Habitat and Human Factors Associated with White-Tailed Deer Density in the Tropical Dry Forest of Tehuacán-Cuicatlán Biosphere Reserve, Mexico

2017

Therya

Self Cite

The white-tailed deer, Odocoileus virginianus, is poorly known in the state of Veracruz, Mexico. In this paper we report the relationships between deer population density and vegetation variables in a landscape in Tantoyuca, northern Veracruz. Pellet groups and vegetation were sampled across 40 strip transects (500 x 2 m) in five locations from April to June 2014. The relationships between deer density and vegetation variables were evaluated with hierarchical agglomerative clustering methods to group tree species per transect, as well as generalized linear models. Density (number of deers/km 2 ) was estimated using the semi-automated process PELLET, and was compared between locations, major vegetation types, and transect clusters. Deers were present in 98 % of the transects sampled, with a mean density of 4.2 ± 2.8 deers/km 2 . Higher densities were estimated in secondary vegetation and pasture-cropping sites. The 40 transects were grouped into four clusters according to the abundance of tree species. In addition, deer density varied between clusters. The density of the white-tailed deer was positively correlated with understory cover and negatively correlated with basal area. Deer density in Tantoyuca is within the range reported in other regions of the state of Veracruz. The use of generalized linear models enabled to predict the density of the white-tailed deer based on some vegetation variables. The localities studied differ in plant composition and structure, and the highest deer density was located in sites with a more diverse and dense plant composition and structure. It is suggested to undertake an in-depth investigation about the effect of landscape structural complexity and connectivity on this and other wildlife species.El venado cola blanca Odocoileus virginianus ha sido poco estudiado en el estado de Veracruz, México. En el presente trabajo se estimó la densidad poblacional de esta especie y se analizó su relación con la vegetación en un paisaje transformado en cinco localidades en municipio de Tantoyuca en el norte del estado. En cada localidad se colocaron 8 transectos (500 x 2 m) para el conteo del número de grupos fecales y la caracterización de la vegetación, y se muestrearon de abril a junio del 2014. Se emplearon análisis de agrupamiento jerárquicos de los transectos en función de similitud en la composición del estrato arbóreo, y modelos lineales generalizados para relacionar la densidad de venados con las variables vegetacionales. La densidad (venados/km 2 ) se estimó con el procedimiento semi-automatizado PELLET, y se comparó entre localidades, principales tipos de vegetación, y grupos de transectos. Los venados estuvieron presentes en el 98 % de los transectos y se estimó la densidad promedio de 4.2 ± 2.8 venados/km 2 . Las densidades más altas se estimaron en la vegetación secundaria con remanentes de selva baja y en las zonas de cultivos-pastizal. El área basal y la cobertura del sotobosque fueron las principales variables para predecir la densidad de este venado en la región de estudio...

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Density of white-tailed deer in relation to vegetation in a landscape of northern Veracruz

Ángel

2017

Therya

Self Cite

“…We considered it important to include the former since it is related to deer strategy for escaping from predators [13,50,51]. Regarding the latter, it is well known that human presence and activity have a negative effect on many species, which has been described for the white-tailed deer in Mexico by some authors [17,18,52]. However, while it is possible to note certain trends in the relationship between these variables and deer density, this was not found to be significant in either of the two study regions.…”

Section: Explicative Environmental Variablesmentioning

confidence: 99%

“…In addition, we analyzed whether differences in deer density and habitat variables existed between the two study regions. This evaluation is important because studies conducted in Mexico on the effects of different habitat variables on the density or abundance of the white-tailed deer [11][12][13][14][15][16][17][18] autocorrelation [19]. Understanding and including spatial autocorrelation can reveal more accurately the relationship between habitat and white-tailed deer population, with important repercussions for conservation and management of the white-tailed deer in Mexico.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Relationship between White-Tailed Deer and Environmental Conditions using Spatially Autocorrelated Data in Tropical Dry Forests of Central Mexico

