical projection of the models, which
was the Americas, between 44 N and 247 S, and 234 E and 2125 E.
The study regions should not include areas that cannot be accessed due to
dispersal problems, but there are some caveats in triatomines: (i) triatomines
are detectable in restricted time/space-windows (time of day or year, type of
habitat), affecting the pseudo-presence/pseudo-absence problem more than inac-
cessible areas; (ii) sampling triatomines is biased: most sampling is associated
with rural houses in detriment to sylvatic habitats (‘biased in environmental dimen-
sions’ [6]), inducing more noise than the pseudo-presence problem; (iii) the use of
domiciliated microhabitats dampens climatic effects [7,8], minimizing pseudo-
presences (areas nearby confirmed presence have a higher possibility of being
pseudo-absence than pseudo-presence); and (iv) in addition to their own dispersal
capacities, triatomines are also transported by humans and animals, resulting in
range shifts that outweigh the pseudo-presence/pseudo-absence problems.
The pseudo-presence and background problems raised by Gutierrez [2] are
possibly the strongest criticism to [1], regardless of the use of surveys and range
maps to estimate richness results in under- and overestimation, respectively [9].
Is not complying with the use of confirmed occurrences a serious violation in
triatomines? In T. infestans using the average first nearest-neighbour distance
(between confirmed occurrences and random coordinates from [3]) was
29.1 km (26.4–31.9 km 95% CI; n ¼ 640). Checking the geographical autocorre-
lation for six bioclimatic predictors on arbitrary ‘transects’ running along
longitudes and ‘crossing’ through various latitudes, in plain and mountainous
regions in Argentina, Colombia and Venezuela, there was a significant spatial
autocorrelation of 100–300 and 50–80 km in the plain and mountainous
areas, respectively. With the nearest-neighbour distances within the spatial
rstb.royalsocietypublishing.org
Phil.Trans.R.Soc.B
371:20160188
2autocorrelation scale of the bioclimatic variables the use of
random coordinates from the range maps would not
invalidate the conclusions of Medone et al. [1].
The shapes of BAM diagrams [6,10] depend upon the
species’ eco-physiological and behavioural characteristics
and the abiotic and biotic environments; triatomine BAM dia-
grams (TBAM) are different to classical BAM diagrams for
triatomines are characterized by: (i) a high mobility (active þ
passive) so factor M in the TBAM includes factors A and B
(and is close to their union, A< B ); (ii) an eclectic diet, so
B overlaps closely with A; and (iii) a selective microhabitat
use, so components A, B and M are extremely close to the
geographic area (G). Using the shape of the Americas as G,
T. infestans shows a disjoint BAM, while R. prolixus shows a
more ‘classical’ BAM, but for both species the M ‘envelope’
encompasses practically the whole of A and B regions. This
peculiar TBAM justifies a large modelling background that
includes almost all of the Americas.
Range maps are scale-dependent abstractions of species
distributions [9], with resolutions less than 28 (200 km)
usually overestimating the occupancy area of individual
species [11]; as the purpose was to link environmental suit-
ability to epidemiological risk, Medone et al. [1] preferred
to err towards an overestimation rather than to an underesti-
mation. Exceptions to the recommended methodology (such
as using confirmed occurrences in MAXENT) are regularly
published when a specific objective justifies them [12,13].
The large M area of T. infestans and R. prolixus is also
influenced by the B region; triatomine presence is mainlydetermined by the host species (pathogens, parasites, preda-
tors and competitors have a much smaller role), and a lack of
negative interactions across G makes the B area smaller than
or equal to abiotic GA [6]. As the performance of MAXENT is
sensitive to the size of the background, this results in a con-
undrum for triatomines, because the large M area would
justify using a large background.
An ongoing analysis of three triatomine species [14], where
confirmed occurrences for Panstrongylus geniculatus, P. megistus
and T. guasayana (with 189, 593 and 90 coordinates, respect-
ively) were compared with a random selection of range maps
from [3] usingMAXENT and GARP software, showed no statisti-
cally significant differences in the suitability predictions
between the two datasets using three methods of evaluation
(average AUC curves from 10 replicates, threshold-dependent
kappa and True Skill Statistics).
