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Forest Ecology and Management xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
Abiotic and biotic changes at the basin scale in a tropical dry forest
landscape after Hurricanes Jova and Patricia in Jalisco, Mexico☆
Marco Antonio Tapia-Palaciosa, Omar García-Suárezb, Jesús Sotomayor-Bonillab,
Miguel Atl Silva-Magañaa, Gustavo Pérez-Ortíza, Ana Cecilia Espinosa-Garcíac,
⁎
Miguel Ángel Ortega-Hurtadod, Carlos Díaz-Ávalose, Gerardo Suzánb, Marisa Mazari-Hiriartc,
a
Facultad de Ciencias-Laboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de México (UNAM), Mexico City,
Mexico
b
Laboratorio de Ecología de Enfermedades y Una Salud, Departamento de Etología, Fauna Silvestre y Animales de Laboratorio, Facultad de Medicina Veterinaria y
Zootecnia, UNAM, Mexico City, Mexico
c
Laboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, UNAM, Mexico City, Mexico
d
Instituto de Biología, Unidad Colima, UNAM, Colima, Mexico
e
Instituto de Investigación en Matemáticas Aplicadas y en Sistemas, UNAM, Mexico City, Mexico
A R T I C L E I N F O
A B S T R A C T
Keywords:
Enhanced Vegetation Index
Environmental monitoring
Small mammals
Tropical dry forest
Water quality
Given the current evidence of global change, extreme events are projected to be more frequent and intense. At a
river basin scale, the immediate effects of hurricanes include changes in soil properties, vegetation structure and
composition, and water volume and quality, which lead to changes in species distribution and community
structure. The goal of this study was to identify key abiotic and biotic elements for monitoring hurricane events
at the river basin scale by linking databases of vegetation cover, small mammal diversity and water quality
between 2010 and 2016. Abiotic parameters and biotic communities were monitored in a tropical dry forest
(TDF) landscape on the coast of Jalisco, Mexico after two major events: Hurricanes Jova (2011) and Patricia
(2015). Three zones (1-upper, 2-middle and 3-lower basin) adjacent to the TDF were analyzed along the
Cuitzmala River catchment areas before and after the events. We used the Enhanced Vegetation Index (EVI) as a
proxy for vegetation greenness and the diversity index of a small mammal community of rodents and bats as
indicators of terrestrial habitat quality, and Fecal Coliform (FC), Fecal Enterococci (FE), and Electrical
Conductivity (EC) as indicators of the river-water condition. During the years of hurricanes (2011 and 2015)
there was a decrease in the EVI and small mammal diversity, as well as changes in the concentration of FC, FE
and EC. The main effects associated with the two hurricanes were observed in the lower basin where hurricanes
made landfall and the forest had been converted to other land uses. The EVI, communities of small mammals,
and abiotic and biotic water conditions were responsive to the effects of hurricanes and can thus be useful for a
long-term ecological monitoring program at the basin scale. This program would allow a faster evaluation and
response to future extreme meteorological events. Our results could be implemented through the Urban and
Environmental Plans at the state level (Ordenamiento Territorial del Estado de Jalisco), through the regulation of
sustainable agricultural and livestock techniques, and by educating local populations of the effects of extreme
meteorological events.
1. Introduction
Given the current evidence of global change, it is predicted that
extreme climatic events will likely occur more frequently and/or will be
more intense (Goodess, 2013; IPCC, 2012; Levy and Patz, 2015). The
effects of these extreme events depend on the spatio-temporal scale
under which they are studied as well as their magnitude and frequency
(Gillespie et al., 2006; Mallin and Corbett, 2006; Vandecar et al., 2011).
Hurricanes can increase the availability of habitat for organisms,
promote species distribution shifts, increase the variability in ecosystem
processes and landscape heterogeneity, alter the direction of ecological
succession, change the composition and structure of natural
☆
Part of the Special Issue “Resilience of tropical dry forests to extreme disturbance events: An interdisciplinary perspective from long-term studies”.
Corresponding author at: Laboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de México, Circuito Exterior Ciudad
Universitaria, Coyoacán, 04510 Ciudad de México, Mexico.
E-mail address: mazari@unam.mx (M. Mazari-Hiriart).
⁎
http://dx.doi.org/10.1016/j.foreco.2017.10.015
Received 31 May 2017; Received in revised form 6 October 2017; Accepted 8 October 2017
0378-1127/ © 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Tapia-Palacios, M.A., Forest Ecology and Management (2017), http://dx.doi.org/10.1016/j.foreco.2017.10.015
Forest Ecology and Management xxx (xxxx) xxx–xxx
M.A. Tapia-Palacios et al.
assemblages and as well as an impact in water quality parameters. By
integrating information generated by different disciplines such as
ecology, veterinary medicine, and spatial analysis oriented towards the
conservation of tropical areas, we aim to acquire a better understanding
of the effects of hurricanes at larger scales, which is fundamental for the
development of management strategies designed to reduce socio-ecosystem vulnerability.
communities, and promote ecological reorganization (Lugo, 2008).
These effects can be exacerbated by land-use change, affecting terrestrial and aquatic systems (Erb, 2012; Baker, 2003). The environmental
and socioeconomic characteristics of the ecosystems will define their
response to the effect of meteorological events such as hurricanes.
Species assemblages after these events may result in communities
dominated by secondary forests and invasive species, which may alter
the functioning of socio-ecosystems. On the other hand, the risk of
being affected by an extreme event is 80 times higher in a developing
than in a developed country because of the lack of infrastructure, risk
reduction and emergency preparedness (Levy and Patz, 2015).
The immediate effects of hurricanes are related to changes in soil
properties, vegetation cover, water volume, and water quality, which in
turn affect forest structure and composition (Wang et al., 2010).
However, the magnitude of these changes is generally reported at the
local scale. Therefore, there is a gap of knowledge on the impacts of
hurricanes at broader spatial scales (Steyer et al., 2013), such as watersheds and river basins. Impacts of hurricanes at large scales can affect also human populations, but they are not limited to human-made
infrastructure, as they can alter productive systems such as crops and
livestock production and increase the exposure to infectious and noninfectious diseases (Khan et al., 2015).
Several studies have used remote sensing to estimate the damage
caused by hurricanes and to observe recovery processes in different
ecosystems including tropical dry forest (TDF) (Gillespie et al., 2006;
Rodgers et al., 2009). There is scarce information on the recovery of
TDFs after extreme events such as high-intensity hurricanes (Imbert and
Portecop, 2008), especially along the Mexican Pacific coast where this
vegetation type predominates. The Enhanced Vegetation Index (EVI)
represents a proxy for plant cover and productivity but for a broader
damage evaluation it is necessary to consider other biological groups
(Steyer et al., 2013). For example, it is expected that populations of
nectarivores, frugivores, and seed eaters will be affected by the high
hurricane winds that strip flowers, seeds, and fruits (DeGraaf and
Miller, 1996). The most representative vertebrate taxa belonging to
these trophic groups are rodents and phyllostomid bats. These are the
most abundant and diverse groups among vertebrates in the TDF; they
occupy all vegetation layers and provide diverse ecosystem services
(e.g., pollen and seed dispersal). Therefore, these vertebrates are considered excellent habitat-quality indicators (Istomin, 2009).
