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Title: Predicting range expansion of Invasive Raccoons in Northern Iran
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Using ENFA Model at Two Different scales
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Authors: Azita Farashi1, Mohammad Kaboli1 and Mahmoud Karami1
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1. Department of Environmental Sciences, Faculty of Natural Resources, University of
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Tehran, Karaj, Iran.
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Keywords: Raccoon, Iran, ENFA, Invasion, Distribution.
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Corresponding author: Azita Farashi
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E-mail: farashi@ut.ac.ir
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Tell: + 989166690479
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Fax: +98 26112245908
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Abstract
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Invasive alien species are considered to be one of the most important causes of extinction
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and decline of wild native species. The raccoon (Procyon lotor) is native to North and
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Central America but at present it also occurs in several European and Asian countries. In
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1991, the raccoon was recorded for the first time in Iran from Lavandevil Wildlife Refuge.
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In order to examine how variation in the extent of the study area influences habitat
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selection of the raccoon, we ran models at two different scales at Lavandevil Wildlife
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Refuge and the Gilan province. We used the Ecological Niche Factor Analysis (ENFA) to
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describe the invasive raccoons’ realized niche and to identify areas exposed to the invasion
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of the raccoon in northern Iran. Our results showed that the spatial distribution of the
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raccoon is heavily influenced by natural variables, landscape variables, and human-related
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variables at Lavandevil Wildlife Refuge scale and topography and vegetation variables at
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Gilan province scale. This prediction indicates that the raccoon has a potential to become
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one of the most numerous mammals in northern Iran.
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Keywords: Raccoon, Iran, ENFA, Invasion, Distribution.
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Graphical abstract
Predicting range expansion of Invasive Raccoons in Northern Iran Using ENFA
Model at Two Different scales
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Highlights
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We ran models at two different scales at Lavandevil Wildlife Refuge and the Gilan
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province > Ecological Niche Factor Analysis (ENFA) > We recognized new regions of the
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species’ occurrence in the Gilan province.
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1. Introduction
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Invasive species have become an issue of great concern in biology, agriculture,
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transportation, and economics (Carlton, 1996; Kareiva, 1996; Williamson, 1999; Enserink,
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1999; Higgins et al. 1999; NAS, 2002) and are considered to be one of the most important
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causes of extinction and decline of wild native species (Margin et al. 1994; Williamson,
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1999). In particular, invasive alien mammals are thought to have serious impacts on native
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ecosystems because of their high trophic level (Ikeda et al. 2004). However, in practice,
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management decisions against a certain invasive species are often hampered by lack of
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relevant ecological information such as the expected distribution and the impacts of the
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species (Strubbe and Matthysen, 2009).
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Models predicting the spatial distribution of species (Boyce and McDonald, 1999; Guisan
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and Zimmermann, 2000; Manly et al. 2002; Pearce and Boyce, 2006) have been of intense
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interest for at least 3 decades. As they often help in both understanding species niche
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requirements and predicting species potential distribution, their use has been especially
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promoted to tackle conservation issues, such as managing species distribution, estimating
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the risk of biological invasions, and managing invasive species (Scott et al. 2002; Guisan
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and Thuiller, 2005).
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Different types of modeling techniques are used to fit different types of biological
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information recorded at each sample site: (1) presence-only: occurrences of the target
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species are recorded; (2) presence/absence: each sample site is carefully monitored so as to
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assert with sufficient certainty whether the species is present or absent. Absence data are
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not available, are unreliable (for cryptic or rare species), or, as in the case of an invading
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alien species, are of limited use because certain sites may be suitable but have not been
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reached by the invader yet (Hirzel et al. 2002). Methods that predict species distribution
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from presence-only data search for an environmental ‘envelope’ characterizing the areas in
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which the species is present and extrapolate to the remaining areas under study (Guisan
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and Zimmerman, 2000). Examples of these alternative techniques, often called profile or
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envelope methods (Pearce and Boyce, 2006), are Bioclimatic Prediction Systems, Support
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Vector Machines (SVM), Ecological Niche Factor Analysis (ENFA) (Busby, 1991; Guo et
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al. 2005; Hirzel et al. 2002), and Genetic Algorithm for Rule-set Production (GARP)
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(Stockwell and Noble, 1992; Stockwell and Peters, 1999; Stockwell, 1999). The ENFA is
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one of suitable models for monitoring the potential spread of invasive alien or re-
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introduced species (Acevedo et al. 2007; Casinello et al. 2006; Hirzel et al. 2001, 2004).
