Redalyc.HABITAT COMPLEXITY and SMALL MAMMAL DIVERSITY

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Mena, José L.; Medellín, Rodrigo A. HABITAT COMPLEXITY AND SMALL MAMMAL DIVERSITY ALONG AN ELEVATIONAL GRADIENT IN SOUTHERN MEXICO Mastozoología Neotropical, vol. 24, núm. 1, julio, 2017, pp. 121-134 Sociedad Argentina para el Estudio de los Mamíferos Tucumán, Argentina

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Artículo

HABITAT COMPLEXITY AND SMALL MAMMAL DIVERSITY ALONG AN ELEVATIONAL GRADIENT IN SOUTHERN MEXICO

José L. Mena1 and Rodrigo A. Medellín2

1 Museo de Historia Natural Vera Alleman Haeghebaert, Universidad Ricardo Palma, Lima, Perú. [Correspondencia: José L. Mena ] 2 Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, D. F., México.

ABSTRACT. We tested the hypothesis that habitat complexity explains alpha diversity of nonvolant small mammals along an elevational gradient in southern Mexico. During October-November 2003, we conducted fieldwork on the Pacific slope of El Triunfo Biosphere Reserve. Small mammal trapping was conducted using standardized techniques (trap lines and pitfalls) along an elevational gradient between 500 and 2100 m elevation. Habitat assessment as indicated by vegetation complexity and diversity was conducted at each site (N = 12). Nine species and 148 individuals were captured in 8400 trap-nights. Results indicate that non volant small mammal diversity increases with habitat complexity. In addition, our study shows that the spatial pattern of diversity cannot be attributed to spatial autocorrelation.

RESUMEN. Complejidad de hábitat y diversidad de mamíferos pequeños en un gradiente altitudinal en el sur de México. Probamos la hipótesis de que la complejidad del hábitat explica la diversidad (diversidad alfa) de mamíferos pequeños no voladores a lo largo de un gradiente de elevación en el sur de México. El trabajo de campo se realizó entre octubre y noviembre de 2003, en la vertiente del Pacífico de la Reserva de la Biosfera El Triunfo. La captura de mamíferos pequeños se llevó a cabo utilizando técnicas estandarizadas (líneas de trampeo y trampas de caída) a lo largo del gradiente de 500 a 2100 m de elevación. El hábitat fue evaluado con base en la complejidad y diversidad de la vegetación en cada sitio evaluado (N = 12). Nueve especies y 148 individuos fueron capturados en 8400 noches-trampa. Los resultados indican que la diversidad de mamíferos pequeños aumenta con la complejidad del hábitat. Además, nuestro estudio muestra que el patrón espacial de la diversidad encontrado no está influenciado por la autocorrelación espacial.

Key words: Alpha diversity. Elevational gradient. Habitat complexity. Mexico. Small mammals.

Palabras clave: Complejidad de hábitat. Diversidad alfa. Gradiente de altitud. Mamíferos pequeños. México.

Recibido 30 enero 2017. Aceptado 27 marzo 2017. Editor asociado: J Morrone 122 Mastozoología Neotropical, 24(1):121-134, Mendoza, 2017 JL Mena and RA Medellín http://www.sarem.org.ar - http://www.sbmz.com.br