Being the first application of a methodology to convert
suitability predictions into epidemiological risk, Medone
et al. [1] stated that results should not be taken as a policy
guide. Owing to the short space available for this reply,
details on the statistical tests and full arguments here used
will appear in a full paper that is underway. Nevertheless, I
consider that the observations and caveats posed in [2] are
valid in general, and I thank this author for pointing
out some incomplete descriptions in [1]; I believe that his
observations about potentially dangerous applications of
the MAXENT software, if used inadequately, will be welcomed
by readers and practitioners, particularly if they keep in mind
‘abusus non tollit usum’ (misuse does not remove use).References1. Medone P, Ceccarelli S, Parham PE, Figuera A,
Rabinovich JE. 2015 The impact of climate change
on the geographical distribution of two vectors of
Chagas disease: implications for the force of
infection. Phil. Trans. R. Soc. B 370, 20130560.
(doi:10.1098/rstb.2013.0560)
2. Gutie´rrez EE. 2016 Ecological niche modelling requires
real presence data and appropriate study regions: a
comment on Medone et al. (2015). Phil. Trans. R. Soc.
B 371, 20160027. (doi:10.1098/rstb.2016.0027)
3. Carcavallo RU, Galı´ndez Giro´n I, Jurberg J, Lent H.
1999 Geographical distribution and alti-latitudinal
dispersion of Triatominae. In Atlas of Chagas’
disease vectors in the Americas, vol. 3 (eds RU
Carcavallo, I Galı´ndez Giro´n, J Jurberg, H Lent),
pp. 747–792. Rio de Janeiro, Brasil: FIOCRUZ.
4. Neiva A, Lent H. 1936 Notas e commentarios soˆbre
triatomideos. Lista de especies e sua distribuc¸a˜o
geographica. Apuntes y comentarios sobre los
triato´midos. Lista de especies y su distribucio´n
geogra´fica. Rev. Entomol. (Rı´o de Janeiro) 6, 153–190.
5. Zeledo´n R. 2004 Some historical facts and recent
issues related to the presence of Rhodnius prolixus(Stal, 1859) (Hemiptera: Reduviidae) in
Central America. Entomologı´a y Vectores 11,
233–246.
6. Peterson AT, Sobero´n J, Pearson RG, Anderson RP,
Martı´nez-Meyer E, Nakamura M, Arau´jo MB. 2011
Ecological niches and geographic distributions.
Monographs in population biology (eds SA Levin,
HS Horn), pp 1–376. Princeton, NJ: Princeton
University Press.
7. Heger TJ, Guerin PM, Eugster W. 2006 Microclimatic
factors influencing refugium suitability for Rhodnius
prolixus. Physiol. Entomol. 31, 248–256. (doi:10.
1111/j.1365-3032.2006.00514.x)
8. Lorenzo MG, Lazzari CR. 1999 Temperature and
relative humidity affect the selection of shelters by
Triatoma infestans, Vector of Chagas disease. Acta
Trop. 72, 241–249. (doi:10.1016/S0001-706X(98)
00094-1)
9. Hurlbert AH, White EP. 2005 Disparity between
range map-and survey-based analyses of species
richness: patterns, processes and implications. Ecol.
Lett. 8, 319–327. (doi:10.1111/j.1461-0248.2005.
00726.x)10. Sobero´n J, Peterson AT. 2005 Interpretation of
models of fundamental ecological niches and
species’ distributional areas. Biodivers. Inform. 2,
1–10. (doi:10.17161/bi.v2i0.4)
11. Hurlbert AH, Jetz W. 2007 Species richness,
hotspots, and the scale dependence of range maps
in ecology and conservation. Proc. Natl Acad. Sci.
USA 104, 13 384–13 389. (doi:10.1073/pnas.