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The relationship between organisms and their environment can vary across spatial
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landscapes and different patterns can emerge at different spatial landscapes. Currently
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there is a growing awareness that our understanding of ecological processes can be
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influenced greatly by the spatial or biological context in which they are investigated.
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Recognizing this potential bias, recent studies evaluating resource selection by wildlife
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have accentuated the need to examine multiple spatial scales in habitat-selection studies
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(Bowyer and Kie, 2006). Although the differential selection of habitats across various
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spatial landscapes certainly can occur in relatively homogeneous environments, the
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influence of resource distribution and animal movement behavior upon studies of habitat
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selection likely is greatest in the diverse matrix of landscape attributes that exemplify
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heavily fragmented landscapes (Iverson 1988; Andersen et al. 1996; Spetich et al. 1997).
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Most wildlife habitats are fragmented by human activities in large landscape. Accordingly,
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we explore ENFA approach in two scales to predict the potential geographic distribution of
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the raccoon in north of Iran.
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The raccoon (Procyon lotor) is native to North and Central Americas. In 1991, the raccoon
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was recorded from Iran near Iran-Azerbaijan border for the first time. Knowledge on the
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species’ distribution, invasion trend, and effects in Iran is poor. To manage the invasive
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species in the first step in this study were determined actual and potential distribution of
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this species and also the environmental parameters that control its distribution. Therefore,
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the aims of this study were: (i) to investigate the environmental factors that constrain the
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current distribution of the raccoon in two scales: Lavandevil Wildlife Refuge and Gilan
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province, (ii) to investigate invasion trends of the raccoon in the Gilan province, and (iii) to
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compare raccoons’ realized niche in two different scales.
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2. Material and methods
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2.1. Study area
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We limited our study area to the Gilan province because reports of raccoon presence are
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limited to this province. Gilan province is situated in the north of Iran and the southwest of
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the Caspian Sea. It has a surface area of 14,711 square km, and is located between 36° 36′
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12″ and 38° 27′ 10″ north latitude and from 48° 43′ 18″ to 50° 34′ 11″ east longitude (Fig.
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1). Gilan territory is composed by two following regions: The lowlands, adjacent to the
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Caspian Sea and the Alborz Mountains. Lavandevil Wildlife Refuge is situated at the
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eastern part of the Gilan province. It has a surface area of 9.49 square km, and is located
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from 38° 18′ 21″ to 38° 23′ 26″ north latitude and from 48° 51′ 16″ to 48° 53′ 11″ east
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longitude (Fig. 1). The site is among the main habitats of birds in Iran; some 125 species of
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birds have been identified here. However it is of low security for the raccoons. This lack of
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security is mostly due to the attitude of local people, especially farmers, towards harmful
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species which in most cases leads to the extermination of the intruder animal. In this study,
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Lavandevil Wildlife Refuge was selected as small scale for two reasons: 1) most of reports
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of raccoon presence were in this area (32 reports), and 2) since Lavandevil Wildlife Refuge
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is located in a mosaic of human activities, including urban and agricultural areas, the
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raccoon has not been recorded in regions surrounding the Refuge.
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2.2. Methods
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2.2.1. Ecological Niche Factor Analysis (ENFA) model
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The ENFA is a presence-only multifactor analysis, comparing the distribution of species to
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a global available environment in a hyperspace defined by ecogeographical variables
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(EGVs) (Hirzel et al., 2002). The transformation of EGVs into a set of uncorrelated
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factorial axes introduces ecological concepts of marginality and specialization. Marginality
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(i.e., how much a species’ habitat differs from the mean available conditions) is
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represented in the first factorial axis and specialization (i.e., breadth of the ecological
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niche) is maximized in the subsequent axes. The factorial axes’ coefficients give the
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importance of each EGV in the different axes and the relative range of the EGVs
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associated with the species. They are also used to compute global marginality (M,
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indicating some degree of marginality when greater than 1), specialization (S, varying
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generally from 0 to ∞), and global tolerance that is the inverse of specialization (T, varying
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generally from 0 to 1) (Hirzel et al. 2002, 2004).