At the river basin scale, water microbiological parameters are also
good indicators of habitat quality because water is an integrating element of many ecological processes in ecosystems. The alteration of
water parameters, such as biomass of phytoplankton or concentration
of contaminants as well as the composition of microorganisms and
physico-chemical elements, have also been recognized as good tracking
features of habitat quality. However, since rivers are highly dynamic
systems it is difficult to identify long-term negative changes in water
quality (Burkholder et al., 2004), and therefore multi-year data are
needed for a better assessment of water quality.
Our interdisciplinary research group monitored the Cuitzmala River
basin over a 10-year period. During this period, two strong hurricanes
hit the region: Hurricane Jova in October 2011, classified as category 3
(Saffir-Simpson Hurricane Wind Scale), made landfall on the Jalisco
coast as a category 2 hurricane (Brennan, 2012), and Hurricane Patricia
in October 2015, classified as category 5 (Saffir-Simpson scale), hit the
coast near the Cuitzmala River mouth as a category 4 (Kimberlain et al.,
2016). Both hurricanes passed through the Sierra Madre Occidental region, causing significant damage to native vegetation and croplands.
These two events provided a unique opportunity to evaluate the response of abiotic and biotic elements in the Cuitzmala River basin. The
goal of this study was to identify key abiotic and biotic elements for
monitoring hurricane events at the river basin scale by linking databases of vegetation cover, small mammal diversity and water quality
between 2010 and 2016. We predict at a basin scale EVI will change
significantly, and as a result there will be a modification of the species
2. Materials and methods
2.1. Study site
The Cuitzmala River basin is located on the Pacific Coast in the state
of Jalisco, Mexico. The landscape matrix is comprised of the TDF, one of
the major vegetation types, wetlands in the lowlands, agricultural
fields, and grasslands used for cattle raising (Sánchez-Azofeifa et al.,
2009; CIGA, 2008). The basin belongs to the Hydrologic Region RH-15,
covering an approximate area of 1096 km2 (Meléndez, 1999). The study
area includes: zone 1 from 2400 to 1000 masl, zone 2 from 1000 to 200
masl, and zone 3 from 200 masl to sea level within the basin (Cotler
et al., 2002), including the Southern and Eastern portions of the Chamela-Cuixmala Biosphere Reserve (Ceballos et al., 1999).
The most important feature of the climate is its seasonality, with a
rainy season from June to October and a dry season from November to
June (Bullock, 1986; García-Oliva et al., 2002). There is a high diversity
of flora and fauna, including > 1200, 400 and 2000 species of plants,
vertebrates, and insects, respectively (Ceballos and Miranda, 2000;
Lott, 2002; Lott and Atkinson, 2002). The predominant vegetation at
the selected sampling sites is TDF mixed with agricultural land in the
lowlands and riparian vegetation (Fig. 1). Except for the vegetation
index, all sampling was performed after Hurricane Jova in 2011 and
Hurricane Patricia in 2015, as soon as it was possible to access the study
sites. Due to limited accessibility to study sites, the number of samples
obtained during hurricane event periods was lower than for years when
hurricanes did not occur.
2.2. Vegetation monitoring
To identify changes in the vegetation cover associated with the
passage of Hurricanes Jova and Patricia, a series of values from the
Moderate Resolution Imaging Spectro radiometer (MODIS)-EVI were
assembled and analyzed. The MODIS-EVI product (MOD12Q1) consisted of 16-day composite vegetation images with a resolution of
250 m, which were downloaded from the Oak Ridge National
Laboratory (ORNL), Distributed Active Archive Center (DAAC), of the
National Aeronautics and Space Agency (NASA) Website (https://
modis.ornl.gov/fixedsite/) (ORNL-DAAC, 2008). ArcGIS version 10.2
(ESRI) software was utilized to process and analyze MODIS-EVI data.
Two-thousand meter wide buffers, from the point located in the center
of the riverbed along a six-kilometer section of the river, were used to
extract the main statistics (e.g. the mean and Standard Deviation [SD])
of EVI values inside of each buffer. Then, mean EVI values were obtained during October 16 to October 31 in 2011, 2010, 2012, 2013,
2014, 2015 and 2016.
2.3. Monitoring of small mammal communities
Small mammals were captured in sites of the TDF adjacent to the
river. Rodents were captured at three selected sites per zone separated
by 400 m each one, employing 100 Sherman traps per site, covering the
buffer area described previously. Each trap was baited with peanut
butter, oats, and vanilla essence, during three consecutive nights in
October 2010, 2011, 2014, and 2015 fieldwork seasons. Bats were
captured at two sites in natural corridors adjacent to the river per zone
separated from each other by at least 10 km, in 2011, 2014, and 2015
with five mist nets (5 × 9 m) per site, which were opened at dusk and
2
Forest Ecology and Management xxx (xxxx) xxx–xxx
M.A. Tapia-Palacios et al.
Fig. 1. Main vegetation types and sampling zones in the Cuitzmala River basin (modified from CIGA, 2008).
microbiological parameters were Fecal Coliform and Fecal Enterococci
(Colony Forming Units [CFU]/100 mL). The physicochemical parameters were obtained in situ using a Multi-Parameter Water Quality
Sonde Model YSI 6600-M (YSI, Inc., Yellow Springs, OH). For the
quantification of the FC and FE, performed by triplicate, standard
procedures were followed (WEF-APHA, 2005).
remained open for four consecutive hours immediately after Hurricane
Patricia. During the November 2015 season we sampled one night for
each zone using 100 Sherman traps for rodents, and five mist nets for
bats. The animals captured were handled following conventional protocols and using specific local field guides (Ceballos and Miranda, 2000;
Sikes and Gannon, 2011). Regarding bats, only phyllostomid bats were
included. Due to restricted accessibility to the study sites, sampling of
the small mammals during the 2015 season (after Hurricane Patricia)
was limited (Tables 2 and 3). Successive sampling events were simulated using the bootstrap method only for 2015 data using 1000 repetitions (Adams et al., 1997; Crowley, 1992; Dixon, 1993), to standardize the data to feed the model used by the iNEXT package in R
software (R Developed Core Team, 2008). Bootstrap is a standard statistical technique based on sampling with replacement from the empirical distribution. A detailed description of the technique is beyond
the scope of this work, but more detail can be found in Efron and
Tibshirani (1993). For species abundance, rarefaction/extrapolation
curves base on Hilĺs numbers were simulated using the iNEXT package
for R (R Developed Core Team, 2008; Hsieh et al., 2013), to compare
species diversity and assemblages before and after extreme events
(Chao et al., 2014).
