Associate Professor of Geography .... of reserve networks where a system of protected areas is linked by corridors to ...... Sinauer Associates, Inc. Sunderland.
LINKING SPATIAL DATA WITH POPULATION VIABILITY ANALYSIS: RESERVE NETWORK DESIGN IN THE NORTHEASTERN ECUADORIAN AMAZON.
A thesis presented to the faculty of the College of Arts and Sciences of Ohio University
In partial fulfillment of the requirements for the degree Master of Science
Galo Zapata-Ríos August 2001
LINKING SPATIAL DATA WITH POPULATION VIABILITY ANALYSIS: RESERVE NETWORK DESIGN IN THE NORTHEASTERN ECUADORIAN AMAZON.
BY GALO ZAPATA-RÍOS
The thesis has been approved for the Program of Environmental Studies and the College of Arts and Sciences
______________________ James Dyer Associate Professor of Geography
______________________ Leslie A. Flemming Dean, College of Arts and Science
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ACKNOWLEDGMENTS
Financial support for this thesis was provided in part by the Fulbright Amazon Basin Program, and the Environmental Studies Program. This work could not have been accomplished without the help of Wini Schmidt and Humbertus Peters of the European Union’s Petramaz Project (ECU/B7-3010/94/130) who provided the Landsat-5 images and cartographic information of the protected areas. The Landsat-5 image analysis was carried out at the Department of Geography of Ohio University. Rodrigo Sierra of Arizona State University kindly provided copies of his unpublished manuscripts prior to publication. Comments from Luis Suárez of EcoCiencia and Jeffrey P. Jorgenson of the Wildlife Conservation Society improved early versions of the thesis proposal. I thank the members of my committee Donald Miles and James Lein for their constructive comments and advice. I also thank James Dyer, my thesis advisor, for his advice and helpful discussions throughout the course of this project. A special debt of gratitude is owed to Gene Mapes, Director of the Environmental Studies Program, for her support during my two years at Ohio University. Finally, I am grateful to María Claudia for her support.
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TABLE OF CONTENTS
Page Acknowledgments
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List of Tables
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List of Figures
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Chapter I. Introduction
1
Chapter II. Literature Review
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Fragmentation Metapopulations Stochasticity and Extinction Vortices Population Viability Analysis Chapter III. Methods Study Area Geographical Data Sources Analysis of Spatial Information Biological Data Sources The PVA Model
6 8 10 13 18 18 21 22 25 25
Chapter IV. Results
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Chapter V. Discussion
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Management Considerations Final Considerations Literature Cited
39 42 44
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LIST OF TABLES
Table
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1. Error matrix of the land-use / land-cover classification
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2. Biological and ecological attributes of the target species based on data from the literature
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3. PVA parameters based on the image analysis
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4. Results of the Population Viability Analysis
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5. Results of PVA stochastic simulations using different population sizes 63 6. Results of PVA stochastic simulations using different sex ratios
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7. Results of PVA stochastic simulations using different life span data
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LIST OF FIGURES
Figures
Page
1. Map of Ecuador
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2. Map of the protected areas
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3. Image analysis procedure and cartographic modeling
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4. Disturbed areas and types of human disturbance in the study area
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5. Anteater trends under stochastic and deterministic factors of extinction 70 6. Howler monkey trends under stochastic and deterministic factors of extinction
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7. Spider monkey trends under stochastic and deterministic factors of extinction
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8. Jaguar trends under stochastic and deterministic factors of extinction
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9. Tapir trends under stochastic and deterministic factors of extinction
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10. Anteater population trends under current rates of deforestation
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11. Howler monkey population trends under current rates of deforestation 76 12. Spider monkey population trends under current rates of deforestation 77 13. Jaguar population trends under current rates of deforestation
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14. Tapir population trends under current rates of deforestation
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15. ANOVA results
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16. Proposed Cuyabeno-Yasuní reserve network
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1 CHAPTER I INTRODUCTION "Our task must be to free ourselves by widening our circle of compassion to embrace all living creatures and the whole of nature in its beauty" Albert Einstein