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The first factor extracted gives the marginality coefficient, which is defined as the
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standardized difference between the average conditions in areas with the species present,
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and conditions of the entire study area. This marginality ranges from -1 to +1 and indicates
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the rarity of the conditions selected by the raccoons within the study area. Positive or
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negative values show a species’ optimum to be higher (respectively lower) than the
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average conditions in the study area. All the subsequent factors (i.e., ‘specialization’
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factors) maximize ratio of the species variance to the global variance. Successive factors
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explain the remaining specialization in decreasing amounts. A high value of a
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specialization coefficient indicates a narrow niche breadth in comparison with available
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conditions.
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Finally, a Habitat Suitability (HS) map is built. It compares the position of each cell of the
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study area to the distribution of presence cells on the different factorial axes. HS values
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range from 0 to 100: a cell adjacent to the median of an axis would score 100 and a cell
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outside of the species distribution would score zero (Hirzel et al. 2002, 2007). All the
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ENFA analyses were conducted using Biomapper 4© software (Hirzel et al. 2007) (For
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details, see Hirzel et al. 2002, 2006).
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2.2.2. ENFA Model validation
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In order to assess the statistical fitness of a predictive habitat model, an extensive number
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of evaluation statistics have been developed. However, most of these methods have been
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developed for presence/absence data and crucially rely on a confusion matrix (a
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contingency table that counts how many presence and absence evaluation points occur in
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both suitable and unsuitable areas) (Hirzel et al. 2006). Presence-only models, such as
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ENFA, suffer from a lack of absence points which causes all methods related to the
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confusion matrix to be flawed (Boyce et al. 2002). ENFA introduces the concepts of
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Explained Specialization (ExS, identical to the traditional ‘‘Explained specialisation’’) and
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Explained Information (ExI, a modified version of ExS that takes the marginality factor
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into account) to indicate how the computed HS models explain the observed data. By
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comparing the ENFA eigenvalues to MacArthur’s broken-stick distribution, we determined
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the number of significant factors to be used in the analyses (Hirzel et al. 2002).
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To assess the robustness and the predictive power of a HS model, ENFA uses the novel
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continuous Boyce index, ExS and ExI (Hirzel et al. 2006, 2002) and their value ranges
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between 0 and 1 (the closer to 1, the better the model), the novel continuous Boyce index a
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threshold independent modification of the Boyce index (Boyce et al. 2002), which
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measures the relation between the observed and expected number of validation points for
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different HS values. The continuous Boyce index yields a smooth curve. By applying a k-
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fold cross validation, k estimates of the continuous Boyce index are produced, allowing
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assessment of its central tendency and variance (Hirzel et al. 2006). The advantage of the
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continuous Boyce index is that it provides guidelines for choosing the number of HS
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classes and their boundaries that give the most consistent prediction of HS (For details, see
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Strubbe and Matthysen, 2009).
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2.2.3. Invasive sampling
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Invasive presence points were collected at the Lavandevil Wildlife Refuge in the field
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using a GPS unit with an accuracy of +/- 5m from November 2008 to November 2009. To
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identify these points, all information from sighting the animal, trapping, and camera trap
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photos were used and location error was minimized since the area was very well known.
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Presence points in the Gilan province were collected from records of Gilan Department of
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Environment, and from regional inventories covering the period 2008–2010. All the
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locations where the raccoon had been reported were examined in the field by searching for
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the animal or signs of its presence, trapping, and taking pictures using camera traps.
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2.2.4. Micro variables for Lavandevil Wildlife Refuge scale and macro variables for
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Gilan province scale
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Habitat variables were different in each scale because of the difference between spatial
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resolutions. We used micro variables in the small scales by cell size 5 meter and macro
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variables in the large scale by cell size 30 meter. This was done because we were looking
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for distribution models in the large scale and habitat suitability in the small scale. For
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instance, in the small scale topography variables are important because Lavandevil
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Wildlife Refuge is mostly flat, lacking much variation in altitude and slope but in the large
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landscape, variables like topography and climate vary throughout the study area and have a
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great impact in modeling. Habitat variables in both scales were divided into various classes
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and to determine the role of each class in model accuracy, the model was run using
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different variations of the variables and the novel continuous Boyce index, ExI and ExS,
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were used to determine model accuracy. Micro variables for Lavandevil Wildlife Refuge
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scale in the ENFA model were subdivided into three categories: natural variables, human-
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related variables, and landscape variables (Table 1). Landscape variables were extracted
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from land cover with the aid of Fragstats 3.3 software (McGarigal et al. 2002). These
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variables were calculated for 6 land cover classes (agriculture, deep marsh, forest, wetland,
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grassland, and rural area), resulting in 32 calculated variables. We reduced the initial set of
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32 landscape variables to 8 landscape variables by grouping them using a cluster analysis
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to eliminate those that were highly correlated. Such methods are common in multivariate
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analysis (Hosmer and Lemeshow, 2000); especially those that utilize landscape metrics
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(Clark et al. 1989; Palma et al. 1999; Nielsen et al. 2003). Data for most variables were
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extracted from the databases of the Iran Department of Environment (IDE), but for
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locations of landfill and game guard station, data were recorded in the field using a
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handheld GPS unit (accuracy of +/- 5 m). Macro variables for Gilan province scale were
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subdivided into four categories: topography, vegetation, landscape, and climate.