2.5. Data analysis
Because of the lack of homogeneity and the differences between
sampling efforts during the monitoring seasons, a Monte Carlo simulation was performed for EVI, FC, FE and EC to have a better comparison of the data available using 3000 randomizations for each simulation (Adams et al., 1997; Crowley, 1992; Dixon, 1993). The parameters
and distributions used for the simulation were the empirical distributions computed from the data obtained during the sampling seasons
near the dates when Hurricanes Jova and Patricia occurred. We assumed that these data were representative of the prevailing conditions
in the study area and independent. The parameters for each variable are
depicted in Table 1.
We compared EVI, rodents, bats, FC, FE and EC data between the
different years to determine whether there was a difference associated
with sampling seasons, i.e. those close to Hurricanes Jova (2011) and
Patricia (2015) and those from other years which were denominated
“no hurricane” years.
A second comparison was conducted based on the three different
zones for EVI, rodents, bats, FC, FE and EC data to identify whether
there were spatial differences.
Kruskal–Wallis and Post-hoc Dunn tests with Bonferroni correction
were used for the multiple comparisons between groups, with a confidence level of 95% (Dunn, 1964; Murtaugh, 2009) using the data
obtained from Monte Carlo method and iNEXT package in R software.
All statistical tests showed a p-value lower than 0.004, as shown
specifically on each figure.
2.4. Water quality monitoring
A water quality monitoring program of the Cuitzmala River was
initiated in 2006 as part of a Long-Term Ecological Research (LTER)
project in Mexico (Bonilla-Meza, 2007; López-Tapia et al., 2007; LópezTapia, 2008; Maass et al., 2005; Solano-Ortiz, 2011; Tapia-Palacios,
2012, 2016; Vaca-Velasco, 2012). Among the water quality parameters
that we have been monitoring since 2006, only temperature, pH,
Electrical Conductivity (EC), Fecal Coliforms (FC) and Fecal Enterococci (FE) were consistently monitored in all seasons and in the
same study sites.
For this analysis we selected data from four rainy seasons during
October 2010, 2011, 2014 and 2015 in three sites within each one of
the evaluated zones. The measured physico-chemical parameters were
temperature (°C), pH and Electrical Conductivity (µS/cm). Evaluated
3
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M.A. Tapia-Palacios et al.
Table 1
Number of samples (n); mean, Standard Deviation (SD), and distribution type for each abiotic and biotic element during 2010–2016. Fecal Coliform/Fecal Enterococci ratio (FC/FE,
Toranzos et al., 2007).
Zone 1
Zone 2
Zone 3
2010
2011
2012
2013
2014
2015
2016
2010
2011
2012
2013
2014
2015
2016
2010
2011
2012
2013
2014
2015
2016
EVI
n
mean
SD
–
–
–
16
4882
609
16
5506
317
16
5171
458
16
5205
290
16
4976
392
16
5067
386
–
–
–
16
4832
577
16
5159
489
16
5483
649
16
5406
628
16
4874
436
16
5280
461
–
–
–
16
2809
892
16
5101
845
16
4630
900
16
5242
579
16
3829
412
16
5030
761
FC
n
mean
SD
12
813
163
18
367
117
–
–
–
–
–
–
18
1261
812
24
618
277
–
–
–
–
–
–
6
163
51
–
–
–
–
–
–
12
3067
833
6
423
50
–
–
–
24
4100
4014
18
433
196
–
–
–
–
–
–
18
7033
4088
18
260
52
–
–
–
FE
n
mean
SD
12
1500
1066
18
396
92
–
–
–
–
–
–
18
1041
660
24
630
199
–
–
–
–
–
–
6
102
13
–
–
–
–
–
–
12
4500
1145
6
423
139
–
–
–
24
7633
1260
18
73
18
–
–
–
–
–
–
18
3144
313
18
89
65
–
–
–
EC
n
mean
SD
12
86
58
18
140
89
–
–
–
–
–
–
18
100
87
24
162
120
–
–
–
–
–
–
6
262
13
–
–
–
–
–
–
12
150
55
6
259
10
–
–
–
24
485
320
18
813
709
–
–
–
–
–
–
18
166
5
18
693
290
–
–
–
0.54
0.93
1.21
0.98
0.68
1
0.54
5.93
2.24
2.92
FC/FE
Distribution
Normal
Lognormal
Lognormal
Normal
1.6
3. Results
Table 2
List of rodent species and number of individuals captured during 2010, 2011, 2014 and
2015.
3.1. Effects of hurricanes on vegetation
Rodent species
We found a significant change in average EVI values during 2011
and 2015, the years when Hurricanes Jova and Patricia made landfall
(Fig. 2a). The average EVI was similar across all three zones during “no
hurricane” periods, but significantly decreased in zones 2 and 3
throughout the Hurricanes Jova and Patricia monitoring period
(Fig. 2b).
Liomys pictus
Baiomys musculus
Oryzomys couesi
Osgoodomys banderanus
Peromyscus perfulvus
Oryzomys melanotis
Sigmodon mascotensis
Reithrodontomys
fulvescens
Sigmodon alleni
Rattus rattus
Hodomys alleni
Rattus norvergicus
Individuals per year
Total
3.2. Effects of hurricanes on small mammal communities
A total of 850 individuals (529 rodents and 321 phyllostomid bats)
were captured during all sampling seasons, which represented 12 rodents and 15 bat species (Tables 2 and 3). The diversity index obtained
for iNEXT in rodents and bats decreased significantly in 2011 and 2015
when the hurricanes made landfall (Fig. 3a and b). Rodent species diversity was similar across the three sampling zones during the “no
hurricane” period, but right after Hurricane Jova and Patricia events,
the diversity index diminished significantly in all zones (Fig. 3c).
a
2010
2011
2014
44
7
3
10
5
3
6
4
13
6
2
16
15
1
2
157
32
47
37
14
24
32
3
55
7
5
1
4
363
82
2015
14 (122a)
2 (17a)
2 (26a)
8 (55a)
3 (23a)
Individuals per
species
228
47
54
71
34
27
39
12
7
5
1
4
29 (243a)
529
Mean generated data by Bootstrap resampling method.
Fig. 2. a: Comparison of the Enhanced Vegetation Index (EVI) during the monitored years (2011–2016) (Kruskal–Wallis, p < 0.05); b: Comparison of the EVI in each zone during “no
hurricane” vs. hurricane events (Dunn test, p < 0.05).
4
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M.A. Tapia-Palacios et al.
Table 3
List of bat species and number of individuals captured during 2011, 2014 and 2015.