0704469104)
12. Diniz-Filho JA, Bini LM, Rangel TF, Loyola RD, Hof C,
Nogue´s-Bravo D, Arau´jo MB. 2009 Partitioning
and mapping uncertainties in ensembles of
forecasts of species turnover under climate change.
Ecography 32, 897–906. (doi:10.1111/j.1600-0587.
2009.06196.x)
13. Lawler JJ, Shafer SL, White D, Kareiva P, Maurer EP,
Blaustein AR, Bartlein PJ. 2009 Projected climate-
induced faunal change in the Western
Hemisphere. Ecology 90, 588–597. (doi:10.1890/
08-0823.1)
14. Rocha Leite G, Vezzani D, Wisnivesky C, Rabinovich J.
In preparation. Niche prediction in triatomines:
methodological and conceptual aspects.


Ecological niche modelling in triatomines-abusus non tollit usum: a reply to Gutiérrez (2016)

CONICET Digital

rstb.royalsocietypublishing.orgInvited replyCite this article: Rabinovich J. 2016 Eco-logical niche modelling in triatomines–abususnon tollit usum: a reply to Gutie´rrez (2016).Phil. Trans. R. Soc. B 371: 20160188.http://dx.doi.org/10.1098/rstb.2016.0188Accepted: 12 April 2016Author for correspondence:Jorge Rabinoviche-mail: jorge.rabinovich@gmail.com& 2016 The Author(s) Published by the Royal Society. All rights reserved.The accompanying comment can be viewed athttp://dx.doi.org/10.1098/rstb.2016.0027.Ecological niche modelling intriatomines–abusus non tollitusum: a reply to Gutie´rrez (2016)Jorge RabinovichCentro de Estudios Parasitolo´gicos y de Vectores (CONICET, CCT- La Plata, UNLP), Universidad Nacional de LaPlata, Bulevar 120s/n e/61 y 62, La Plata, Provincia de Buenos Aires B1902CHX, ArgentinaJR, 0000-0002-3792-742XIn [1], the vectorial risk of Chagas disease transmission was estimated from theclimatic suitability expected for Rhodnius prolixus and Triatoma infestans by year2050 in Argentina and Venezuela using ecological niche modelling (ENM), andGutierrez [2] claimed that the nature of the data and the study regions mightinvalidate the conclusions. Although this warning is fit and welcomed, in tria-tomines and for particular goals the use of ENM, though not complying withsome methodological prescriptions, can be justified.In [1], data quality controls were applied: R. prolixuswas deleted from Boliviaand southern Amazonia, T. infestans was pooled with T. melanosoma, andextended in Bolivia; coordinates were deleted using a database with georefer-enced water bodies. Elevational false positives claimed by Gutierrez [2] are notsuch (by mistake, Medone et al. [1] reported the altitudinal limits given byCarcavallo et al. [3] instead of the ones of the data used). Other false positivesmen-tioned by Gutierrez [2] were R. prolixus in Costa Rica (though its presence therehas been confirmed [4,5]), and in Panama, the latter an historical taxonomic error.Lack of prediction errors from the use of confirmed occurrences was criticizedby Gutierrez [2]; however, MAXENT was not run with confirmed presence data:only predictions from range maps with confirmed presences were checked. Weapologize for not reporting the geographical projection of the models, whichwas the Americas, between 44 N and 247 S, and 234 E and 2125 E.The study regions should not include areas that cannot be accessed due todispersal problems, but there are some caveats in triatomines: (i) triatominesare detectable in restricted time/space-windows (time of day or year, type ofhabitat), affecting the pseudo-presence/pseudo-absence problem more than inac-cessible areas; (ii) sampling triatomines is biased: most sampling is associatedwith rural houses in detriment to sylvatic habitats (‘biased in environmental dimen-sions’ [6]), inducing more noise than the pseudo-presence problem; (iii) the use ofdomiciliated microhabitats dampens climatic effects [7,8], minimizing pseudo-presences (areas nearby confirmed presence have a higher possibility of beingpseudo-absence than pseudo-presence); and (iv) in addition to their own dispersalcapacities, triatomines are also