suitability of the vegetation types in mexico's tamaulipas state for the siting of hazardous...

Suitability of the vegetation types in Mexico’s Tamaulipas state

for the siting of hazardous waste treatment plants

Silke Cram a,*, Irene Sommer a, Luis-Miguel Morales a, Oralia Oropeza a,

Estela Carmona a, Francisco Gonzalez-Medrano b

a Institute of Geography, Universidad Nacional Autonoma de Mexico (UNAM), Aptdo. Postal 20-850, Mexico 01000, D.F., Mexicob Department of Botany, Institute of Biology, UNAM, Circuito de la Investigacion Cientıfica, C.U., Mexico 04510, Mexico

Received 17 September 2004; received in revised form 8 August 2005; accepted 10 August 2005

Available online 20 December 2005

Abstract

A land suitability study was carried out by applying a multiple-criteria technique to 12 different vegetation types in Mexico’s Tamaulipas state

to help select potentially suitable sites for hazardous waste treatment plants. Species richness, spatial distribution, and uniqueness were selected as

the criteria for estimating a vegetation type’s suitability. Using the analytical hierarchy process, we ranked and mapped vegetation types, then

compared the results with rankings of the same vegetation types based only on their number of endemic species. The suitabilities of the various

vegetation types were ordered in more or less the same way by both methods, except in two cases for which the results were very different. The

method proved to be a useful tool despite the availability of only partial (mostly qualitative) information; under such circumstances, expert

experience can be incorporated in the evaluation process to a limited degree. The technique described in this paper has a high potential to aid

decisions when many opinions and options must be considered simultaneously.

q 2005 Elsevier Ltd. All rights reserved.

Keywords: Multiple-criteria techniques; Land suitability; Ranking of vegetation types

1. Introduction

It is very important to accurately select locations for

hazardous waste-treatment plants because of their potential to

contaminate both air and water, with corresponding effects on

land use and public health (SEDESOL-INE, 1994; Randall

et al., 2004).

Techniques for land-use suitability analysis have evolved

rapidly during the last century, and especially computer-based

techniques assisted by Geographic Information Systems (GIS).

Collins et al. (2001) recognized two main research directions

during the 1990s: The first involves the evaluation of Boolean

logic to handle spatial boundaries in a GIS-based analysis,

combined with fuzzy-set theory to allow determination of

degrees of belonging, thereby making the inclusion of a land

0301-4797/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jenvman.2005.08.013

* Corresponding author. Tel.: C55 56 22 43 36; fax: C55 56 22 43 52/4.

E-mail addresses: [email protected] (S. Cram), irenes@igiris.

igeograf.unam.mx (I. Sommer), [email protected] (L.-

M. Morales), [email protected] (O. Oropeza), ecj@igiris.

igeograf.mx (E. Carmona), [email protected] (F. Gonzalez-

Medrano).

unit in various classes a more flexible (less binary) matter. The

second research direction is concerned with incorporating the

preferences of stakeholders in site selection. This idea gave rise

to multiple-criteria evaluations for analyzing multiple-objec-

tive decisions. In the 1970s Banai-Kashani proposed the use of

the Analytical Hierarchy Process (AHP) within a GIS

environment as a mathematical method for estimating the

value of a function based on various pairwise comparisons, and

subsequently using the function to rank the alternatives. This

method was further developed by Saaty (1980) as a scenario-

based approach, which nowadays is integrated in some GIS soft

wares such as IDRISI.

Due to the fact that judgments of this type generally require

specific standards of comparison that are seldom available,

Saaty (1987) suggested the use of ‘structural dependency’, in

which the relative weights of alternative sites are derived based

upon paired comparisons.

Considering this feature AHP has become a common tool in

land evaluation because of its advantages over other methods.

The AHP method has been intensively applied since its

creation, alone or in combination with other methods. Its

versatility allows a wide range of applications like land

suitability for different purposes (Aras et al., 2004; Basnet

et al., 2001; Dey, 2002; Hokey, 1994; Siddiqui et al., 1996;

Journal of Environmental Management 80 (2006) 13–24

www.elsevier.com/locate/jenvman

http://www.elsevier.com/locate/jnlabr/jenvman

Fig. 1. Map of Mexico’s Tamaulipas State, and location of the study area.