Bat species
Artibeus jamaicensis
Artibeus lituratus
Artibeus phaeotis
Artibeus toltecus
Artibeus watsoni
Centurio senex
Choeroniscus godmani
Chiroderma salvini
Desmodus rotundus
Glossophaga morenoi
Glossophaga soricina
Hylonycteris underwoodi
Leptonycteris yerbabuenae
Sturnira lilium
Sturnira ludovici
Individuals per year
Individuals total
a
2010
2014
15
2
3
1
141
30
5
33 (70a)
1 (2a)
7 (15a)
3
3
1 (3a)
14
3
10
2 (4a)
2
1
1
9
1
1
1
5
42
4
4
2
219
2015
3 (8a)
1 (2a)
7 (12a)
5 (9a)
60 (125)
Individuals per species
189
33
15
1
4
5
1
1
25
4
14
1
5
16
7
321
Mean generated data by Bootstrap resampling method.
Although bat species diversity increased along the Cuitzmala basin
during “no hurricane” periods, the diversity index in zones 2 and 3
decreased significantly (Fig. 3d). Rarefaction curves derived from the
diversity measurements showed rapid recovery in both rodent and bat
communities (Fig. 4a and b).
Fig. 4. Rarefaction (solid) and extrapolation (dash) comparing a: rodent diversity species;
b: bat diversity species before and after hurricane events, based on Hill’s number.
Fig. 3. (a and b) Comparison of diversity index for rodents and bats for the 2011–2015 period (Kruskal–Wallis, p < 0.05); (c and d) Comparison of diversity index in each zone during “no
hurricane” periods vs. hurricane events (Dunn test, p < 0.05).
5
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M.A. Tapia-Palacios et al.
Fig. 5. (a, b and c) comparison of Fecal Coliforms (FC) and Fecal Enterococci (FE) density, and Electrical Conductivity for the 2010–2015 period (Kruskal–Wallis, p < 0.05); (d, e and f)
comparison of FC and FE density, and conductivity in each zone during “no hurricane” periods vs. hurricane events (Dunn test, p < 0.05).
hurricane years 2011 and 2015 (Fig. 5c). EC increased along the river
with decreasing altitude. After the Jova and Patricia events, EC increased in each zone with respect to “no hurricane” periods, but it was
significant only in zones 2 and 3 (Fig. 5f).
3.3. Effects of hurricanes on water quality
Microbiological water quality indicators, represented by the concentration of FE and FC, were significantly lower in 2011 and 2015
(Fig. 5a and b), especially at zones 2 and 3 (Fig. 5d and e). Density of
both FC and FE indicators increased from the upper to the lower basin
zone (from zone 1–3).
Compared to no hurricane years, Electrical Conductivity (EC) was
the only parameter that exhibited significant differences in the
4. Discussion
The monitoring of biotic and abiotic elements in the Cuitzmala
River basin during the 2010–2016 period allowed us to quantify and
6
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M.A. Tapia-Palacios et al.
communities depend on the dynamics of the water–soil–atmosphere
interface at different scales (Maass et al., 2002). Extreme meteorological events such as hurricanes can modify the dynamic of this interface, thus these disturbances can alter key ecological processes
(Wang et al., 2010).
The analysis of waterborne pathogen presence in the Cuitzmala
River allowed us to observe a significant decrease in the density of FC
and FE in 2011 and 2015, immediately after Hurricanes Jova and
Patricia. Burkholder et al. (2004) reported a decrease in contaminants
due to the increase in water volume that entered the Albemarle–Pamlico Estuarine System, during three hurricanes in 1999. However, a
precise density of the bacterial indicators should consider that these
microorganisms are adsorbed into the suspended solids in the water
column, which tend to increase right after hurricanes. The suspended
particles favor bacterial survival, providing nutrients and protection
from UV light. Therefore, there could be an underestimation of bacterial indicator densities if these are only quantified in the free water
phase (Galfi, 2014). The bacteriological analysis does not discern such
differences; thus, this topic needs further exploration.
Even though our results indicate a decrease of microorganisms of
fecal origin during the two Jova and Patricia events, this does not imply
that health risks also diminished; on the contrary, there may be a
greater dispersion of contaminants toward water supply sources, and
therefore an increase of disease outbreaks due to waterborne pathogens
(Bales et al., 2000; Cann et al., 2013).
In this analysis, the water supply sources were not evaluated.
However, in other investigations under hurricane conditions (Bales
et al., 2000; Cann et al., 2013), the water supply sources were compromised in their quality due to the increase of the water volume that is
transported from upper to lower regions. Therefore we consider the
monitoring of water supply sources as a relevant issue in future research.
During Jova and Patricia events the FC and FE densities were similar
in the three zones (Fig. 5d and e) as opposed to the hurricane periods
where zone 3 showed higher bacterial density. This can be related to a
less steep slope in zone 3 and the waste discharges from denser human
settlements that concentrate in this area of the basin.
Following Toranzos et al. (2007) we calculated the fecal coliform to
fecal streptococci ratio (FC/FE) as an indicator of the contamination
origin due to the different concentration of microorganisms from animals and humans (Table 1). In four occasions there seems to be a
tendency of animal sources of contamination in year 2010 and 2011 in
zone 1, as well as in 2014 in zone 2. Contrasting with year 2011 after
hurricane Jova, there seems to be an influence of human origin in zone
3; the area that presents a higher population density and receives the
discharges from the upper part of the river.
Electrical Conductivity (EC) is a basic water parameter, easy to
monitor and interpret. Changes in EC are sensitive to variations in
dissolved solids, and mainly in mineral salts (Bales et al., 2000). In
rivers, EC increases along streams due to the amount and intensity of
precipitation, geology and soil type, topography, and vegetation cover,
and can be modified by land use (Baker, 2003). Our findings demonstrate that EC increased along the Cuitzmala River basin, mainly in the
middle and lower basin (zones 2 and 3). This may have been also related to the loss of vegetation cover, as indicated by changes in EVI,
since during the same years (2011 and 2015) and in the same zones (2
and 3), EC increased while the EVI decreased. Similarly, Hagy III et al.
(2006) found that EC increased when hurricanes occur.
Meteorological events affect the socio-ecosystem at different spatial
and temporal scales; such effects can be evaluated by monitoring a
diversity of elements such as those measured in this study. Hurricane
effects have been frequently assessed based on changes in vegetation
cover (Rodgers et al., 2009; Wang and D’Sa, 2009), animal mortality
(Gunter and Eleuterius, 1971; DeGraaf and Miller, 1996; Klinger,
2007), and water quality (Burkholder et al., 2004; Baker, 2003; Hagy III
et al., 2006), but these approaches do not lead to an understanding of
compare changes in vegetation cover, small mammal communities, and
river water quality before and after the impact of hurricanes Jova and
Patricia. Therefore, we provide key evidence of significant effects of
hurricanes on EVI, small mammal communities and water quality at the
basin scale.