INTRODUCTION taposition), source-sink dynamics and habitat heterogeneity (Brown, 2001; Lomolino, 2001; The study of local elevational gradients has Grytnes and McCain, 2007). However, eleva- great potential for increasing our knowledge tional gradients in species richness result from about both regional and global scale diver- a combination of ecological and evolutionary sity processes and the implications of climate processes, and thus, may not reflect one over- change. Indeed, elevational gradients have been riding force (Lomolino, 2001; Wu et al., 2013). reassessed in recent years (Brown, 2001; Lo- Certainly, habitat heterogeneity has a large molino, 2001; Mena and Vázquez-Domínguez, effect on species richness, but the relevant type 2005; McCain, 2007b; Guo et al., 2013) due to of heterogeneity will depend on the species changing perspectives on their interpretation. group and the scale of study (Brown, 2001; Elevational gradients can be used as natural Grytnes and McCain, 2007; Rowe et al., 2015). experiments, allowing for rigorous testing of Specifically, habitat structure appears to be hypotheses elicited by specific questions, such the most important factor describing diversity as effects of small spatial scale or elevational of terrestrial small mammals (August, 1983; trends in abiotic factors (Grytnes and Mc- Medellín and Equihua, 1998; Lambert et al., Cain, 2007). 2006; Mena and Medellín, 2010); however, There are two ways to quantify elevational this hypothesis has not been tested rigorously patterns in species richness: alpha and gamma along elevational gradients. Specifically, the diversity studies (McCain, 2005). Alpha diver- habitat complexity or habitat structure hy- sity studies use local field sampling of plots pothesis predicts that alpha diversity should along a transect, usually on one mountain slope, vary with local habitat complexity, and peak preferably with equal sampling effort at each at elevations characterized by higher habitat elevational band; and gamma diversity studies complexity (MacArthur, 1964; MacArthur, use regional data from previously collected 1972; Rosenzweig, 1992). Here, we examine specimens and field records from an entire this hypothesis in order to understand if this mountain or mountainous region (McCain, explains species richness along an elevational 2005; McCain, 2007a). Clearly, the observed gradient in southern Mexico. In addition, we elevational trend in species varies among groups explore whether the spatial autocorrelation of organisms. The most commonly observed (Koenig and Knops, 1998; Overmars et al., patterns are decreasing richness with increas- 2003) can influence the resulting elevational ing elevation (amphibians, bats and reptiles) gradient in species richness. Testing spatial (Sánchez-Cordero, 2001; Patterson et al., 1996; autocorrelation is helpful to address an essen- Sergio and Pedrini, 2007; Chettri et al., 2010), tial question for this type of studies: method- a low plateau (high diversity across most of the ologically, what elevational interval is useful to lower portion of the gradient then decrease assess elevational patterns in species richness [birds, reptiles]) (McCain, 2009; McCain, 2010), (e.g., 250 or 500 m intervals). This approach or a humped pattern with a richness peak at can be very helpful for selection of sites along intermediate elevations (mainly in nonvolant elevational gradients where researchers will small mammals and plants) (Kessler, 2001; conduct small mammal inventories. Usually, McCain, 2005; Cardelús et al., 2006). sampling sites along elevational gradients are The explanations commonly offered for selected as vegetation changes; however, there elevational patterns in species richness can are other ways to determine their location. In be grouped into four categories: climatic hy- this context, spatial correlation analysis is a potheses based on current abiotic conditions, useful tool to investigate mechanisms operating spatial hypotheses of area and spatial constraint, on species richness at different spatial scales historical hypothesis invoking processes occur- (Diniz-Filho et al., 2003). Indeed, this method ring across evolutionary timescales, and biotic is often used to assess the relationship between hypotheses such as community overlap (jux- variables along gradients. SMALL MAMMALS AND ELEVATIONAL GRADIENT IN MEXICO 123

MATERIAL AND METHODS 20 °C and annual precipitation between 2500-4500 mm (Williams-Linera, 1991; INE, 1998; Morón-Ríos Study area and Morón, 2001). Annual mean temperature at elevations between 1000 and 2000 m is 18-22 °C, The study was carried out in the El Triunfo Bio- with annual precipitation between 2000-3000 mm; sphere Reserve and its buffer zone (Fig. 1), on below 1000 m, annual mean temperature is 26 °C, the Pacific slope of the Sierra Madre de Chiapas, and annual precipitation between 2500-4000 mm southeastern Mexico (15° 09’10’’-15° 57’02’’N, 92° (INE, 1998). 34’04’’-93° 12’42’’W). This reserve is one of the Long and Heath (1991) and Williams-Linera few relatively undisturbed Mexican cloud forests. (1991) provide detailed information on vegetation The Pacific slope descends from the highest peak, and climate at El Triunfo. This reserve protects Cerro El Triunfo (2450 m a.s.l.), with steep slopes 10 of the 19 vegetation types found in Chiapas, influenced by erosion and landslides. Slopes level including large areas of the remaining stands of off somewhat at mid-elevations; below 800 m, land Central American cloud forest (Breedlove, 1981). is more influenced by agriculture (mainly coffee The vegetation types that cover the Pacific slope of plantations), ranching, and human settlement. The El Triunfo are tropical evergreen forest (< 800 m), wet season extends from May to October and the oak forest (800-1200 m), montane rainforest (1200- dry season from November to April. Annual mean 1600 m), pine forest (1600-1800 m), and upper cloud temperature above 2000 m elevation is between 16- forest (> 1800 m). Tropical evergreen forest (TEF), 527-632 m elev. This forest is disturbed and fragmented (buffer zone of the reserve) and larger fragments of undisturbed forest are absent below 500 m a.s.l. In general, lower areas of the gradient are mainly cultivated with coffee plantations. Important canopy tree species in this forest are Ceiba sp. (Bombaca- ceae), Platymiscium sp. and Calliandra sp. (Fa- baceae), Trophis sp. and Pseudolmedia sp. (Mora- ceae), and Eugenia sp. (Myrtaceae); understory species are Piper sp. (Piperaceae) and species of Fabaceae, Lythraceae and Urticaceae. Oak forest (OF), 945 m elev.