Forest clearance and conversion are root causes of the current global biodiversity crisis, yet surprisingly scientific understanding of the link between deforestation and species extinction is very poor (Terborgh, 1992; Soulé & Sanjayan, 1998). Ecosystems and communities are being degraded and destroyed, and species are being driven to extinction. The species that persist are losing genetic variability as the number of individuals in populations shrinks, unique populations and subspecies are destroyed, and remaining populations become increasingly isolated from one another (Lande, 1998). Because an endangered species may consist of just a few populations, or even a single population, protecting populations in functioning ecosystems is the key to preserving species. As a result, many protected areas have been created to protect species; however, designating the habitats in which these species live as protected areas may not be enough to stop their decline and extinction. In the context of growing human pressures and development needs, protected areas cannot maintain wildlife populations within their borders in the long-term (McNeely et al., 1987; Redford & Robinson, 1991; Peres & Terborgh, 1995;
2 Bruner et al., 2001). If protected areas are failing despite current conservation efforts, better options should be sought. A step toward a better performance of current protected areas and their surrounding landscapes is the design and creation of reserve networks where a system of protected areas is linked by corridors to increase the conservation potential of individual reserves (Noss & Cooperrider, 1994). The probability of survival of wildlife populations in fragmented habitats and inside protected areas increases if their numbers are maintained above a certain size. This concept is termed the minimum viable population (Shafer, 1981). A minimum viable population (MVP) is the smallest isolated population having a 99% chance of surviving for 100 years despite the foreseeable effects of demographic, environmental, and genetic stochasticity, and natural catastrophes (Shaffer, 1981, 1987; Shaffer & Sampson, 1985; Lacy, 2000a). This concept allows a quantitative estimate to be made of how large a population must be to assure its long-term survival. Another concept related to MVP is the minimum dynamic area (MDA), which is the area of suitable habitat necessary for maintaining the minimum viable population or the smallest area that contains patches unaffected by the largest expected disturbances (Schwartz, 1999). Data availability from field research currently is fragmentary and insufficient even to document the process of declining wildlife populations. Therefore, species-specific approaches to counteract the current trends in species
3 loss are highly unlikely to be successful in the short-term. Instead, landscape approaches are more likely to be successful and cost effective. This approach requires utilization of spatial information on habitat suitability. These requirements can be met by linking single-species population ecology modeling with landscape data using satellite remote sensing and geographic information systems (Dale et al., 1994; Scott & Csutti, 1997; Cardillo et al., 1999; Chiarello, 1999; Lindemayer et al., 1999; Cuarón, 2000; Jennings, 2000). This thesis presents a population viability analysis (PVA) in two protected areas of the northeastern Ecuadorian Amazon. The PVA links spatial data for extinction risk assessment, viability analysis, reserve design, and wildlife management. Because there are no methods to determine the minimum areas of reserves with reference only to ecosystem properties (Beier, 1993), conservationists conduct PVA for a few indicator species as an efficient way to address the viability of the whole system. Five large mammal species of the western Amazon were chosen as indicator species: giant anteater (Myrmecophaga tridactyla), white-bellied spider monkey (Ateles belzebuth), red howler monkey (Alouatta seniculus), jaguar (Panthera onca), and Brazilian tapir (Tapirus terrestris). Large mammals were chosen as indicator species because they are strongly and negatively affected by deforestation and fragmentation, have large individual area requirements, are likely to occur at low population sizes, and are sensitive to extinction due to human activities in natural landscapes (Soulé & Simberloff, 1986;
4 Terborgh, 1988, 1992; Leader-Williams & Dublin, 2000). In addition, they are considered “umbrella” species. Umbrella species require large areas of relatively undisturbed habitat, so that a protected population of these species can encompass the habitat requirements of many other species of the same community and ensure also their protection (Beier, 1993; Caro & O’Doherty, 1999; Leader-Williams & Dublin, 2000). The PVA was carried out, using the VORTEX computer simulation model (Lacy, 1993, 2000b; Miller & Lacy, 1999) to estimate the long-term viability of the indicator species in two protected areas, Cuyabeno Fauna Production Reserve and Yasuní National Park, in northeastern Ecuador. This study includes long-term simulations of single population dynamics as well as hypothetical metapopulations dynamics occurring in these protected areas. A metapopulation structure assumes that species are distributed over several interacting populations allowing dispersal and gene flow among them (Merriam, 1995; Hanski, 1999). The objective of the simulations was to determine if the protected areas individually are protecting the indicator species populations in the long-term. The specific objectives of the study included: a) classify land cover inside the protected areas and in the surrounding areas, b) estimate the area of contiguous rain forest in existence or the area of suitable habitat for wildlife populations inside both protected areas, c) calculate a theoretical population size of the five mammal species for each one of the protected areas, d) determine if the indicator populations
5 are currently minimally viable, e) determine if the protected areas are minimum dynamic areas, f) identify the main factors of population decline, and g) design a reserve network joining both protected areas.