On the Pacific coast, hurricanes are a common ecological perturbation, but with different characteristics (wind velocity, temperature,
humidity, precipitation, and distance to the track of the hurricane’s eye)
(Imbert and Portecop, 2008). Thus, the effects of these disturbances are
strongly influenced by topography, landscape heterogeneity, and land
use (Chazdon, 2003). Simulated EVI products were significantly lower
in years when Hurricanes Jova and Patricia impacted the area. We
found that the lower basin area corresponding to zone 3, the coastal
plain, was the most affected area along the river basin. It was most
likely the first area hit by the hurricanes when they made landfall and
where natural vegetation had been transformed to agricultural land.
Larger human settlements of the socio-ecosystem and the economic
activities they perform take place mainly in the costal area. Hurricane
Jova led to a great deal of damage in this zone that may be attributed to
the high-water volume discharged (Sampablo et al., 2016). The waterlevel of the Cuitzmala River increased at the river’s mouth (OlguínLópez et al., 2015) during and after hurricane Jova. During hurricane
Patricia in 2015, wind direction and velocity caused generalized forest
defoliation (Sampablo et al., 2016). However, plants produced new
shoots and leaves a few days after the hurricane, a pattern that has been
observed after in other regions (Tanner et al., 1991).
Forest productivity variation can exert an influence on the richness,
abundance and composition patterns of small mammals, because these
species depend on forest conditions such as microclimatic variables:
humidity and temperature, which change abruptly post-hurricane
(Oindo et al., 2000, Willig et al., 2007). A drastic decrease was observed
in species richness and abundance in both small mammal groups (rodents and bats) in 2011 and 2015 after Hurricanes Jova and Patricia.
Rodent diversity decreased in all three zones, and similarly that of bats,
mainly in zones 2 and 3. These changes in small mammal diversity are
consistent with the vegetation changes found at each zone and can be
associated which changes in forest condition post-hurricane.
A post-hurricane decrease in species density has been documented
in different taxonomic groups, such as mammals, birds and reptiles
(Brown et al., 2011; Gannon and Willig, 1994; Wunderle et al., 2004).
In the short-term, wildlife may die because of the destruction of refuges,
drowning, an increased predation, or also wildlife may respond to
hurricane damage by shifting their diet and foraging habitats or changing their reproductive patterns (Willig et al., 2007; Klinger, 2007). For
example, in July 2000, a catastrophic flood in Bladen Nature Reserve,
Belize eliminated the entire spiny rat (Heteromys desmarestianus) population, but by September 2001 population density had increased from
34.4 individuals per ha−1 immediately pre-flood to 42.5 individuals per
ha−1 (Klinger, 2007).
In others studies it has been reported that an increase in post-hurricane food supplies may have contributed to an increased reproductive
output in rodents (Rao, 1981). In the long term, wildlife recovery responds to vegetation changes as the forest regenerates (DeGraaf and
Miller, 1996). According the extrapolation of Hill numbers for our study
area, small mammal communities show a potential recovery. The most
abundant species in the study area, including the painted spiny pocket
mouse (Liomys pictus), and Jamaican fruit bat (Artibeus jamaicensis),
both important to forest regeneration because of their function as seed
scatters, presented a relatively quick recovery. In another study the
Jamaican fruit bat population recovered to a pre-hurricane level two
years after hurricane Hugo (Gannon and Willig, 1994). Similar patterns
of recovery have been observed in other vertebrate groups, such as in
amphibians, reptiles and birds (Schoener et al., 2001; Woolbright,
1991; Wunderle et al., 2004; Brown et al., 2011; Waide, 1991).
Water acts as an integrative element of ecological processes in socioecosystems through the nutrient flux. The structure and function of
7
Forest Ecology and Management xxx (xxxx) xxx–xxx
M.A. Tapia-Palacios et al.
Mónica Jacinto, Julio Barrón, Luis Gerardo Salazar, and Miguel Rivas
students of the Environmental Monitoring Area, Laboratorio Nacional
de Ciencias de la Sostenibilidad, UNAM for their valuable help during
field work, and to Dr. Amy M. Lerner for the final English revision of
this manuscript.
the alteration of ecological processes at the basin scale. Our work attempted to integrate several abiotic and biotic elements at the basin
level to understand the ecosystem dynamics that occur after hurricane
events, which are predicted to be more frequent throughout the world
(Goodess, 2013; Levy and Patz, 2015).
Our study suggests that suspended solids in the water, a simple
abiotic parameter, can be useful as a proxy for the condition of the
river-basin cover and how the microbial pathogens can be transported
through the system. In addition, we could identify other key abiotic and
biotic parameters to identify shifts in ecological processes at the watershed scale. For example, key indicators could be rodents such as
Liomys pictus and bats such as Artibeus jamaicensis, which represent the
most important TDF’s seed dispersers.
As in many other Latin American TDF regions, there is a clear need
for an update of the Ecological Land-Use Plan, which in Mexico is a
collaborative land-use planning tool, recognizing that human settlements and agricultural, livestock, recreational, and conservation activities should be performed with sustainable techniques. One of the
limitations for the management of these natural and urban areas is that
the Ecological Land-Use Plan is restricted to small geographic areas. In
addition, the management plan of the Chamela-Cuixmala Biological
Reserve was designed to conserve native vegetation and this restrictive
area does not even reach a basin scale; it comprises only partially the
lower area of the basin. Our results suggest that regional environmental
and urban planning should consider the vulnerability of different basin
or watershed zones to extreme hydro-meteorological events such as
hurricanes.
Future studies should consider integrating the possible effects of
extreme climatic events and corresponding adaptation and mitigation
strategies into urban and environmental planning. This problem has
been widely recognized by experts in the fields of natural hazards,
emergency preparedness, civil defense and other fields. The tragedy of
hurricane Katrina exposed the negative effects of an ill prepared civil
protection system (NRC, 2009).
We recommend protecting the natural vegetation, including TDF
and wetland vegetation, along the lower basin as a preventive measure,
to protect these forests and local communities from extreme hydrometeorological events hitting the coastal area.
Formatting and funding sources
Field and experimental work was supported by the Research Grant
MAB/UNESCO (2007); SEP-CONACYT 2005-50955 (2007–2010);
CONACYT 83441 (2009–2012), UNAM, DGAPA-PAPIIT IN215910
(2009-2011); UNAM, DGAPA-PAPIIT IG200213 (2013–2014); SEPCONACYT 179045. (2013–2017). We are grateful to Posgrado de
Ciencias Biológicas, UNAM for the CONACYT Masters Scholarship assigned to Marco Antonio Tapia-Palacios (2012–2014), and to the SNICONACYT grant as Research Assistant (2017). We are grateful to
Posgrado en Ciencias de la Producción y la Salud Animal, UNAM, for
the CONACYT Masters Scholarship assigned to Omar García-Suárez
(2013–2015) and to Jesús Sotomayor-Bonilla (2013–2017) as a
Doctoral student.
References
Adams, D.C., Gurevitch, J., Rosenberg, M.S., 1997. Resampling tests for meta-analysis of
ecological data. Ecology 78, 1277–1283. http://dx.doi.org/10.1890/00129658(1997) 078{[}1277:RTFMAO]2.0.CO;2.