Fig. 1. The study area and sampling sites on the Pa- cific slope of the El Triunfo Biosphere Reserve. Trap lines (solid circles) and complementary trapping (white circles) are shown on the map. 124 Mastozoología Neotropical, 24(1):121-134, Mendoza, 2017 JL Mena and RA Medellín http://www.sarem.org.ar - http://www.sbmz.com.br

This forest has a canopy dominated byQuercus Laboratorio de Ecología y Conservación de Verte- salicifolia (Fagaceae), and an understory repre- brados (Instituto de Ecología, Universidad Nacional sented by Ternstroemia sp. (Ericaceae). Montane Autónoma de México). rainforest (MR), 1256-1318 m elev. This forest has a canopy dominated by Amphitecna sp. (Big- Habitat structure assessment noniaceae), Quercus spp. (Fagaceae), Ocotea sp., We established ten circular habitat stations (4 m Nectandra sp. Persea sp. (Lauraceae), and Eugenia sp. radius) in each trap line. Habitat stations were placed (Myrtaceae); and understory species of Araliaceae, at odd trapping stations. All individual trees with Fagaceae, Melastomataceae and Piperaceae. Pine > 5 cm diameter at breast height (d.b.h.) in each forest (PF), 1794 m elev. This forest is dominated habitat station were identified and counted, and were by Cupressus sp. and Pinus sp., with understory assigned into four categories: 5-10 d.b.h. (T0510), species of Ericaceae and Rubiaceae. Upper cloud 10-30 d.b.h. (T1030), 30-50 d.b.h. (T3050), > 50 d.b.h. forest (UCF), 1988-2020 m elev. (mainly primary (T>50). Other variables measured were canopy cover forest) is dominated by Quercus oocarpa (Faga- (PCC) with a spherical densiometer, taken five times ceae), Matudaea trinervia (Hamamelidaceae), (one in each direction of the four cardinal points Hedyosmum mexicanum (Chloranthaceae) and and at the center of the circular station), and percent Dendropanax populifolius (Araliaceae), with under- of herbaceous vegetation cover (HC) within 1-m2 story species of Araliaceae, Melastomataceae, Pipera- quadrant (100, 50, and 0 categories). ceae and Saurauriaceae (Long and Heath, 1991). Species richness analyses Small mammal trapping We define alpha diversity as the number of species During October-November 2003, we used removal detected per spatially standardized survey effort trapping of nonvolant small mammals (Soric- (Lomolino, 2001) in one season. An alpha diversity idae, Didelphidae, Heteromyidae and Cricetidae) data set included species recorded for each sampling in 12 sampling sites using trap lines for 8-9 nights site (N = 12 trap lines, see Table 1). In order to as- (Table 1). Each trap line had 20 stations with 5-m sess the completeness of our trapping, we used a spacing. At each station, two traps were set on the rarefaction-extrapolation approach to extrapolate ground (1 Sherman 8 x 9 x 23 cm and 1 Victor the observed accumulation curve (Chao et al., 2014; mouse trap 16 x 9 cm) and two on fallen logs, Colwell et al., 2012; Chao and Jost, 2012). In this trees, vines or lianas (ca. 2 m height). Traps were way, we used bootstrap methods to construct con- baited with a mixture of peanut butter, rolled oats fidence intervals for alpha diversity of any rarefied and vanilla extract, a standard procedure for small or extrapolated sample (trap line) and developed an mammals (Voss and Emmons, 1996). individual-based (abundance) model. For each tra- As complementary methods for the small mam- pline, we estimated sample completeness (%) which mal’s inventory, we conducted two trappings at 2000, is measured by sample coverage: the proportion of 1300 and 550 m. A protocol for Ichthyomyinae in the total number of individuals that belong to the small streams was conducted with 6 Victor traps for species detected in the sample (Chao and Jost, 2012). 