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Topography variables in this scale were obtained from a Digital Elevation Model (DEM)
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generated by the National Cartographic Center of Iran (NCC) at 1:25000 scale. Vegetation
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variables were Normalized Difference Vegetation Index (NDVI) and vegetation type.
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NDVI was extracted from Landsat TM imagery and existed at a 28.5 × 28.5 m resolution
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and vegetation type (deciduous forest, coniferous forest, shrub, grass land, agricultural
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land, bare land, sandy surface, wetland, and urban area) that were generated by the
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National Cartographic Center of Iran (NCC) at 1:25000 scale. Landscape variables were
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extracted from land use with the aid of Fragstats 3.3 software. These variables were
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calculated for protected area class, resulting in 32 calculated variables. We reduced the
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initial set of 32 landscape variables to 8 landscape variables by grouping them using a
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cluster analysis to eliminate those that were highly correlated. Climatic variables were
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derived from the temperature and precipitation datasets of the Iran Meteorological
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Organization from 1970 to 2008. Here too, the highly correlated variables were eliminated
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using a cluster analysis and the initial set of 19 climatic variables was reduced to 6.
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3. Results
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Table 3 shows the evaluation statistics for the ENFA analysis and HS computations by the
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different combinations of habitat variables at two scales. The high values for ExS, ExI and
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the Boyce index indicated that the dataset containing natural variables, landscape variables,
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and human-related variables at Lavandevil Wildlife Refuge scale and topography and
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vegetation variable at Gilan province scale are the best model. To further differentiate
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between these models, we used the maximum F-value. This value indicates how much a
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model deviates from randomness, and according to this criterion, these variables perform
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the best. In the best model, we determined 5 factors explaining 97% of the information (i.e.
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100% of the marginality and 94% of the specialization) at Lavandevil Wildlife Refuge
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scale and 4 factors explaining 96% of the information (i.e. 100% of the marginality and 92
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% of the specialization) at Gilan province scale to be used in the analyses. Two suitability
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maps were built from these factors, which are plotted in Fig. 1.
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Table 4 and table 5 show the score matrix for the ENFA analysis at two scales. The habitat
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variables in the first column of these tables (Factor 1) are arranged in order of importance
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in model building, with the variables in the upper rows being the most important. The five
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habitat variables affecting raccoon distribution in order of importance included Normalized
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Difference Vegetation Index, Altitude, Slope, Annual Precipitation, and Annual Mean
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Temperature at the large scale and Vegetation density, Water resources, Rubus plant
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community, Punica plant community, and Pterocarya plant community at the small scale.
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Based on these results, vegetation is the most important variable in determining raccoon
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distribution in both scales but in the large scale, this important variable is followed by
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topographic variables while at the small scale, water resources including seasonal wetlands
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and small ponds are of highest importance after vegetation.
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In this study, global marginality was 1.56 and 1.77 in the large and small scales
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respectively. Tolerance in the large and small scales was 0.30 and 0.32 respectively,
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showing that raccoons have a narrow niche in both scales.
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The resulting HS map (Fig. 1) shows that there is ample suitable habitat for raccoons to
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spread in, especially through the center of Lavandevil Wildlife Refuge. The characteristics
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of the suitable area for this species were: Punica plant community, Rubus plant
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community, and vegetation density > 40%. At Gilan province most potential presence
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areas are located close to the Caspian Sea and are below 500 m with a high vegetation
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density. Also, results showed many parts of protected areas in this province are at risk of
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invasion and some of these protected areas are exposed to invasion on their whole range
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(Fig 1).