Baker, A., 2003. Land use and water quality. Hydrol. Process. 17, 2499–2501.
Bales, J., Oblinger, C.J., Sallenger, A.H., 2000. Two months of flooding in eastern North
Carolina, September–October 1999: hydrologic, water quality, and geologic effects of
Hurricanes Dennis, Floyd, and Irene (No. 4093). US Department of the Interior, US
Geological Survey.
Bonilla-Meza, S., 2007. Evaluación del humedal de Cuitzmala para identificar prioridades
de rehabilitación. Posgrado en Ciencias Biológicas. Maestría en Biología Ambiental
(Restauración Ecológica). Facultad de Ciencias, UNAM. México.
Brennan, M.J., 2012. Hurricane Jova (EP102011), 6–12 October 2011. Tropical Cyclone
Report. NOAA/NHC 1–17.
Brown, D.R., Sherry, T.W., Harris, J., 2011. Hurricane Katrina impacts the breeding bird
community in a bottomland hardwood forest of the Pearl River basin. Louisiana. For.
Ecol. Manage. 261, 111–119. http://dx.doi.org/10.1016/j.foreco.2010.09.038.
Bullock, S.H., 1986. Climate of Chamela, Jalisco, and trends in the south coastal region of
Mexico. Arch. Meteorol. Geophys. Bioclimatol. Ser. B 36, 297–316. http://dx.doi.
org/10.1007/BF02263135.
Burkholder, J., Eggleston, D., Glasgow, H., Brownie, C., Reed, R., Janowitz, G., Posey, M.,
Melia, G., Kinder, C., Corbett, R., Toms, D., Alphin, T., Deamer, N., Springer, J., 2004.
Comparative impacts of two major hurricane seasons on the Neuse River and western
Pamlico Sound ecosystems. Proc. Natl. Acad. Sci. U. S. A. 101, 9291–9296. http://dx.
doi.org/10.1073/pnas.0306842101.
Cann, K.F., Thomas, D.R., Salmon, R.L., Wyn-Jones, A.P., Kay, D., 2013. Extreme waterrelated weather events and waterborne disease. Epidemiol. Infect. 141, 671–686.
http://dx.doi.org/10.1017/S0950268812001653.
Ceballos, G., and Miranda, A., 2000. Guía de Campo de los mamíferos de la costa de
Jalisco, México. D.F., México. Fundación Ecológica de Cuixmala, AC. Instituto de
Ecología/Instituto de Biología, Universidad Nacional Autónoma de México. México.
Ceballos, G., Szekely, A., García, A., Rodríguez, P., Noguera, F., 1999. Programa de
Manejo de la Reserva de la Biósfera Chamela-Cuixmala. Instituto Nacional de
Ecología, SEMARNAP, México, D.F.
Centro de Investigación en Geografía Ambiental (CIGA), 2008. Memoria Técnica.
Cartografía de la Cubierta Vegetal y uso del suelo en la cuenca del Río Cuitzmala,
Jalisco. Velázquez, A. (Coord.). Morelia, Michoacán, México. Anexo Mapa de
Coberturas de vegetación y uso del suelo cuenca Río Cuitzmala, Jalisco. Scale 1:75
000.
Chao, A., Gotelli, N.J., Hsieh, T.C., Sander, E.L., Ma, K.H., Colwell, R.K., Ellison, A.M.,
2014. Rarefaction and extrapolation with Hill numbers: a framework for sampling
and estimation in species diversity studies. Ecol. Monogr. 84, 45–67. http://dx.doi.
org/10.1890/13-0133.1.
Chazdon, R.L., 2003. Tropical forest recovery: legacies of human impact and natural
disturbances. Perspect. Plant Ecol. Evol. Syst. 6, 51–71. http://dx.doi.org/10.1078/
1433-8319-00042.
Cotler, H., Durán, E and Siebe, C., 2002. Características morfo-edafológicas y calidad de
sitio de un bosque tropical caducifolio. In: Noguera, F.A., Vega-Rivera, J.H., GarcíaAlderete, A.N., Quesada-Avendaño, M. (Ed.). Historia Natural de Chamela. Instituto
de Biología. UNAM. Jiménez Ed. México, D.F., México.
Crowley, P.H., 1992. Resampling methods for computation-intensive data-analysis in
ecology and evolution. Annu. Rev. Ecol. Syst. 23, 405–447. http://dx.doi.org/10.
1146/annurev.es.23.110192.002201.
DeGraaf, R.M., Miller, R.I., 1996. The importance of disturbance and land-use history in
New England: implications for forested landscapes and wildlife conservation. In:
DeGraaf, R.M., and Miller, R.I. (Ed.). Conservation of Faunal Diversity in Forested
Landscapes. Netherlands: Springer. doi: 10.1007/978-94-009-1521-3_1.
5. Conclusions
The EVI, small mammal communities, and abiotic and biotic river
elements are adequate parameters for a long-term ecological monitoring program at the basin or watershed scale. By using these indicators, which are relatively easy to measure, it is possible to capture
the response of the socio-ecosystem to hurricanes. Understanding the
response of the system to extreme events is useful to assess the time that
the socio-ecosystem takes to recover and to gain insights into its resilience and buffering capability. Further monitoring would enable a
better understanding of the most appropriate environmental management strategies after extreme meteorological events.
Zone 3 (the lower basin zone), compared to the zone 2 and 1 (the
higher basin zone), showed a decrease in EVI and small mammal diversity, as well as an increase in EC. The high EC detected in the lower
basin zone suggests that there is an important substrate that could be
supporting potential pathogenic microorganisms. Future studies should
analyze the potential health risks associated with this coastal zone. Our
results highlight the importance of preserving the areas of the TDF and
wetlands, and to perform sustainable management practices which include the protection of forested areas.
Acknowledgments
We are grateful to Estación de Biología Chamela, Instituto de
Biología, UNAM and to Abel Verduzco and Salvador Araiza for logistic
support. To Nallely Vázquez, Erick Hjort, Eduardo Rodríguez-Atriano,
8
Forest Ecology and Management xxx (xxxx) xxx–xxx
M.A. Tapia-Palacios et al.
ecological data. Ecol. Lett. 12, 1061–1068. http://dx.doi.org/10.1111/j.1461-0248.
2009.01361.x.
National Research Council (NRC), 2009. The New Orleans Hurricane Protection System:
Assessing pre-Katrina Vulnerability and Improving Mitigation and Preparedness.
National Academies Press.
Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center for
Biogeochemical Dynamics (DAAC), 2008. MODIS Collection 5 Land Products Global
Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, TN, USA. Accessed
August 25, 2015. Subset obtained for MOD13Q1 product at 39.497N, 107.3028W,
time period: 2000-02-18 to 2015-07-28, and subset size: 0.25 x 0.25 km. http://dx.
doi.org/10.3334/ORNLDAAC/1241.