8 days (and baited with crab meat). In addition, we All estimates were obtained by the software iNEXT installed 100-m long lines of pitfall traps to record (Hsieh et al., 2015). small insectivores. Each pitfall line consisted of 10 buckets spaced 10 m apart, with one bucket at Autocorrelation assessment either end, for a total length of 100 m. Drift fences, consisting of a continuous barrier running the total In order to assess the spatial autocorrelation in the length of each line, were made of 40 cm wide strips elevational gradient of the El Triunfo, we generated of hardware clear polyethylene clipped to vertical spatial correlograms for alpha diversity and elevation stakes hammered into the ground. Pitfall traps using Moran’s I coefficients at 8 elevational classes were operated for 8 days. However, the individuals (SAAP 4.3; Wartenberg, 1989). Upper limits for these captured with these complementary methods were elevation classes were 56, 311, 476, 686, 758, 849, not included in the analysis of our hypothesis. 1356, and 1490 m. Moran’s I usually varies between Specific identification of the captured small mam- -1.0 and 1.0 for maximum negative and positive au- mals was aided by the use of a field guide (Reid, tocorrelation, respectively (Diniz-Filho et al., 2003). 1997). A limited number of voucher specimens of Non-zero values of Moran’s I indicate that richness representative mammals were collected. Specimens (alpha diversity) values in sites connected at a given are housed at the Colección de referencia del elevation are more similar (positive autocorrelation) SMALL MAMMALS AND ELEVATIONAL GRADIENT IN MEXICO 125 0 0 0 0 0 2 2 5 3 100 640 527 3 0 0 0 0 0 0 4 0 91 720 531 0 0 1 0 0 0 0 6 2 92 720 554 1 0 2 0 0 0 0 7 0 100 720 632 1 0 2 0 3 0 0 0 0 88 640 945 3 0 0 0 0 0 1 1 2 85 720 1256 1 0 1 0 0 0 2 4 4 76 720 1312 Elevational sampling points Elevationalsampling 2 0 2 0 0 0 2 1 0 Table1 93 720 1318 0 3 0 8 2 0 0 3 0 100 640 1794 0 0 5 2 0 1 8 0 11 100 720 1988 0 6 0 3 4 0 0 4 1 100 720 2017 0 4 0 0 0 0 0 0 97 13 720 2020 Sample completeness (%) completeness Sample Trap nights nights Trap Tylomys nudicaudus Tylomys Reithrodontomys mexicanus Reithrodontomys Peromyscus mexicanus Peromyscus Peromyscus guatemalensis Peromyscus Peromyscus aztecus Peromyscus Oryzomysalfaroi Nyctomys sumichrasti Nyctomys Heteromys desmarestianus Heteromys Scientific name Marmosamexicana Non-volant small mammals along elevational sampling sites at El Triunfo Biosphere Reserve. Shaded areas indicate groups included in the generalized linear in thelinear generalized included groups Reserve. indicate areas Shaded Biosphere El Triunfo at sites sampling elevational along small mammals Non-volant model(GLMM). mixed 126 Mastozoología Neotropical, 24(1):121-134, Mendoza, 2017 JL Mena and RA Medellín http://www.sarem.org.ar - http://www.sbmz.com.br or less similar (negative autocorrelation) than ex- without replacement). Regression of the empirical pected for randomly associated pairs of traplines. The data on the predicted values based on the average spatial autocorrelogram for small mammal diversity of 50 000 iterations, provided r2 estimates to the fit indicated that it was positively autocorrelated up of the null model (McCain, 2004). to c. 60 m (with statistical significance), followed We performed a linear regression to assess the by a continuous decrease in Moran’s I coefficients relationship between habitat complexity and eleva- (non-significant) up to c. 1600 m, at which point tion. In addition, we conducted generalized linear there is a highly significant negative autocorrelation models (GLM) with assumption of Poisson errors coefficient. After including elevation variables succes- (Zuur et al., 2009; Crawley, 2013), to quantify the sively in the model (linear and quadratic function), effects of habitat complexity on alpha diversity, over- spatial autocorrelation in the residuals disappeared. all abundance, and abundance of the most common Results were not affected by number and definition species along the elevational gradient. For model of distance classes in the correlogram, and Moran’s selection we used the Akaike Information Criterion coefficient was not significant (P > 0.90). (AIC), and all models with AIC differences of less than 2 were retained because it is suggested that Hypothesis assessment these have a substantial level of empirical support Habitat complexity describes the development of (Burnham and Anderson, 2002). For small sample vertical strata within an habitat (August, 1983). sizes (n), where n/k < 40 (where k is the number