S. Cram et al. / Journal of Environmental Management 80 (2006) 13–2414

Tseng et al., 2001), natural resources management (Gamini,

2004; Lahdelma et al., 2000; Mardle et al., 2004; Mendoza

and Prabhu, 2000; Pavlikakis and Tsihrintzis, 2003; Zhu and

Dale, 2000;), ecological assessments (Bojorquez-Tapia et al.,

2002; Purnendu and Ritwik, 2003; Tran et al., 2002),

evaluation of different industrial products or processes in

view of their acceptance or their potential environmental

impacts (Chien-Chung and Hwong-Wen, 2004; Kazakidis

et al., 2004; Li and Hui, 2001; Morrisey and Browne, 2004;

Pineda-Henson et al., 2002; Sadiq et al., 2003; Solones,

2003; Yedla and Shrestha, 2003), public involvement in

environmental issues (Duke and Aull-Hyde, 2002; Kovacs

et al., 2004; Malczewsky and Moreno-Sanchez, 1997; Smith-

Korfmacher, 2001), and transparency in judging criteria

(Spires, 1991; Wang et al., 2005).

The aim of this paper is to test the applicability of the AHP

method to define the most suitable regions in Mexico’s

Tamaulipas state for the future siting of hazardous waste-

treatment plants with an emphasis on minimizing the

environmental costs. The specific objectives of our study

focused on evaluating the biological and ecological com-

ponents through a set of easy-to-obtain values of the 12

vegetation types that have been defined (SARH-COTECOCA,

1972, 1977) for Tamaulipas.

We chose Tamaulipas state to test this method because of its

recent industrial development, which consisted of the

construction of several ‘maquiladora’ industries, and because

of the availability of most of the information required to

support our analysis. Moreover, the state possesses a wide

variety of ecosystems that would let us test the suitability of

this method under different conditions.

The results of this phase were later integrated with those of

other work groups (geographic, social and economic aspects)

which are not presented here, to propose the regions most

suitable for the plants.

2. Study area

The state of Tamaulipas is located in northeastern Mexico

between 22812 031 00 N and 27840 052 00 N and between

97808 038 00 W and 100808 051 00 W. Its northern limits border

the US state of Texas (INEGI, 1983). The topography is

mainly characterized by low-elevation hills and plains that

are part of the Golfo de Mexico (Gulf of Mexico) Coastal

Plain. The highest elevations are found in the Sierra Madre

Oriental, the Sierra de San Carlos and the Sierra de

Tamaulipas, with altitudes up to 2000 m a.s.l. (Fig. 1). The

southern and southeastern parts of the state have a humid and

semi-humid climate (Garcıa, 1988), respectively, with a

summer rainfall season. The rest of the state has an arid or

semi-arid climate. Temperatures are warmer in the south than

in the north, and in the highlands, some small areas have a

temperate humid climate. Topography and climate are the

main features that influence vegetation diversity and

distribution in the state.

During the last 20 years, Tamaulipas has undergone

accelerated development, mainly through industrialization.

Manufacturing (maquiladoras) and oil refining are currently

the main industrial activities. Cattle ranching and agriculture

are also important in the southern and northern areas. The

infrastructure and services of the state are those needed by

industry and agriculture: power plants, dams, ports, and road

and railway networks. The rapid industrial development has

generated an increased use of hazardous materials, and their

ultimate disposal represents a serious threat to the environ-

ment. At present, there are no facilities within or near the

state that can adequately treat these wastes (Bowen et al.,

1995).

The 12 vegetation types defined for Tamaulipas state that

are considered in the present study (SARH-COTECOCA,

1972, 1977) were: Mountain cloud forest (BMM), Mixed

coniferous/oak forest (BPE), Sub-deciduous tropical forest

(SM), Deciduous tropical forest (SB), Chaparral (sclerophyl-

lous shrubland, Ch), Tamaulipan shrubland (xerophyllous

shrubland, Mtam), High sub-inerm shrubland (Mas),

Xerophyllous shrubland (MX), Mezquital (Mez), Climatic

grassland (natural grassland, Pcl), Perturbed forest/shrubland

(Secondary and perturbed vegetation, Sec), and Barren land

(Pel). A description for each vegetation type is provided in

Appendix A.