Oindo, B.O., De By, R.A., Skidmore, A.K., 2000. Interannual variability of NDVI and bird
species diversity in Kenya. Jag 2, 172–180. http://dx.doi.org/10.1016/S03032434(00)85011-4.
Olguín-López, J.L., Guevara-Gutiérrez, R.D., Romero, J.M.R., Rodríguez, M.R.A., 2015.
Los efectos de “Jova” en el municipio de Autlán de Navarro, Jalisco, México: un caso
histórico/The effects of“ Jova” in the municipality of Navarro Autlán, Jalisco,
México: a case history. RIDE Revista Iberoamericana para la Investigación y el
Desarrollo Educativo 2, 1–19.
R Development Core Team, 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-070. Available in URL at: < http://www.R-project.org > .
Rao, A.M.K.M., 1981. Impact of Cyclone on the Rodent Population in Andhra Pradesh. J.
Bombay Nat. Hist. Soc. 77, 502–503.
Rodgers, J.C., Murrah, A.W., Cooke, W.H., 2009. The impact of hurricane Katrina on the
coastal vegetation of the Weeks Bay Reserve, Alabama from NDVI data. Estuaries
Coasts 32, 496–507. http://dx.doi.org/10.1007/s12237-009-9138-z.
Sánchez-Azofeifa, G.A., Quesada, M., Cuevas-Reyes, P., Castillo, A., Sánchez-Montoya, G.,
2009. Land cover and conservation in the area of influence of the Chamela-Cuixmala
Biosphere Reserve. Mexico. For. Ecol. Manage. 258, 907–912. http://dx.doi.org/10.
1016/j.foreco.2008.10.030.
Schoener, T.W., Spiller, D.A., Losos, J.B., 2001. Natural restoration of the species-area
relation for a lizard after a hurricane. Science 294, 1525–1528. http://dx.doi.org/10.
1126/science.1064396.
Sampablo, L.M.P., Robles, C.L.M., Molina, L.M.F., Tereshchenko, I., 2016. Pronóstico y
precipitación de los ciclones Jova (2011), Manuel (2013) y Patricia (2015) que
afectaron al estado de Jalisco. Teoría y Praxis, Número especial, pp. 10–30.
Sikes, R.S., Gannon, W.L., the Animal Care and Use Committee 1 of the American Society
of Mammologists, 2011. J. Mammal. 92, 235–253.
Solano-Ortiz, R., 2011. Avances metodológicos para la detección del virus de la hepatitis
A y bacteriófagos en agua. Facultad de Ciencias, UNAM. México, Tesis Licenciatura,
Biología.
Steyer, G.D., Couvillion, B.R., Barras, J.A., 2013. Monitoring vegetation response to
episodic disturbance events by using multitemporal vegetation indices. J. Coast. Res.
63, 118–130. http://dx.doi.org/10.2112/SI63-007.1.
Tanner, E.V.J., Kapos, V., Healey, J.R., 1991. Hurricane effects on forest ecosystems in the
Caribbean. Biotropica 513–521.
Tapia-Palacios, M.A., 2012. Detección de Cryptosporidium parvum y Giardia lamblia en
agua del río Cuitzmala. Tesis Licenciatura, Biología. Facultad de Ciencias, UNAM.
México, Jalisco.
Tapia-Palacios, M.A., 2016. Detección de Cryptosporidium parvum y Giardia lamblia en
dos ecosistemas acuáticos contrastantes. Posgrado en Ciencias Biológicas. Maestría en
Biología (Manejo Integral de Ecosistemas). Instituto de Ecología, UNAM, México.
Toranzos, G.A., McFeters, G.A., Borrego, J.J., Saville, M., 2007. Detection of
Microorganismos in Environmental Freshwater and Drinking Waters. In: Manual of
Environmental Microbiology. Hurst, C.J., Crawford, R.L., Garland, J.L., Lipson, D.A.,
Mills, A.L. and Stetzenbach, L.D. (Eds.). American Society for Microbiology.
Washington D.C. 3rd edition. 249–264.
Vaca-Velasco, A.D., 2012. Saneamiento de la cuenca del río Cuitzmala, Jalisco, basado en
el nivel de perturbación causado por actividades antropogénicas. Posgrado en
Ciencias Biológicas. Maestría en Restauración Ecológica, UNAM, México.
Vandecar, K.L., Lawrence, D., Richards, D., Schneider, L., Rogan, J., Schmook, B., Wilbur,
H., 2011. High mortality for rare species following hurricane disturbance in the
Southern Yucatan. Biotropica 43, 676–684. http://dx.doi.org/10.1111/j.1744-7429.
2011.00756.x.
Waide, R.B., 1991. The Effect of Hurricane Hugo on Bird Populations in the Luquillo
Experimental Forest, Puerto-Rico. Biotropica 23, 475–480. http://dx.doi.org/10.
2307/2388269.
Wang, F., D’Sa, E.J., 2009. Potential of MODIS EVI in identifying hurricane disturbance to
coastal vegetation in the northern Gulf of Mexico. Remote Sens. 2 (1), 1–18.
Wang, W., Qu, J.J., Hao, X., Liu, Y., Stanturf, J.A., 2010. Post-hurricane forest damage
assessment using satellite remote sensing. Agric. For. Meteorol. 150, 122–132. http://
dx.doi.org/10.1016/j.agrformet.2009.09.009.
Water Environmental Federation-American Public Health Association (WEF-APHA),
2005. Standard methods for the examination of water and wastewater. American
Public Health Association, Washington, D.C.
Willig, M.R., Bloch, C.P., Brokaw, N., Higgins, C., Thompson, J., Zimmermann, C.R.,
2007. Cross-scale responses of biodiversity to hurricane and anthropogenic disturbance in a tropical forest. Ecosystems 10, 824–838. http://dx.doi.org/10.1007/
s10021-007-9054-7.
Woolbright, L.L., 1991. The impact of Hurricane Hugo on forest frogs in Puerto Rico.
Biotropica 23, 462–467. http://dx.doi.org/10.2307/2388267.
Wunderle, J.M., Mercado, J.E., Parresol, B., Terranova, E., 2004. Spatial ecology of Puerto
Rican boas (Epicrates inornatus) in a hurricane impacted forest. Biotropica 36,
555–571. http://dx.doi.org/10.1111/j.1744-7429.2004.tb00350.x.
Dixon, P.M., 1993. The bootstrap and jackknife: describing the precision of ecological
indices. In: Scheiner, S.M. and Gurevitch, J. (Ed.) Design and analysis of ecological
experiments. Chapman and Hall Ed., New York, New York, USA.
Dunn, O., 1964. Multiple Comparisons Using Rank Sums. Technometrics 6, 241–252.
http://dx.doi.org/10.2307/1266041.
Efron, B., Tibshirani, R., 1993. An Introduction to the Bootstrap. Chapman and Hall, Boca
Raton, FL.