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4. Discussion
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Models of distribution or suitability can be highly sensitive to the scale of resolution
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(grain) as well as the extent (domain) (Soberón and Peterson 2005) and there are no
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obvious guidelines about which choice of scale is appropriate, because such choice
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depends on the ecology of the organism at hand and the objectives of the investigation
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(Boyce et al. 2003). Due to the management goals that we pursued, and the little that was
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known about the ecology of the raccoon, we chose to carry out the study at two scales, the
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Lavandevil Wildlife Refuge as a protected area, and the Gilan province because it is the
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most vulnerable to raccoon invasion in Iran and management actions are concentrated here.
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In the case of invading species, it is possible that a species may behave as a specialist in
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the early stages of colonization, while becoming more generalist as the population expands
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(Hilden 1965; Sol et al., 1997). Global marginality and global specialization indicated that
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the niche breadth of the species is similar in both scales. The raccoon occupies a narrow
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niche, it can be considered as a specialized species in the area.
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In Lavandevil Wildlife Refuge scale, the most suitable habitats are located in Rubus,
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Punica, Pterocarya, or Alnus plant communities, with a density of over 40 percent. Rubus
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and Punica communities are important food sources for the raccoon, especially in summer
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and fall. Newbury and Nelson (2007) also showed that food resources are important factors
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in determining raccoon distribution.
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In Gilan province scale, most potential presence areas are located close to the Caspian Sea
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and are below 500 m with a high vegetation density, mostly of deciduous forests.
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Therefore our results suggest that dense forests are important to raccoon distribution in
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both scales. Wilson and Nielsen (2007) also point to the fact that size and density of forest
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patches in an area are significantly related to the distribution of the raccoon.
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In Lavandevil Wildlife Refuge scale, the second important micro variable is water
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resources available in the area. Many studies have reported positive associations between
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raccoon distribution and proximity to water in different seasons (Stuewer, 1943;
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Sanderson, 1987; Gehrt and Fritzell, 1998; Wilson and Nielsen, 2007). This can be related
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to behavioral characteristics of the raccoon, including preference of food that contains
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much water and washing the food in water. In Lavandevil Wildlife Refuge, sources of
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water are places with dense vegetation which can also attract the raccoons. In Gilan
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province scale, the second important macro variables are Topography Variables. The
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potential distribution map of the raccoon in Gilan province showed that lowland plains are
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of higher preference than mountainous regions which, although covered with dense
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deciduous forest, are less prone to invasion by the raccoon.
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There are some constraints for raccoon distribution in Gilan province, for example
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developing residential area and land use change that cause the species absent in potential
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distribution areas.
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Conclusion
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The study showed that the potential distribution of raccoons includes most of the Gilan
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province. We recognized new regions of the species occurrence in the Gilan province, as
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well as areas in invaded regions where no occurrence has been recorded but that are at risk
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according to the model which (fig 1), therefore, warrant close attention in the near future.
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A further outcome of identification of new invaded distributional areas for raccoons in the
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Gilan province is the possibility of considering new biocontrol agents. This possibility is
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especially promising in protected areas.
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21
Tables
Table 1. List of the micro variables (EGV) included
at Lavandevil Wildlife Refuge scale. FQ is
frequency and DIST distance variables.
Micro variable
code
Table 2. List of the macro variables used at Gilan province
scale.