S. Cram et al. / Journal of Environmental Management 80 (2006) 13–24 15

3. Method

As described by Banai-Kashani (1989) the AHP has certain

advantages over other methods: it is a multiple-criteria

technique, it can be adapted to conditions with limited data,

it allows the incorporation of quantitative and qualitative

information, it takes into consideration the relationships

between criteria, it offers a way of calculating inconsistencies

during data processing (which arise due to the subjectivity

introduced by expert judgments), and it is based on a logical

and hierarchical scheme that defines analytical levels

composed of information with the same level of generalization.

The AHP also has certain shortcomings that have been reported

in the literature and compiled by Ramanathan (2001).

The application of the AHP method to land suitability has

been characterized by the common objective of identifying the

most ecologically valuable natural areas so that planning and

management practices can be applied so as to maintain the

values of those areas.

The latest tendencies in natural resources management are

focused on efficient use with the minimum ecological cost,

affording better human life quality but preventing impairment

of cycling of energy and nutrients (ecosystem health) (Rapport

et al., 1998; Patil et al., 2001; Brooks et al., 1998). Some of the

variables that have been proposed to evaluate ecosystems’

quality are presented in Table 1.

Table 1

Some ecosystem-level quality variables proposed by some international institutions

Institution Purpose Proposed variables/crite

World Wild Life

(2005)

Variables to define most

valuable ecoregions of the

world

Species richness

Endemisms

Higher taxonomic uniq

Extraordinary ecologica

phenomena

Global rarity of the ma

World Bank (1977) Universal indicators to assess

status and trends of biodiver-

sity

Ecosystem quantity

Ecosystem quality

Relative number of thre

species

USEPA (2002) Essential ecological attributes Landscape condition

Biotic condition

Chemical and Physical

Ecological processes

Hydrology and geomor

Natural disturbance reg

In real conditions, for the selection of variables the

following characteristics have to be considered: their signifi-

cance in the context of the study, the working scale, the

fulfillment of minimum standards of reliability and their easy

access. Taking this into account, the following variables were

used to define the actual vegetation types

† richness: the number of species in each vegetation type

† spatial distribution: the patchiness or disconnection of each

vegetation type, and its representativeness at local, regional,

and national levels

† uniqueness: the uniqueness of a vegetation type (i.e. one

that cannot be easily found in other places or under different

conditions).

Considering our study area size we used a regional scale and

preferred ecosystem-level variables over ecosystem processes.

We compiled the required information out of sources of

varied origin and type (SEDESOL-Gobierno del Estado de

Tamaulipas, 1994a,b, 1995; SEDUE, 1989; SARH-COTE-

COCA, 1972, 1977; SARH-Instituto de Geografıa, 1995;

Flores and Gerez, 1994; Hernandez et al., 1991; Rzedowski,

1978; Jahrsdoerfer and Leslie, 1988; Johnston, 1963; Gonza-

lez-Medrano, 1993 and personal communication).

SEDESOL-INE (1994) stated that certain minimum

requirements for design as well as rules-based criteria ensure

that disposal or treatment sites cannot be located in

ria Specific variables

None

ueness

l or evolutionary

yor habitat type

For ecosystem quality:

Species abundance and/or distribution (evenness)

atened and extinct Species richness

Ecosystem structure and complexity

Biotic condition

Forests:

characteristics At risk native forest species

Populations of representative forest species

phology Forest disturbance: fire, insects, and disease

imes Tree condition

Ozone injury to trees

Grassland and Shrublands:

At risk native grassland and shrubland species.

Population trends in invasive and native non-invasive

bird species

National level(USA):

At risk native species

Bird community index

Terrestrial plant growth index


environmentally sensitive areas, including wetlands, 100-year

flood-plains (i.e. any area that is predicted to be flooded at least

once during a century), permafrost areas, habitats of

endangered species, and recharge zones for aquifers. Accord-

ing to this, some vegetation types were excluded from the

beginning because they are strongly associated with land types

that are permanently or commonly submerged (i.e. wetlands),

that are highly unstable (i.e. dunes) or previously assigned to

incompatible land uses (i.e. urban, agricultural). We also

Fig. 2. Distribution of the main vegetatio

excluded those lands declared or projected as protected areas

by both federal and state authorities. The distribution of the

main vegetation types is shown in Fig. 2.