Erb, K.H., 2012. How a socio-ecological metabolism approach can help to advance our
outstanding of changes in land-use intensity. Ecol. Econ. 76, 8–14.
Galfi, H., 2014. Suspended solids and indicator bacteria in storm water runoff: sources of
bias in field measurements. Lulea University of Technology, Sweden Licentiate thesis.
García-Oliva, F., Camou, A., y Maass, J.M., 2002. El clima de la región central de la costa
del Pacífico mexicano. In: Noguera, F.A., Vega-Rivera, J.H., García-Alderete, A.N.,
Quesada-Avendaño, M. (Ed.). Historia Natural de Chamela. Instituto de Biología.
UNAM. Jiménez Ed. México, D.F., México.
Gannon, M.R., Willig, M.R., 1994. The effects of Hurricane Hugo on bats of the Luquillo
Experimental forest of Puerto Rico. Biotropica 26, 320. http://dx.doi.org/10.2307/
2388854.
Gillespie, T.W., Zutta, B.R., Early, M.K., Saatchi, S., 2006. Predicting and quantifying the
structure of tropical dry forests in South Florida and the Neotropics using spaceborne
imagery. Glob. Ecol. Biogeogr. 15, 225–236. http://dx.doi.org/10.1111/j.1466822X.2005.00203.x.
Goodess, C.M., 2013. How is the frequency, location and severity of extreme events likely
to change up to 2060? Environ. Sci. Policy 27, S4–S14.
Gunter, G., Eleuterius, L.N., 1971. Some effects of hurricanes on the terrestrial biota, with
special reference to Camille. Gulf Caribbean Res. 3, 283–289.
Hagy III, J.D., Lehrter, J.C., Murrell, M.C., 2006. Effects of Hurricane Ivan on water
quality in Pensacola Bay, Florida. Estuaries Coasts 29, 919–925.
Hsieh, T.C., Ma, K.H., Chao, A., 2013. iNEXT online: Interpolation and extrapolation
(Version 1.0). Downloaded from < http://chao.stat.nthu.edu.tw/inext/ > .
Imbert, D., Portecop, J., 2008. Hurricane disturbance and forest resilience: assessing
structural vs. functional changes in a Caribbean dry forest. For. Ecol. Manage. 255,
3494–3501. http://dx.doi.org/10.1016/j.foreco.2008.02.030.
Intergovernmental Panel on Climate Change (IPCC), 2012. Managing the Risks of Extreme
Events and Disasters to Advance Climate Change Adaptation. Cambridge University
Press, Cambridge.
Istomin, A.V., 2009. Influence of windfalls on dynamics of communities of small mammals in forests of southern Taiga. Vestn. Mosk. Gos. Univ. Lesa – Lesnoi Vestn. 1,
196–201.
Khan, S.J., Deere, D., Leusch, F.D., Humpage, A., Jenkins, M., Cunliffe, D., 2015. Extreme
weather events: should drinking water quality management systems adapt to changing risk profiles? Water Res. 85, 124–136.
Kimberlain, T.B., Blake, E.S., Cangialosi, J.P., 2016. Hurricane Patricia (EP202015),
20–24 October. Tropical Cyclone Report. NOAA/NHC, 1-32. National Hurricane
Center.
Klinger, R., 2007. Catastrophes, disturbances and density-dependence: population dynamics of the spiny pocket mouse (Heteromys desmarestianus) in a Neotropical lowland forest. J. Trop. Ecol. 23, 507–518. http://dx.doi.org/10.1017/
S0266467407004415.
Levy, B., Patz, J., 2015. Climate Change and Public Health. Oxford University Press, U.K.
López-Tapia, D.M., 2008. Elaboración de criterios para la restauración de la Cuenca del
Río Cuixmala, Jalisco. Posgrado en Ciencias Biológicas. Maestría en Biología
Ambiental (Restauración Ecológica). Facultad de Ciencias, UNAM. México.
López-Tapia, D.M., Pérez-Ortiz, G., Mazari-Hiriart, M., Maass-Moreno, M., 2007. Informe
Final del Proyecto de Investigación MAB/UNESCO Reserva de la Biósfera ChamelaCuixmala: un estudio de calidad del agua bajo un enfoque de manejo integrado de
cuencas. United Nations Educational, Scientific and Cultural Organization.
Lott, E., 2002. Lista anotada de las plantas vasculares de Chamela-Cuixmala. In: Noguera,
F.A., Vega-Rivera, J.H., García-Alderete, A.N., and Quesada-Avendaño, M. (Ed.).
Historia Natural de Chamela. Instituto de Biología. UNAM. Jiménez Ed. México, D.F.,
México.
Lott, E., and Atkinson, T., 2002. Biodiversidad y fitogeografía de Chamela-Cuixmala,
Jalisco. In: Noguera, F.A., Vega-Rivera, J.H., García-Alderete, A.N., and QuesadaAvendaño, M. (Ed.). Historia Natural de Chamela. Instituto de Biología. UNAM.
Jiménez Ed. México, D.F., México.
Lugo, A.E., 2008. Visible and invisible effects of hurricanes on forest ecosystems: an international review. Austral Ecol. 33, 368–398. http://dx.doi.org/10.1111/j.14429993.2008.01894.x.
Maass, J.M., Balvanera, P., Castillo, A., Daily, G.C., Mooney, H.A., Ehrlich, P., Quesada,
M., Miranda, A., Jaramillo, V.J., García-Oliva, F., Martinez-Yrizar, A., Cotler, H.,
López-Blanco, J., Pérez-Jiménez, A., Búrquez, A., Tinoco, C., Ceballos, G., Barraza, L.,
Ayala, R., Astrakhan, J., 2005. Ecosystem services of tropical dry forests: insights
from long-term ecological and social research on the Pacific Coast of Mexico. Ecol
Soc 10.
Maass, J.M., Jaramillo, A., Martínez-Yrízar, A., García-Oliva, F., Pérez-Jiménez, L.A.,
Sarukhán, J. 2002. Aspectos funcionales del ecosistema de selva baja caducifolia en
Chamela, Jalisco. In: Noguera, F.A., Vega-Rivera, J.H., García-Alderete, A.N.,
Quesada-Avendaño, M. (Ed.). Historia Natural de Chamela. Instituto de Biología.
UNAM. Jiménez Ed. México, D.F., México.
Mallin, M.A., Corbett, C.A., 2006. How hurricane attributes determine the extent of environmental effects: multiple hurricanes and different coastal systems. Estuaries
Coasts 29, 1046–1061.
Meléndez, J.F., 1999. Hidrogeografía de la Cuenca del Río Cuitzmala, Jalisco. Posgrado
en Filosofía y Letras. Maestría en Geografía, UNAM. Facultad de Filosofía y Letras,
UNAM, México.
Murtaugh, P.A., 2009. Performance of several variable-selection methods applied to real
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