6 CHAPTER II LITERATURE REVIEW
Fragmentation Loss of habitat is probably the most important cause of species extinction in recent times. Habitat loss often results not only in an overall decrease in the amount of habitat, but also in discontinuities in the distribution of the remaining habitat. The result is the fragmentation of the original habitat. Fragmentation is a process by which a natural and contiguous habitat is broken up into small parcels of natural ecosystems, isolated from one another in a matrix of lands dominated by human activities (Lord & Norton, 1990). This manipulation of the environment has consequences for biodiversity at both the landscape and the forest-fragment level. Factors such as fragment size, degree of isolation and time since excision from the continuous forest may directly influence the biodiversity of a fragment and hence, in a complex manner, the biodiversity of the collection of fragments that occupies the landscape (Turner, 1996). Fragmentation has become a major issue in conservation biology for two reasons: rates of habitat loss have increased throughout the world, especially in the tropics, and because many protected areas have become isolated fragments or are in the process of becoming so (Saunders et al., 1991).
7 During the early days of conservation biology, it appeared that fragmentation could be fully understood using the models of the theory of island biogeography (MacArthur & Wilson, 1967). Currently, it is recognized that the applicability of the theory is somewhat limited, mainly because terrestrial islands (forest fragments) are not as isolated as true oceanic islands (Zimmerman & Bierregaard, 1986; Doak & Mills, 1994; Prendergast et al., 1999). Nevertheless, island biogeography theory provides a theoretical foundation for understanding fragmentation. When a formerly continuous forest is isolated, the number of species will shift from its original equilibrium, mainly because of the effects of area reduction and distance to continuous forest or between forest patches (MacArthur & Wilson, 1967; Diamond, 1975). The number of species a habitat can be expected to maintain after a period of isolation is strongly area dependent. The larger the forest island, the higher the original number of species included and the lower is the rate of subsequent extinctions (Diamond, 1975). Because ecosystem destruction is an integral part of fragmentation, it is inevitable that fragmentation will have negative effects on biodiversity. Moreover, the consequences are much greater than could be anticipated based solely on the area of ecosystems destroyed. Deforestation affects biological diversity in three ways: habitat loss, isolation of fragments of formerly contiguous habitat, and edge effects within a boundary zone between forest and deforested areas (Terborgh, 1992; Dale et al., 1994; Cuarón, 2000). Edge effects extend some distance into the
8 remaining forest. In this zone there are greater exposure to winds, dramatic microclimatic differences over short distances, modifications of the local water regime caused by alterations of the hydrological cycle, facilitated access for exotic and domesticated species, and this in turn lead to accelerated tree mortality and the penetration of non-forest species into forest habitat (Lovejoy et al., 1978; Lord & Norton, 1990; Saunders et al., 1991). Many studies in the tropics have recorded these phenomena in tropical fragmented ecosystems (e.g. Bierregaard et al., 1992; Laurance & Bierregaard, 1997; Chiarello, 1999; Lindenmayer et al., 1999). At present rates of land-use/land-cover conversion, in a few decades most of the world’s tropical forests will consist of isolated forest remnants (Terborgh, 1992). Therefore, it is important to determine the extent to which isolated forest fragments can sustain representative tropical biotas, and to apply strategies of reserve design that help minimize effects of habitat loss and fragmentation.