Once appropriate variables have been selected, they must be

translated into criteria, which are categories with well-defined

limits (i.e. the highest and lowest permissible values) that allow

alternative sites to be judged (accepted or rejected) according

to the objectives that are being pursued. We established that no

vegetation type would be 100% suitable for this use, so we

n types in Tamaulipas State, Mexico.


canceled this possibility; and because the final score for the 12

vegetation types would be expressed as a percentage, a value of

8.3% (100/12) would be assigned to each vegetation type if

they were all equally suitable. Our assessment used the

following criteria: ‘suitable with restrictions’ if the final

score was less than 8.3%, and ‘unsuitable’ if the final score

was greater than 8.3%.

We applied the AHP as follows (Ramanathan, 2001):

(a) We constructed the hierarchical model: The first level

relates to the focus of the problem (i.e. the best region for

a hazardous waste treatment facility). The intermediate

level corresponds to criteria (the three criteria: richness,

spatial distribution, uniqueness). The lowest level con-

tains the decision alternatives (12 vegetation types)

(Fig. 3).

(b) We performed pairwise comparisons and obtained a

judgment matrix: The elements of each particular level

were compared in pairs with respect to a specific element

in the level immediately above their level; for example,

two vegetation types were compared with respect to their

uniqueness. We then built a matrix for these elements, and

calculated relative weights for each element. The relative

weight (importance) of each element is expressed as a

percentage (using a common scale from 0 to 100% for all

elements) based on a ‘quantified judgment’; we used the

Fig. 3. The analytical levels used in the present

9-point scale to transform verbal judgments into numeri-

cal quantities suggested by Banai-Kashani (1989). An

example of the matrix is given in Table 2. We obtained the

following final ranking based on the relative weight

values for each criteria: richness (48%), uniqueness

(41%), distribution (11%).

(c) Before arriving at these final values, we evaluated the

consistency of the comparisons: The subjectivity of a

judgment matrix can be determined by means of a

measure called the ‘consistency ratio’. Saaty (1980) has

provided average consistencies for randomly generated

matrices that can serve as a basis for comparison; and

stated that a consistency ratio of 0.10 (10%) or less was

acceptable. If a higher value was obtained, the matrix had

to be verified by assigning new values to the elements and

starting over from the beginning. We obtained a

consistency ratio of 4.3% for our ranking of criteria,

which is less than the 10% that Saaty proposed as the

critical limit. Nevertheless, a certain degree of incon-

sistency is considered important because it expresses the

level of actual knowledge, which can progressively

evolve into a state with less inconsistency.

(d) We evaluated alternative sites (vegetation types) as a

function of the ranked criteria. We used the same

procedure used to rank richness, spatial distribution, and

study, according to the AHP methodology.

Table 2

A sample criteria matrix including subjective judgments with some inconsistency

Richness Uniqueness Spatial distribution Richness Uniqueness Spatial distribution Criteria weight(%)

Richness 1 1 5 5/11 3/7 5/9 48.06

Uniqueness 1 1 3a 5/11 3/7 3/9 40.54

Spatial distribution 1/5 1/3 1 1/11 1/7 1/9 11.40

Totals 11/5 7/3 9 100

To obtain the criteria weights: Add the values per column. Divide each column value by their corresponding total. Calculate the mean value for each line.a To be consistent this value should be 5.


uniqueness, so information about the features of each site

could be used to assign values according to the AHP scale.

Next, we performed paired comparisons and generated a

matrix for each criterion. For these matrices, consistency

ratios were also calculated. Criteria must be applied at this

level to classify the vegetation types as ‘suitable with

restrictions’ or ‘unsuitable’.

(e) We ranked the options for the vegetation types: To

determine overall scores, we multiplied the weights of

each of the criteria corresponding to each vegetation type

by the overall weight for each of the three criteria, then

summed the results (Table 3). A ranking of all vegetation

types was obtained thereby.

We also produced a single synchronous map for each

criterion. The vegetation types were grouped using the method

ArcGIS, 8.3 software (ESRI, 380 New York Street Redlands,

CA, USA). A map of the suitability of each vegetation type of

the state for the specified use (siting waste-treatment plants)

appears in Fig. 4.