Thus, complex habitats would have dense and tall of parameters) a “corrected AIC” (AICc) (Bolker, ground cover, many large trees and shrubs, and a 2008) is recommended, so that, selection model was dense canopy and understory. In this way, we per- based on AICc. All analyses were executed using formed a principal components analysis (PCA) to the R programming environment (R Core Team, identify vegetation variables that helped distinguish 2016), with ggord (Beck, 2016), lme4 (Bates et al., the trap lines in terms of their habitat complexity 2015) and AICcmodavg (Mazerolle, 2016) libraries. along the elevational gradient. The idea of PCA is to We estimated the explained deviance (because we find a small number of linear combinations of the do not have an R2 in GLM models), which was variables so as to capture most of the variation in calculated in terms of null deviance and residual the data as a whole, and only the first two or three deviance (Zuur et al., 2009). components are generally used as new variables, To answer the question about how strongly alpha since they often explain most of the total sample diversity is related to habitat complexity, or to predict variance (Crawley, 2013). Since the variables are alpha diversity from habitat complexity, we drawn expressed in different measurement scales, we com- inferences from a Bayesian framework, where is puted a PCA on the correlation matrix, including key the joint posterior distribution of β (vector that 2 a standardization of the variables (Borcard et al., contains β0 and β1) and σ , the residual variance. 2011). As index of habitat complexity by each trap The posterior distributions describe the range of line we used the scores resulting from the first and plausible parameter values given the data and the second principal components (PC1 y PC2), because model, an estimate of our uncertainty about the these condense all of the original variables into two model parameters. In this way, the 2.5% and 97.5% measures of overall size. We selected the number quantiles of the marginal posterior distributions can of axes representing the major features of the data be used as 95% credible intervals (CrI) of the model according to the Kaiser-Guttman criterion, which parameters, thus, the interpretation of the 95% cred- consists in computing the mean of all eigenvalues ible interval is straightforward, in this way, we are and interpreting only the axes whose eigenvalues 95% sure that the true regression line is within the are larger than that mean (Borcard et al., 2011). credible interval (Korner-Nievergelt et al., 2015). The species richness data for alpha diversity along First, we obtained parameter estimates and then, we the elevational gradient were compared with null used the sim function in the package arm (Gelman model predictions using the Monte Carlo simulation and Su, 2015), which uses the results from the model program “Mid-Domain Null” (McCain, 2004). This fit to calculate the posterior distribution assuming program simulates species richness curves based on flat prior distributions (Gelman and Hill, 2007). We empirical range sizes or range midpoints within use the function sim to draw 5000 random values a bounded domain. The hard boundaries for the from the joint posterior distribution of the model model were defined by the lowlands and highlands parameters; that is, we draw 5000 values for each of the study area. The elevation midpoint for each parameter while taking the correlation between the species was drawn at random (50 000 iterations, parameters into account. We obtained a graphical SMALL MAMMALS AND ELEVATIONAL GRADIENT IN MEXICO 127

2 output using a frequentist method with the predict (y = -2.203 + 0.002x, R = 0.37, F1, 10 = 5.92, function (Crawley, 2013; Korner-Nievergelt et al., P = 0.035). Alpha diversity was poorly associated 2015), which simulated data to estimate confidence with elevation; however, it showed a positive intervals around the predicted line (Zuur et al., modest association with habitat complexity (see 2009). This provides a similar result with Bayesian Table 4 and Fig. 3). The uncertainty measure- methods, but it is simpler in R (Korner-Nievergelt et ments for the parameter estimates (alpha diver- al., 2015). Finally, we conducted a generalized linear mixed model (GLMM) grouping nearby sampling sity vs. habitat complexity) were obtained from sites (< 60 m apart), in three groups (low, middle the posterior distribution simulated by sim. The ^b and high elevation), following recommendations 95% credible interval of 1 (Alpha diversity ~ provided by the autocorrelation analysis. GLMM are β0 + β1*habitat complexity) was -0.089 – 0.253 useful when we model spatial correlation, where it (β1= 0.084). Fig. 3 shows alpha diversity with a can be accommodated by adding correlated spatial fitted Poisson GLM curve with 95% confidence random effects, with the correlation being a func- bands. Moreover, we found a distinctive positive tion of their distance in space (Kéry and Royle, relationship between habitat complexity and 2016). This model was performed with assumption abundances of some common species of small of Poisson errors (Zuur et al., 2009; Crawley, 2013), mammals along the elevational gradient (see to quantify the effects of habitat complexity on alpha Table 4). GLMM provides similar results than diversity. Habitat complexity was treated as a fixed factor and elevational replicates as a random one. GLM, a positive relationship between habitat We performed a dispersion test of the mixed model complexity with alpha diversity, and we did not recommend in Zuur et al. (2013). find problems of overdispersion in each case.