transported by humans and animals, resulting inrange shifts that outweigh the pseudo-presence/pseudo-absence problems.The pseudo-presence and background problems raised by Gutierrez [2] arepossibly the strongest criticism to [1], regardless of the use of surveys and rangemaps to estimate richness results in under- and overestimation, respectively [9].Is not complying with the use of confirmed occurrences a serious violation intriatomines? In T. infestans using the average first nearest-neighbour distance(between confirmed occurrences and random coordinates from [3]) was29.1 km (26.4–31.9 km 95% CI; n ¼ 640). Checking the geographical autocorre-lation for six bioclimatic predictors on arbitrary ‘transects’ running alonglongitudes and ‘crossing’ through various latitudes, in plain and mountainousregions in Argentina, Colombia and Venezuela, there was a significant spatialautocorrelation of 100–300 and 50–80 km in the plain and mountainousareas, respectively. With the nearest-neighbour distances within the spatialrstb.royalsocietypublishing.orgPhil.Trans.R.Soc.B371:201601882autocorrelation scale of the bioclimatic variables the use ofrandom coordinates from the range maps would notinvalidate the conclusions of Medone et al. [1].The shapes of BAM diagrams [6,10] depend upon thespecies’ eco-physiological and behavioural characteristicsand the abiotic and biotic environments; triatomine BAM dia-grams (TBAM) are different to classical BAM diagrams fortriatomines are characterized by: (i) a high mobility (active þpassive) so factor M in the TBAM includes factors A and B(and is close to their union, A< B ); (ii) an eclectic diet, soB overlaps closely with A; and (iii) a selective microhabitatuse, so components A, B and M are extremely close to thegeographic area (G). Using the shape of the Americas as G,T. infestans shows a disjoint BAM, while R. prolixus shows amore ‘classical’ BAM, but for both species the M ‘envelope’encompasses practically the whole of A and B regions. Thispeculiar TBAM justifies a large modelling background thatincludes almost all of the Americas.Range maps are scale-dependent abstractions of speciesdistributions [9], with resolutions less than 28 (200 km)usually overestimating the occupancy area of individualspecies [11]; as the purpose was to link environmental suit-ability to epidemiological risk, Medone et al. [1] preferredto err towards an overestimation rather than to an underesti-mation. Exceptions to the recommended methodology (suchas using confirmed occurrences in MAXENT) are regularlypublished when a specific objective justifies them [12,13].The large M area of T. infestans and R. prolixus is alsoinfluenced by the B region; triatomine presence is mainlydetermined by the host species (pathogens, parasites, preda-tors and competitors have a much smaller role), and a lack ofnegative interactions across G makes the B area smaller thanor equal to abiotic GA [6]. As the performance of MAXENT issensitive to the size of the background, this results in a con-undrum for triatomines, because the large M area wouldjustify using a large background.An ongoing analysis of three triatomine species [14], whereconfirmed occurrences for Panstrongylus geniculatus, P. megistusand T. guasayana (with 189, 593 and 90 coordinates, respect-ively) were compared with a random selection of range mapsfrom [3] usingMAXENT and GARP software, showed no statisti-cally significant differences in the suitability predictionsbetween the two datasets using three methods of evaluation(average AUC curves from 10 replicates, threshold-dependentkappa and True Skill Statistics).Being the first application of a methodology to convertsuitability predictions into epidemiological risk, Medoneet al. [1] stated that results should not be taken as a policyguide. Owing to the short space available for this reply,details on the statistical tests and full arguments here