Due to the lack of results from previous studies or other

objective standards of comparison to further validate our

Table 3

Determination of the overall scores

Vegetation type Richness

(weight)

Uniqueness

(weight)

Distribution

(weight)

Overall

scoresa

Mountain cloud

forest

0.0879 0.0848 0.1152 0.0898

Mixed coniferous/

oak forest

0.1879 0.0380 0.0282 0.1089

Sub-deciduous tro-

pical forest

0.1879 0.0380 0.2268 0.1315

Deciduous tropical

forest

0.1879 0.0380 0.2268 0.1316

Chaparral 0.0402 0.0848 0.0597 0.0605

Tamaulipan shrub-

land

0.0879 0.2947 0.1152 0.1748

High sub-inerm

shrubland

0.0879 0.0380 0.1152 0.0708

Mezquital 0.0200 0.0174 0.0282 0.0199

Climatic grassland 0.0200 0.1657 0.0282 0.0800

Xerophyllous

shrubland

0.0402 0.1657 0.0282 0.0897

Perturbed forest/

shrubland

0.0402 0.0174 0.0142 0.0280

Barren land 0.0120 0.0174 0.0142 0.0144

a To calculate overall scores: Multiply the criteria value obtained for each

individual vegetation type by its correspondent criteria weight obtained in

Table 3; add this 3 values to obtain the overall score.

method, we performed an additional exercise to test the

adequacy of the AHP method in comparison with another

broadly accepted method that we chose as our standard. This

second method is based on the assumption that vegetation types

with a high number of endemic species have a high biological

value (Worldbank, 2005). Based on this single value (diversity

of endemic species), we constructed a priority sequence that we

could use as a basis for comparison against which to test the

results obtained by means of the AHP method.

4. Results and discussion

According to the suitability map (Fig. 4), most of the

cartographical units based on vegetation type are unsuitable

locations for a hazardous waste-treatment plant. Some barren

lands located on the coast have high apparent suitability

according to the biological criteria alone, but are not suitable at

all because of their location. Other criteria (e.g. geographical

features) must also be taken into account before making a final

decision.

We ranked the vegetation types in terms of their

suitability, from least suitable (1) to most suitable (12).

Based on our method, we judged six vegetation types to be

unsuitable sites for waste-treatment plants: (1) Tamaulipan

shrubland, (2) Deciduous tropical forest, (3) Sub-deciduous

tropical forest, (4) Mixed coniferous/oak forest, (5)

Mountain cloud forest and (6) Xerophyllous shrubland.

These vegetation types deserve to be protected due to their

richness, their spatial distribution, their uniqueness, or some

combination of these three criteria.

We judged six vegetation types to be suitable, but with

restrictions: (7) Climatic grassland (8) High sub-inerm shrub-

land, (9) Chaparral, (10) Perturbed forest/shrubland, (11)

Mezquital, and (12) Barren land. Based on the difference

between the lowest suitability scores, this category could be

further subdivided into two more categories: a group that

includes Perturbed forest/shrubland, Mezquital, and Barren

land should be considered first for the siting of hazardous

waste-treatment plants; Climatic grassland, High sub-inerm

shrubland and Chaparral are also potentially suitable, but are

less desirable sites. Table 4 ranks the vegetation types and the

corresponding decisions. The botanical expert we consulted

considered these results acceptable from a biological

standpoint.

We compiled a list of the number of reported endemic

species for each vegetation type in our study, with these species

Fig. 4. Map of the suitability of different regions of Tamaulipas State to the siting of hazardous wastes treatment plants based on the vegetation types shown in Fig. 2.


categorized at three levels of generalization: national (endemic

to Mexico), regional (endemic to northeastern Mexico and the

southeastern USA), or local (endemic to Tamaulipas State).

We considered the latter category most important because it

contained species with the most limited distribution, which

were thus most vulnerable to environmental disruptions created

by hazardous waste-treatment plants. We obtained the

following results:

† Mountain cloud forest, 22 endemic species.

† Chaparral, 12 endemic species.

† Mixed coniferous-oak forest, 9 endemic species.

† Deciduous tropical forest, 6 endemic species.

† Sub-deciduous tropical forest, 4 endemic species.

† Tamaulipan shrubland, 4 endemic species.

† High sub-inerm shrubland, 4 endemic species.

† Mezquital, Climatic grassland, Perturbed forest/shrubland,

and Barren land; no endemic species reported.

† Xerophyllous shrubland could not be tested because of a

lack of information for this vegetation type. (See Appendix

B.)

We arbitrarily chose a cutoff value of 8 endemic species

such that any vegetation type with more than this number of

endemic species was judged unsuitable for siting hazardous

waste-treatment plants.