RESULTS DISCUSSION One hundred forty-eight individuals represent- Our study provides new data on inventories of ing 9 species were captured in 8400 trap-nights nonvolant small mammals on the Pacific slope (Table 1). The estimated completeness of the of the El Triunfo Biosphere Reserve, one of small mammal fauna for each trap line was the very few remaining complete elevational similar along the elevational gradient (> 76%). gradients in Mesoamerica. Previous inventories Nearly all species were distributed across all in this reserve have been conducted mainly in elevations, with the exception of those restricted the upper cloud forest and along the eastern to higher elevations (i.e. Reithrodontomys slope of the Sierra Madre de Chiapas. Species mexicanus and Peromyscus guatemalensis). accumulation curves showed that our sampling Most species occurring in the lowlands had protocol was efficient and our inventory nearly elevational ranges of between 700 and 1500 m. completes (see Table 1). Complementary meth- Sorex veraepacis (at 2000 m) and Rheomys ods provide records of species at middle and thomasi (at 1300 m) were recorded with pitfall high elevations (S. veraepacis and R. thomasi), and Ichthyomyinae protocol respectively. Alpha and two additional records (Mena, in litt.), diversity did not fit the predictions of a null Neotoma mexicana at 1611 m in a previous model (see McCain, 2004). pilot assessment and an anecdotic record of Our PCA model was performed with 8 habi- Oligoryzomys fulvescens at 500 m provide some tat variables (see Table 2), and the first two evidence of an increase in species at middle principal components explaining > 65% of the and high elevations. variance of the data (Fig. 2). Trap lines at high Only three relatively rare species known from elevations displayed the largest value of number the area such as Cryptotis goodwini, Habromys of trees and basal area (Table 3). Trap lines at lophurus and Reithrodontomys megalotis (Me- low elevations (and middle elevations) showed dellín, 1988; Espinoza-Medinilla et al., 1998) the largest values in herbaceous cover and the were not represented in our samples. McCain lowest values in canopy cover and basal area. (2004) emphasized the importance of replica- Habitat complexity showed a significant, tion in examining spatial diversity but noted positive modest association with elevation that if only single surveys are feasible, sampling 128 Mastozoología Neotropical, 24(1):121-134, Mendoza, 2017 JL Mena and RA Medellín http://www.sarem.org.ar - http://www.sbmz.com.br 7 4 12 19 19 45 36 97.4 2020 654250 6 6 11 28 28 44 42 87.5 2017 1221576 4 2 10 49 35 64 57 85.4 1988 1000017 9 9 7 7 4 91 28 64.4 1794 528693 8 9 8 1 28 25 20 95.3 1318 638056 7 3 38 31 56 39 13 93.2 1312 Fig. 2. PCA biplot of the habitat variables (BA, CC, HC 1238237 and trees) along the elevational gradient at the El Triunfo. The plot shows sampling sites. 6 8 1 31 25 40 44 93.2 1256 687346 small mammals in Central American forests Table2 should be during the wet season (but see Wen 5 4 3 84 26 38 12 945 87.3 et al., 2014). Although, expansion of species 602462 elevational ranges and seasonal differences in species trappability due to season remain to 4 8 8 2 be assessed, we do not think that our results 59 17 19 632 96.1

586031 were strongly affected. Overall, it is becoming increasingly rare to find complete and intact elevational gradients ranging from sea level 3 9 4 1 18 18 19 554 98.4 to high-elevation mountaintops due to habitat 237571 disturbance (Nogues-Bravo et al., 2008). A quantitative analysis of species richness 2 9 5 3 patterns (plants, invertebrates and vertebrates) 17 27 24 531 96.9

457679 along elevational gradients world-wide (including 204 data sets) showed that about 50% were hump-shaped, 25% were monotonically- 1 1 2 31 23 28 31 527

95.1 decreasing, and 25% had other distributions Average of vegetation variables for each trapping site along the Pacific slope of the El Triunfo, Chiapas, México. Chiapas, Triunfo, of the El slope the Pacific along site trapping each for variables vegetation of Average 268029 (Rahbek, 2005). A mid-elevational peak in species richness has been suggested to be the rule for terrestrial small mammals (McCain, 2005). However, some studies have shown an increase of species richness with elevation. On the western slope of the Andes, diversity increases with elevation, probably as a result of Variable/Elevation Trapline Trapline Canopy cover Canopy Herbaceous cover Herbaceous Tree species richness Tree Trees 5- 10 d.b.h. 10 5- Trees Trees 10-30 d.b.h. 10-30 Trees Trees 30-50 d.b.h. 30-50 Trees Trees > 50 d.b.h. 50 > Trees Tree basal area/ha Tree increased rainfall (and vegetation), and more SMALL MAMMALS AND ELEVATIONAL GRADIENT IN MEXICO 129