Macro variables
code
Topography Variables
Natural variables
Vegetation density
V-D
Altitude
T-A
Punica plant community – FQ
V-PU
Slope
T-S
Alnus plant community - FQ
V-AL
Vegetation variables
Gleditsia plant community - FQ
V-GL
Normalized Difference Vegetation Index
V-N
Cynodon plant community - FQ
V-CY
vegetation type
V-T
Rubus plant community - FQ
V-RU
Landscape variables
Phragmites plant community - FQ
V-PH
Patch Perimeter
L- PERIM
Pterocarya plant community - FQ
V-PT
Patch Area
L- AREA
Areas without plant-FQ
V-W
Perimeter-Area Ratio
L- PARA
Water resource-FQ (Seasonal wetland)
WR
Related Circumscribing Circle
Zoning
ZON
Fractal Dimension Index
L- FRAC
Euclidean Nearest-Neighbor Distance
L- ENN
Human-related variables
L-CIRCLE
Road-FQ
H-RO
Climatic variables
Landfill-FQ
H-LA
Annual Mean Temperature
C-1
Aviculture-FQ
H-AV
Mean Diurnal Range
C-2
Agricultural land-DIST
H-AL
Temperature Annual Range
C-3
High way-DIST
H-HW
Temperature Seasonality(C of V)
C-4
Fencing areas-FQ
H-FA
Annual Precipitation
C-5
Village-DIST
H-VI
Precipitation Seasonality (C of V)
C-6
Game guard station
H
Landscape variables
Patch Area
L-AREA
Patch Perimeter
L-PERIM
Patch Density
L-PD
Perimeter-Area Ratio
L-PARA
Fractal Dimension Index
L-FRAC
Related Circumscribing Circle
Proximity Index
Interspersion and Juxtaposition Index
L-CIRCLE
L-PROX
L-IJI
All variables shown in tables 1 and 2 are the results of reduction steps.
Gilan province scale
Lavandevil Wildlife
Refuge scale
Table 3. ENFA evaluation statistics for different models.
Model
#F
ExS
ExI
k
Habitat suitability algorithm
Distance Harmonic
Distance geometric
Median
mean
mean
F
B ± SD
k
F
B ± SD
k
F
B ± SD
Natural
4
0.76
0.88
12
8
0.41 ± 0.36
16
10
0.45 ± 0.18
14
8
0.31 ± 0.29
Human-related
4
0.69
0.74
17
8
0.41 ± 0.33
16
6
0.58 ± 0.31
11
9
0.15 ± 0.19
Landscape
4
0.94
0.97
18
7
0.35 ± 0.34
12
9
0.41 ± 0.12
10
9
0.36 ± 0.16
Natural, Human-related
4
0.90
0.93
17
6
0.46 ± 0.40
12
10
0.47 ± 0.22
13
10
0.31 ± 0.23
Natural, landscape
5
0.84
0.90
17
11
0.32 ± 0.30
13
11
0.68 ± 0.30
13
10
0.29 ± 0.30
Landscape, Human-related
4
0.81
0.86
10
9
0.44 ± 0.38
13
5
0.58 ± 0.18
13
7
0.27 ± 0.24
All
5
0.94
0.97
10
10
0.74 ± 0.40
14
12
0.73 ± 0.13
13
11
0.45 ± 0.18
Topography
3
0.73
0.91
16
6
0.32 ± 0.30
11
9
0.48 ± 0.18
13
8
0.38 ± 0.18
Climatic
4
0.66
0.84
18
7
0.41 ± 0.23
13
9
0.48 ± 0.19
14
9
0.50 ± 0.17
Vegetation
4
0.83
0.81
24
7
0.42 ± 0.26
13
8
0.52 ± 0.17
1
9
0.28 ± 0.19
Landscape
4
0.92
0.77
21
8
0.41 ± 0.31
13
9
0.59 ± 0.20
14
9
0.33 ± 0.20
Topography, Climatic
4
0.77
0.76
23
8
0.39 ± 0.21
14
9
0.60 ± 0.17
13
10
0.38 ± 0.22
Topography and Vegetation
4
0.92
0.96
14
8
0.38 ± 0.20
13
9
0.64 ± 0.14
16
10
0.44 ± 0.24
Landscape and Topography
3
0.85
0.73
12
11
0.37 ± 0.16
13
8
0.61 ± 0.18
16
9
0.49 ± 0.19
Climatic and Vegetation
4
0.96
0.89
11
8
0.47 ± 0.22
12
9
0.51 ± 0.19
12
8
0.51 ± 0.28
Climatic and Landscape
5
0.88
0.78
11
8
0.44 ± 0.21
11
10
0.52 ± 0.20
18
8
0.37 ± 0.30
Vegetation, Landscape
5
0.90
0.84
11
8
0.43 ± 0.21
12
11
0.58 ± 0.17
12
8
0.45 ± 0.20
Vegetation, Climatic and Topography
4
0.73
0.88
12
8
0.46 ± 0.23
12
11
0.59 ± 0.18
25
9
0.42 ± 0.19
Climatic, Landscape and Topography
3
0.82
0.89
13
9
0.39 ± 0.29
10
10
0.56 ± 0.16
11
9
0.41 ± 0.15
Vegetation, Topography and Landscape
4
0.79
0.94
13
9
0.34 ± 0.21
10
12
0.60 ± 0.22
10
8
0.43 ± 0.19
Vegetation, Climatic, Landscape
4
0.86
0.71
12
9
0.29 ± 0.20
16
10
0.52 ± 0.27
12
9
0.39 ± 0.23
All
4
0.85
0.73
18
10
0.22 ± 0.20
14
12
0.55 ± 0.16
12
11
0.39 ± 0.15
#F = number of retained factors, ExS = Explained specialization, ExI = Explained information, B =
Continuous Boyce index, SD = Standard deviation, F = Max. of Boyce curve (= deviation from randomness),
k= number of k-fold).