In comparing these results with those obtained by means of

the AHP method, we noted two main differences. First, the

Tamaulipan shrubland that was considered to have the highest

priority for protection based on the AHP method was the least

important based on the single-criterion ranking based on the

diversity of endemic species. Second, chaparral, which was

Table 4

Ranked vegetation types in terms of their priority (i.e. the importance of

protecting the vegetation type by not siting a waste-treatment plant on sites with

that vegetation type)

Site

priority

Vegetation type Weight

(%)

Decision

1 Tamaulipan shrubland 17.48 Unsuitable (excluded)

2. and 3 Deciduous tropical forest 13.16 Unsuitable (excluded)

Sub-deciduous tropical

forest

4 Mixed conifer/oak forest 10.9 Unsuitable (excluded)

5. and 6 Mountain cloud forest 9 Unsuitable (excluded)

Xerophyllous shrubland

7 Climatic grassland 8 Suitable with restrictions

8 High sub-inerm shrub-

land

7.1 Suitable with restrictions

9 Chaparral 6.1 Suitable with restrictions

10 Perturbed forest/shrub-

land

2.8 Suitable with restrictions

11 Mezquital 1.99 Suitable with restrictions

12 Barren land 1.44 Suitable with restrictions


judged suitable with restrictions based on the AHP method,

was considered to have a high biological value based on the

single-criterion method.

The categories of the other vegetation types (suitable with

restrictions, unsuitable) were judged to be roughly the same in

both methods, even though the order differed slightly. The

AHP method provided a finer grouping (overall weights are

specific for almost each vegetation type) than the simpler

method based exclusively on the number of endemic species.

This comparison revealed that some fragile or threatened

ecosystems that deserve protection, such as the Tamaulipan

shrubland, could be erroneously ranked as one of the least

important vegetation types if evaluated by means of a single

factor. However, it must be noted that widespread ecosystems

such as the Chaparral vegetation type have probably been

studied in more depth than less common ecosystems, and thus,

the number of endemic species in such ecosystems receives a

disproportionately high weight; less common ecosystems may

actually have more species that have simply not yet been

reported.

In view of these considerations, the multiple-criteria method

offers important advantages compared with a conventional

single-criterion method; the latter appears to be objective, but

actually leads to a serious risk of underestimating an

ecosystem’s importance and thereby exposing valuable

ecosystems to the risk of damage.

The multiple-criteria method was not particularly demand-

ing in terms of the quantity or quality of the required

information, and as was the case in our study, individual

preferences or expert opinion (which is generally difficult to

quantify) can be considered in a controlled way.

Finally, this method can also be conceived as a series of

approximations based on the application of successive

screens; for example, if the biological and ecological

aspects of an area were judged to be the highest priority,

all of the land judged as unsuitable through this first

screening could be set aside so that the time, information,

and effort required to screen the remaining potentially

suitable areas could be greatly reduced. The biological and

ecological aspects would thus play a predominant role, but

other criteria could still be applied.

Thus, the method described in this study allows researchers

to develop a series of alternative scenarios that give different

weights to the different aspects that are considered to be

important components of a complete evaluation.

5. Conclusions

The method proposed in this paper proved to be a useful tool

in situations where only partial and mostly qualitative

information was available. The results obtained agreed with

the opinions of the expert we consulted during the study, and

the subjectivity involved during the process could be measured

and was judged to be within acceptable limits. This method is

thus considered potentially appropriate for application on a

national scale, and also allows the incorporation of information

concerning other factors such as geographical, social, or

economic aspects.

When we compared the AHP method with a more

conventional approach based on a single criterion (number of

endemic species), the Tamaulipan shrubland was ranked first

and last, respectively, in terms of its priority for protection. The

Chaparral, with a high number of endemic species, was

considered to be suitable, but with restrictions, based on the

AHP method. These different results can be explained in part

by the fact that some vegetation types have received more

attention than others and may therefore be considered to

contain a larger number of reported endemic species than

vegetation types that have been studied less. Another important

point is that the spatial distribution and uniqueness of each

vegetation type are not necessarily represented by the number

of endemic species alone, which can make the single-criterion

method more susceptible to bias.

Based on the results of our study, the AHP method has

considerable potential as a screening tool that is capable of

lowering costs and time requirements, while at the same time

providing a less-biased means of testing different scenarios.