Table 3 Summary of the first two principal components

Variable Acronyms PC1 PC2 Standard deviation 1.7620 1.5578 Proportion explained 0.3880 0.3034 Cumulative proportion 0.3880 0.6914 Canopy cover PCC 0.0562 -0.5208* Herbaceous vegetation HC -0.1546 0.5516** Tree species TSPP 0.4515** -0.2809 Trees 5- 10 d.b.h. T0510 0.5191** 0.0929 Trees 10-30 d.b.h. T1030 0.4791** -0.0326 Trees 30-50 d.b.h. T3050 0.0863 0.3426 Trees > 50 d.b.h. T>50 0.1498 0.3989 Basal area AB 0.4900** 0.2437 Spearman’s rank correlation with original variables are showed as P < 0.01 (**) and P < 0.05 (*)

Table 4 Summary of model (GLM) selection for data on small mammal communities along the elevational gradient at El Triunfo. Only models with a model weight (w) > 0.1 are shown.

^ MODEL β AICc ∆AICc w -2l ED Alpha diversity Habitat complexity 0.084 45.86 0.00 0.50 -20.26 24.56 Habitat complexity* 0.118 39.60 1.00 -16.81 39.48 Abundance Habitat complexity + Habitat complexity^2 0.048 72.30 0.00 0.79 -33.48 52.17 Habitat complexity^2 0.122 75.24 2.94 0.18 -36.67 49.89 Heteromys desmarestianus Habitat complexity^2 0.045 71.52 0.00 0.47 -33.09 2.48 Habitat complexity 0.065 71.57 0.05 0.45 -33.12 2.30 Marmosa mexicana Habitat complexity 0.196 40.51 0.00 0.52 -17.59 5.92 Habitat complexity^2 0.070 40.70 0.19 0.48 -17.69 1.78 Peromyscus aztecus Habitat complexity^2 0.216 38.70 0.00 0.70 -16.68 14.88 Reithrodontomys mexicanus Habitat complexity^2 0.379 53.11 0.00 0.54 -23.89 42.07 Habitat complexity + Habitat complexity^2 0.259 54.55 1.44 0.26 -22.78 45.90

AIC: Akaike Information Criterion, ∆ AICC: difference in AICC values between each model and the best model, w: AICC model weight, -2l: twice the negative log-likelihood, ED: Explained deviance. * Generalized linear mixed model (GLMM). 130 Mastozoología Neotropical, 24(1):121-134, Mendoza, 2017 JL Mena and RA Medellín http://www.sarem.org.ar - http://www.sbmz.com.br

Fig. 3. Observed species richness with a fitted Poisson GLM (solid line) and 95% confidence bands (dotted lines) based on estimates of model parameters ^ ^ ( β1 = -5.265, β0 = 0.084)