23
Table 4. Scores of the micro variables on the first five axes of the Ecological Niche Factor Analysis (ENFA)
for the raccoon in Lavandevil Wildlife Refuge
Factor 1*
Factor 2
Factor 3
Factor 4
Factor 5
71.200 %
7.300 %
3.200 %
2.900 %
1.200 %
Specialization
Specialization
Specialization
Specialization
Specialization
V-D
0.349
0.007
0.022
0.026
-0.001
WR
0.308
0.118
-0.220
-0.022
-0.128
V-RU
0.303
-0.211
-0.085
-0.036
-0.050
V-PU
0.300
0.487
0.048
0.032
0.043
V-PT
0.277
-0.125
-0.083
-0.027
-0.046
H-VI
-0.276
0.685
-0.261
-0.101
-0.137
H-RO
-0.275
-0.059
-0.031
-0.016
0.027
H-HW
-0.274
-0.434
-0.082
0.077
0.217
L-AREA
0.236
-0.085
-0.102
0.259
-0.305
H
0.261
-0.091
0.024
0.008
-0.039
V-CY
0.217
-0.071
0.058
-0.020
0.020
V-AL
0.208
0.297
0.248
0.152
0.215
L-PERIM
0.180
0.105
0.210
-0.666
0.087
L-PROX
0.161
0.064
-0.500
0.206
-0.079
H-AV
0.065
-0.056
-0.077
-0.020
-0.024
ZON
0.064
-0.014
-0.003
0.001
0.012
L-PARA
0.059
-0.007
-0.041
0.069
-0.086
V-GL
0.050
-0.017
-0.009
-0.004
0.018
L-IJI
0.043
-0.078
-0.086
0.163
-0.193
L-CIRCLE
0.043
0.087
-0.157
-0.590
0.554
V-PH
0.040
-0.021
0.047
-0.095
-0.172
L-PD
0.038
-0.052
0.655
-0.465
0.395
H-AL
-0.038
0.067
0.055
0.039
0.117
H-FA
0.031
0.047
0.015
0.050
0.014
L-FRAC
0.029
-0.064
0.078
0.474
-0.442
H-LA
0.022
-0.006
0.029
0.053
0.061
V-W
-0.016
0.099
0.089
-0.068
0.067
Variable
* Factor 1 includes 100% Marginality
24
Table 5. Scores of the macro variables on the first four axes of the Ecological Niche Factor Analysis (ENFA)
for the raccoon in Gilan province.
Factor 1*
Factor 2
Factor 3
Factor 4
80.400 %
6.200 %
4.200 %
1.200 %
Specialization
Specialization
Specialization
Specialization
V-N
0.643
0.140
0.451
0.327
V-T
0.643
0.142
0.454
0.327
T-A
0.429
0.009
0.006
0.017
T-S
0.427
0.217
-0.249
-0.587
C-5
0.274
-0.321
-0.709
0.185
C-1
0.211
-0.001
0.001
0.001
L- AREA
0.201
-0.230
0.053
-0.202
L- PERIM
0.161
0.080
-0.087
-0.364
C-2
0.142
-0.135
0.082
0.068
L- FRAC
0.134
0.280
0.200
0.010
L-CIRCLE
0.077
0.807
-0.091
-0.073
C-6
0.060
0.300
0.320
0.080
L- ENN
0.047
-0.294
0.135
0.511
L- PARA
0.014
0.114
-0.417
-0.264
C-4
0.007
-0.035
0.120
0.021
0.440
0.080
0.010
Variable
C-3
0.001
* Factor 1 includes 100% Marginality
25