Acknowledgements

We thank M. Gutierrez (UNAM), coordinator of the overall

project to which this study contributed, P. Fernandez-Lomelın

for her contributions and the staff of the ecological

administration of Tamaulipas State. Also to J.C. Preciado-

Lopez for his support in GIS processing maps

Appendix A. Description of the Tamaulipas’ vegetation

types

A.1. Mountain cloud forest (BMM)

This forest is considered a transitional type of vegetation

between oak forest and tropical forest. It has a high species


richness; a characteristic species is Liquidambar styraciflua,

together with species in the Quercus, Magnolia, Cornus,

and Styrax genera. Some of its species, such as Fagus

mexicana (Flores and Gerez, 1994), are considered as relict.

Crataegus uniflora is endemic to Tamaulipas, and other

species such as Magnolia tamaulipana, Acer grandidenta-

tum, and Beschorneria septentrionalis are endemic to the

northern part of Mexico. On a national scale, its distribution

is restricted to a few localities, mainly to mountain slopes

with high humidity. This type of forest covers only 1% of

the national area but hosts almost 10% of the Mexican flora

(Rzedowski, 1978). Tamaulipas represents the northern limit

of this vegetation type’s national distribution. The mountain

cloud forest is the habitat of 69 endemic species of

Mesoamerican fauna, two of which are restricted to

Tamaulipas (Flores and Gerez, 1994).

A.2. Mixed coniferous/oak forest (BPE)

This type of vegetation includes pine forest, oak forest,

and pine-oak (Pinus spp.—Quercus spp.) forest. It has a

high species richness due to its patchy distribution. In

Mexico, it hosts a high percentage of the endemic species

(25%) and is home to a large number of associated

vertebrates (127 vertebrate species are endemic to Mesoa-

merica, and 5 of them are restricted to Tamaulipas) (Flores

and Gerez, 1994).

A.3. Sub-deciduous tropical forest (SM)

Tropical forests reach their northern limit (along the

Atlantic coast) in Tamaulipas state. Sub-deciduous tropical

forests are considered one of the richest and more complex

plant communities (typical species include Brosimum

alicastrum, Mirandaceltis monoica, Wimmeria concolor,

Bursera simaruba, Archras zapota, Lysiloma sp., Ficus

spp., and Enterolobium cyclocarpum). They form an

important refuge for plants and animals of Neotropical

origin. This forest is organized into three spatial layers

characterized by the presence of climbing species, and 103

endemic species of Mesoamerican fauna are associated with

this type of vegetation (Flores and Gerez, 1994).

A.4. Deciduous tropical forest (SB)

The principal characteristic of this forest is the fall of

leaves during 5 to 8 months of the year. It has a high

species richness. Two types of this vegetation have been

reported (Rzedowski, 1978) for the Sierra de Tamaulipas,

one dominated by Bursera simaruba and Lysiloma divar-

icata and the other by Phoebe tampicensis and Pithecello-

bium ebano, with some Esenbeckia runyonii. Like the sub-

deciduous tropical forest, it also forms an important refuge

for plants and animals of Neotropical origin. Deciduous

tropical forests are the habitat of six endemic species of

Mexican feline (Flores and Gerez, 1994).

A.5. Chaparral (sclerophyllous shrubland) (Ch)

This vegetation type is a dense community of small oaks

and other species such as Arctostaphylos spp. Species

richness is not particularly high, but a great number of relict

species are included. Its distribution is quite restricted on a

national level and therefore its situation in the study area

should be considered critical (Rzedowski, 1978).

A.6. Tamaulipan shrubland (xerophyllous shrubland) (Mtam)

The species richness of this vegetation type is classified

(Jahrsdoerfer and Leslie, 1988) as moderate to high (typical

species include Prosopis grandulosa, Celtis pallida, Randia

aculeata, Bumelia angustifolia, Castela texana, and Con-

dalia obovata). It is a unique vegetation type because its

distribution is restricted to the northeastern part of Mexico

and the southeastern part of the USA. It is a transitional

type of vegetation (between desertic shrubland and sub-

mountain shrubland), and hosts 54 endangered species and

127 others reported (Jahrsdoerfer and Leslie, 1988) in a

watch-list for the northern border. This shrubland has

undergone intensive changes in land use, with only 5% of

its original distribution remaining in the USA (Jahrsdoerfer

and Leslie, 1988). Consequently, it presents a patchy

distribution.