usedwill appear in a full paper that is underway. Nevertheless, Iconsider that the observations and caveats posed in [2] arevalid in general, and I thank this author for pointingout some incomplete descriptions in [1]; I believe that hisobservations about potentially dangerous applications ofthe MAXENT software, if used inadequately, will be welcomedby readers and practitioners, particularly if they keep in mind‘abusus non tollit usum’ (misuse does not remove use).References1. Medone P, Ceccarelli S, Parham PE, Figuera A,Rabinovich JE. 2015 The impact of climate changeon the geographical distribution of two vectors ofChagas disease: implications for the force ofinfection. Phil. Trans. R. Soc. B 370, 20130560.(doi:10.1098/rstb.2013.0560)2. Gutie´rrez EE. 2016 Ecological niche modelling requiresreal presence data and appropriate study regions: acomment on Medone et al. (2015). Phil. Trans. R. Soc.B 371, 20160027. (doi:10.1098/rstb.2016.0027)3. Carcavallo RU, Galı´ndez Giro´n I, Jurberg J, Lent H.1999 Geographical distribution and alti-latitudinaldispersion of Triatominae. In Atlas of Chagas’disease vectors in the Americas, vol. 3 (eds RUCarcavallo, I Galı´ndez Giro´n, J Jurberg, H Lent),pp. 747–792. Rio de Janeiro, Brasil: FIOCRUZ.4. Neiva A, Lent H. 1936 Notas e commentarios soˆbretriatomideos. Lista de especies e sua distribuc¸a˜ogeographica. Apuntes y comentarios sobre lostriato´midos. Lista de especies y su distribucio´ngeogra´fica. Rev. Entomol. (Rı´o de Janeiro) 6, 153–190.5. Zeledo´n R. 2004 Some historical facts and recentissues related to the presence of Rhodnius prolixus(Stal, 1859) (Hemiptera: Reduviidae) inCentral America. Entomologı´a y Vectores 11,233–246.6. Peterson AT, Sobero´n J, Pearson RG, Anderson RP,Martı´nez-Meyer E, Nakamura M, Arau´jo MB. 2011Ecological niches and geographic distributions.Monographs in population biology (eds SA Levin,HS Horn), pp 1–376. Princeton, NJ: PrincetonUniversity Press.7. Heger TJ, Guerin PM, Eugster W. 2006 Microclimaticfactors influencing refugium suitability for Rhodniusprolixus. Physiol. Entomol. 31, 248–256. (doi:10.1111/j.1365-3032.2006.00514.x)8. Lorenzo MG, Lazzari CR. 1999 Temperature andrelative humidity affect the selection of shelters byTriatoma infestans, Vector of Chagas disease. ActaTrop. 72, 241–249. (doi:10.1016/S0001-706X(98)00094-1)9. Hurlbert AH, White EP. 2005 Disparity betweenrange map-and survey-based analyses of speciesrichness: patterns, processes and implications. Ecol.Lett. 8, 319–327. (doi:10.1111/j.1461-0248.2005.00726.x)10. Sobero´n J, Peterson AT. 2005 Interpretation ofmodels of fundamental ecological niches andspecies’ distributional areas. Biodivers. Inform. 2,1–10. (doi:10.17161/bi.v2i0.4)11. Hurlbert AH, Jetz W. 2007 Species richness,hotspots, and the scale dependence of range mapsin ecology and conservation. Proc. Natl Acad. Sci.USA 104, 13 384–13 389. (doi:10.1073/pnas.0704469104)12. Diniz-Filho JA, Bini LM, Rangel TF, Loyola RD, Hof C,Nogue´s-Bravo D, Arau´jo MB. 2009 Partitioningand mapping uncertainties in ensembles offorecasts of species turnover under climate change.Ecography 32, 897–906. (doi:10.1111/j.1600-0587.2009.06196.x)13. Lawler JJ, Shafer SL, White D, Kareiva P, Maurer EP,Blaustein AR, Bartlein PJ. 2009 Projected climate-induced faunal change in the WesternHemisphere. Ecology 90, 588–597. (doi:10.1890/08-0823.1)14. Rocha Leite G, Vezzani D, Wisnivesky C, Rabinovich J.In preparation. Niche prediction in triatomines:methodological and conceptual aspects.

Jorge Rabinovich

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Ecological niche modelling in triatomines–
            <i>abusus non tollit usum</i>
            : a reply to Gutiérrez (2016)

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Ecological niche modelling in triatomines–abusus non tollit usum: A reply to gutiérrez (2016)

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