in alpha diversity, and none in Mesoamerica (Patterson et al., 1990; Kok et al., 2012). Indeed, this paper suggests that habitat complexity is positively related to alpha diversity. In temperate Andean rainforest, Pat- terson et al. (1990) found that elevation variation speciation events there (Pearson and Ralph, in habitat and specific habitat associations pro- 1978; Marquet, 1994); a similar increase has vided a plausible explanation for correlations been reported in the Philippine Islands (Rickart of mammalian distribution and abundance et al., 1991; Balete et al., 2009; Rickart et al., with elevation. Similarly, Batin et al. (2002) 2011) and, associated with highlands being found that elevation significantly influenced centers of mammalian diversity. Our results habitat variables along an elevational gradi- suggest that alpha diversity is associated with ent in Mount Nuang (Malaysia), suggesting habitat complexity along the Pacific slope of El that declining habitat structure may reduce Triunfo, but a clear relationship with elevation resource availability with increasing eleva- was not evident. Furthermore, it is important tion, and thus explain declining small mam- to acknowledge that Highlands of Chiapas have mal diversity. Similar results were described been recognized as an important biogeographic for Mount Kilimanjaro, Tanzania (Mulungu region, harboring high levels of species rich- et al., 2008) and Oaxaca, México (Sánchez- ness and endemicity (Escalante et al., 2007). Cordero, 2001) where small mammal diversity Endemic species restricted to upper cloud and distribution patterns were influenced by forest include Cryptotis goodwini, Habromys habitat complexity at different elevations. In lophurus, Peromyscus aztecus, P. guatemalensis our study site, traplines at the upper cloud and Sorex veraepacis which occur there or in forest (> 1800 m) was characterized by higher high elevation sites in adjacent regions of Chi- tree basal areas, and abundance and species apas (Espinoza-Medinilla et al., 1998; Vázquez richness of trees (Table 2; Williams-Linera, et al., 2001; Carraway, 2007). Elsewhere, the 1991; Martínez-Melendez et al., 2008), lending Chiapas highlands and north-western Middle support to the habitat complexity hypothesis. America are implicated as an important area for In general, this forest, the wettest habitat on diversification of small mammals (Woodman, the Pacific versant of Chiapas, and indeed, of 2005; León-Paniagua et al., 2007; Rogers et al., Mexico, had greater species richness compared 2007), and provide support for a hypothesis with other habitats along the gradient. This that species richness (alpha diversity) increases is consistent with the hypothesis that species in areas with high rates of speciation (see richness increases with increasing rainfall and Sánchez-Cordero, 2001). primary productivity (Rahbek, 1997; Lomolino, Few studies have integrated habitat struc- 2001; Sánchez-Cordero, 2001). Moreover, we ture in the assessment of elevational patterns found some relationships between habitat and SMALL MAMMALS AND ELEVATIONAL GRADIENT IN MEXICO 131 species abundance. For example, Heteromys Middle American montane forests are se- desmarestianus is associated with several verely threatened by deforestation and other vegetation types including tropical rainfor- anthropic impacts at lower elevations, and this est, coffee plantations and agricultural lands may have already affected the ability to detect from sea level up to ~ 1860 m a.s.l. (Ceballos, processes which explain diversity there (see 2014), but despite their apparent generalist Nogues-Bravo et al., 2008). In fact, complete habitat requirements, we found a relationship elevational gradients in reasonably intact state with habitat complexity. Similarly, Marmosa are virtually absent in the region. Thus, we need mexicana appears to be associated to forest to preserve as many such tropical gradients habitats and disturbed areas (Alonso-Mejía as well as cloud and montane forests as pos- and Medellín, 1992; Ceballos, 2014), and we sible. Understanding the factors that explain found an association with habitat complexity. diversity along elevational transects remains a Our analyses suggest that spatial patterns of key question in ecology and conservation, and species richness cannot be attributed primarily El Triunfo is a primary example that provides to spatial autocorrelations in elevation. Most important insights on this. positive autocorrelations were for sites with close elevational proximity and are likely a ACKNOWLEDGEMENTS result of sampling within areas with similar This work was supported by the SRE scholarship from the habitat characteristics; this is mainly a problem Mexican government to José Luis Mena, by a grant from when explicit causal effects are being tested Rufford Small Grant and by the Instituto de Ecología, (Legendre et al., 2002). The main positive, Universidad Nacional Autónoma de México. We thank small-scale spatial autocorrelations in our data Lisette Adrianzén, Marco Hernández, Susana Maza, and Alejandro Gómez Nisino for their support during the field suggest that some replicates from nearby trap work. We also thank Osiris Gaona for technical assistance. lines were not truly independent (< 60 m be- Thanks are extended to the El Triunfo Biosphere Reserve tween elevations). Thus, studies analyzing spe- for permission to conduct this study. This is a contribution cies richness should not ignore spatial effects, of the Wildlife Trust Alliance and BIOCONCIENCIA, A.C. and sampling sites along elevational gradients should be distant enough from each other to LITERATURE CITED ensure that spatial autocorrelation is minimal. ALONSO-MEJÍA A and RA MEDELLÍN. 1992. Marmosa We did not address how the anthropic dis- mexicana. Mammalian Species 421:1-4. turbance of the lowlands may have influenced AUGUST PV. 1983. The role of habitat complexity and heterogeneity in structuring tropical mammal’s small mammal distributions and elevational communities. Ecology 64:1495-1507. patterns. In particular, lowland forests ( < 800 m BALETE DS, LR HEANEY, MJ VELUZ and EA RICKART. a.s.l.) have been largely fragmented and defor- 2009. Diversity patterns of small mammals in the ested on much of the Pacific versant of Chiapas; Zambales Mts., Luzon, Philippines. 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