A.7. High sub-inerm shrubland (Mas)

This vegetation type includes sub-mountain and moun-

tain-foot shrubland, it has the highest species richness

among shrublands (Gonzalez-Medrano, 1993) (typical

species include Helietta parvifolia, Acacia rigidula, Leuco-

phyllum frutescens, Acacia berlandieri, Cordia boissieri,

and Viguieria stenoloba). Extensive cattle ranching has

significantly decreased the total area once occupied by this

vegetation type.

A.8. Xerophyllous shrubland (MX)

This vegetation type is represented mostly by cactaceous

plants and others with a slow growth rate, long life cycle,

and long reproductive periods (with reproduction by means

of seeds). It is therefore considered very vulnerable to

disturbance. Many endangered species are found in this type

of shrubland, and it should consequently be protected

(Gonzalez-Medrano, 1993).

A.9. Mezquital (Mez)

This vegetation type is a special kind of xerophyllous

shrubland. Its species richness ranges from medium to low

(typical species include Prosopis grandulosa, Condalia

obovata, Lycium berlandieri, Acacia rigidula, and Leuco-

phyllum frutescens). It has undergone a rapid change in land

use due to agricultural development, therefore its


distribution has become restricted and fragmented (Gonza-

lez-Medrano, 1993).

A.10. Climatic grassland (Pcl)

Species richness ranges from medium to low (typical species

include Calliandra conferta, Krameria ramosissima, Cassia

greggii, and a wide variety of grasses). Some species are

reported as endangered (e.g. Asclepias prostata and Boerhavia

mathisiana) and some native species are considered to have a

high genetic potential (e.g. Manihot walkerae, a very rare

species of very restricted distribution) (Gonzalez-Medrano,

1993). This type of vegetation has been severely impacted by

grazing, and is now restricted to small patches.

A.11. Perturbed forest/shrubland (Sec)

Species richness can be high in some cases. This vegetation

type has no singular or relevant features apart from its origins in

a disturbance of some kind. Its distribution is thus widespread

due to cattle ranching and the expansion of sorghum (Sorghum

bicolor) production in Tamaulipas.

Barren land (Pel). Natural desert zones and eroded areas are

included in this category.

Appendix B

(APP) Endemic species reported in the Tamaulipas’ vegetation types

Endemic species

Vegetation types (# of

endemic species)

National level Regional level Local level

Mountain cloud forest

(22)

Acer skutchii, Cornus

excelsa, Ilex discolor, Taxus

globosa, Turpinia

occidentalis

Carya myristicaeformis,Clethra pringlei,

Zanthoxylum pringlei

Acalypha tamaulipensis, Abutilon procerum,

Comarostaphylis sharpii, Eupatorium richardsonii,

Louteridium tamaulipense, Omphalodes richardsonii,

Phyllanthus barbarae, Senecio richardsonii, Verbe-

sina richardsonii, Gochnatia magna, Macromeria

alba, Ceratozamia zaragozae, Magnolia tamauli-

pana, Beschorneria septentrionalis

Mixed coniferous/oak forest

(9) Bahuinia coulteri, Berberis gracilis, B.

hartwegii, Carya ovata, Casimiroa prin-

glei, Clethra pringlei, Colubrina greggii,

Pinus estevezii, Quercus rysophylla

Sub-deciduous tropical

forest (4)

Exostema mexicana Chione mexicana, Robinsonella discolor,

Savia sessiliflora

Deciduous tropical for-

est (6)

Acacia coulteri Casimiroa pringlei, Nectandra salicifo-

lia, Robinsonella discolor, Savia sessili-

flora, Thouinia villosa

Chaparral (12) Exostema coulteri, Eysen-

hardtia polystachia, Mimosa

leucaenoides, Yucca

carnerosana

Bahuinia coulteri, Berberis gracilis,

Casimiroa pringlei, Cercocarpus fother-

gilloides, Columbrina greggii, Dasylirion

miquihuanensis, D. quadrangulatum,

Rhus pachyrrachis

Tamaulipan shrubland

(4)

Schaefferia cuneifolia, Manihot walk-

erae, Frankenia johnstonii, Dyssodia

tephroleuca

High sub-inerm shrub-

land (4)

Acacia micrantha, Mimosa

leucaenoides

Colubrina greggii, Rhus virens

(Sources: Gonzalez-Medrano, 1993; Jahrsdoerfer and Leslie, 1988). Mezquital, Climatic grassland, perturbed forest/shrubland and barren land. No endemic species

reported. Xerophyllous shrubland could not be tested because of a lack of information.

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