Yáñez‐Arenas

Tropical Conservation Science

2015

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Ordinary least-square (OLS) regression is fundamental to quantitative research in many ecological disciplines. However, spatially explicit methods have recently been proposed that allow the incorporation of spatial autocorrelation into ecological models. We compared the spatial error simultaneous autoregressive model (SARerr) and generalized least squares regression (GLS) with the results of simple and multiple OLS regressions, to analyze the relationship between white-tailed deer (Odocoileus virginianus) population density and environmental conditions in two regions dominated by tropical dry forests in central Mexico. The spatially explicit methods presented better goodness of fit than the OLS regression; we also observed a miscalculation in the probabilities obtained with the OLS regression, which in this method led to an incorrect interpretation. In general, we suggest the application of spatially explicit methods to analyze species-habitat relationships when SAC is observed in model residuals. We also discuss the management implications of these results.Key words: species-habitat relationships, spatially explicit models, autoregressive model, generalized least squares regression. ResumenEn los últimos años se han propuesto diversos métodos conocidos como espacialmente explícitos que permiten incorporar la autocorrelación espacial en los modelos. En este estudio, aplicamos modelos autorregresivos simultáneos (SARerr) y generalización de mínimos cuadrados (GLS) para analizar la relación entre la densidad poblacional del venado cola blanca Odocoileus virginianus y un conjunto de variables de hábitat en dos regiones dominadas por bosque tropical seco en el centro de México. Contrastamos los resultados con los obtenidos con la regresión simple y múltiple por cuadrados mínimos ordinarios (OLS), que es el método comúnmente usado en este tipo de estudios. Los métodos espacialmente explícitos tuvieron un mejor desempeño que la regresión OLS en términos de bondad de ajuste. Por otro lado, observamos un cálculo incorrecto en las probabilidades obtenidas con la regresión OLS lo que en este método nos llevó a una mala interpretación de cuáles son las variables asociadas con la densidad de esta especie. Recomendamos, de manera general la aplicación de métodos espacialmente explícitos para analizar las relaciones especie-hábitat cuando se observe autocorrelación espacial en los residuales de los modelos. Discutimos las implicaciones de manejo.Palabras clave: relación especie-hábitat, modelos espacialmente explícitos, modelo autorregresivo.

“…In Mexico, the method has been used for the mule deer [13,14] and the white-tailed deer, mainly in temperate [15][16][17][18][19][20][21][22] and tropical dry [23][24][25][26][27][28][29] forests. The method also serves to estimate the population density of various species of leporids [30][31][32][33][34], kangaroos [35][36][37][38][39], elephants [40] and marmots [41].…”

Section: Introductionmentioning

confidence: 99%

PELLET: An Excel®-Based Procedure for Estimating Deer Population Density Using the Pellet-Group Counting Method

Tropical Conservation Science

2014

Self Cite

Pellet-group counting (PGC) is a commonly used method for estimating the population density of various ungulate species. The method assumes a positive linear relationship between the number of animals and the number of pellet-groups. To estimate population density from PGC, three parameters must be determined: defecation rate, persistence time, and spatial pattern of pellet-groups. This article introduces PELLET, a semi-automatic procedure in Microsoft Excel® for estimating population density in deer. The purpose of PELLET is to support deer managers and studies that determine and compare densities in different types of habitats. The density calculation includes a range of variation in the defecation rate that eliminates the subjective practice of selecting one single defined value for this rate. The calculation also incorporates spatial variation and deposition time. PELLET comprises four Excel® worksheets, the choice of which depends on type of plot and number of field samples. As an example, I describe the application of this procedure in a case study. Spanish and English versions of PELLET are freely available online.Keywords: monitoring, defecation rate, spreadsheet, linear model, probability distribution. ResumenEl conteo de grupos fecales (CGF) es un método empleado comúnmente para estimar la densidad poblacional de varias especies de ungulados. Este método asume una relación lineal positiva entre el número de venados y el número de grupos fecales. Para convertir el CGF a número de animales, se requiere conocer tres parámetros: la tasa de defecación, el tiempo de permanencia y el patrón espacial de los grupos fecales. En este artículo se introduce PELLET un procedimiento semiautomatizado en Excel® para estimar la densidad de venados. El propósito de PELLET es apoyar a los manejadores y estudiosos de los venados para calcular y comparar las estimaciones en diferentes tipos de hábitats principalmente bosques densos en regiones tropicales y otras. El cálculo de la densidad se realiza incluyendo un rango de variación en la tasa de defecación lo cual elimina la subjetividad de seleccionar solo un valor determinado de la misma. Además, el cálculo incorpora la variación espacial y el tiempo de depósito de los excrementos. PELLET tiene cuatro hojas dependiendo el diseño de muestreo: tipo de parcelas y número de muestreos en campo. A manera de ejemplo, se presenta la aplicación de este procedimiento en un estudio de caso. PELLET está disponible gratis en línea en versión en español o inglés.Palabras clave: monitoreo, tasa de defecación, hoja de cálculo, modelo lineal, distribución probabilística.