Metapopulations Another ecological theory that has proven influential in conservation biology and in the understanding of the effects of fragmentation is the theory of metapopulation dynamics (Levins, 1969; Doak & Mills, 1994; Hanski & Simberloff, 1997). Some of the effects of fragmentation are habitat loss and isolation that cause disruption in migration and dispersal patterns, and reduced population sizes and genetic variability. These effects of fragmentation are
9 manifested through increased isolation of small populations in forest patches embedded in a once contiguous forest. These populations that are patchily distributed and are interconnected by patterns of gene flow, extinction, and recolonization are termed metapopulation (Wiens, 1996; Ritchie, 1997). Several metapopulations models have been proposed: a) the Levins’ metapopulation that assumes a large network of similar small patches, with local dynamics occurring at a much faster time scale than metapopulation dynamics; in a broader sense used for systems in which all local populations, even if they may differ in size, have a significant risk of extinction; b) the mainland-island metapopulation is a system of habitat patches (islands) located within dispersal distance from a very large habitat patch (mainland) where the local population never goes extinct. Hence mainland-island metapopulations do not go extinct; c) in source-sink metapopulations there are patches in which the population growth rate at low density and in the absence of immigration is negative (sinks) and patches in which the growth rate at low density is positive (sources); and d) the nonequilibrium metapopulation is a metapopulation in which (long-term) extinction rate exceeds colonization rate or vice versa; an extreme case is where local populations are located so far from each other that there is no migration between them and hence no possibility for recolonization (Hanski & Simberloff, 1997). In some species, every subpopulation is shorted lived, and the distribution of the species changes dramatically with each generation. In other species, the
10 metapopulation may be characterized by one or more core, or source populations with fairly stable numbers, and several satellite or sink populations that fluctuate with arrivals of immigrants. Populations in the satellite areas may become extinct in unfavorable years, but the areas are recolonized by migrants from the more permanent core population when conditions become more favorable – a rescue effect – (Wiens, 1996; Ritchie, 1997). The rescue effect was first envisioned in the context of the dynamic theory of island biogeography, which assumes the mainland-island metapopulation structure. In the case of metapopulations where no mainland exists, there is still rescue effect for individual populations, only with the difference that immigration rates vary temporally, depending on how many habitat patches within migration range happen to be occupied. With increasing size of the metapopulation (more occupied patches), there is more immigration to a particular patch and thus a stronger rescue effect (Hanski et al., 1996).
Stochasticity and Extinction Vortices The causes for extinction among small and isolated populations can be divided into two different types: deterministic or systematic pressures (human generated), and stochastic (accidental) perturbations. Deterministic pressures are those that can be predicted and controlled, such as hunting, deforestation, pollution, and introduction of species. Stochastic perturbations are those that elude human prediction and control. Stochastic perturbations introduce uncertainty into the
11 future of a population, and the smaller the population, the greater the uncertainty (Lande, 1998). So the study of uncertainty and its consequences is crucial to species conservation. In general, there are four sources of uncertainty to which a population may be subject: demographic stochasticity, environmental stochasticity, genetic stochasticity, and natural catastrophes. Demographic stochasticity means accidental variations in birth rates, death rates, and the ratio of the sexes. Environmental stochasticity means fluctuations in weather, food supply, and population levels of predators, competitors, parasites, and disease organisms with which species must deal. Neither of these two types of uncertainty could destroy a large population of organisms (Shafer, 1987). Genetic stochasticity refers to random processes by which certain alleles become more common or more rare within a gene pool, irrespective of the influence of natural selection (Shaffer, 1987). Genetic stochasticity can be produced in two ways. First, helpful alleles can become so rare by random processes, such as genetic drift, that they disappear accidentally. Second, harmful alleles that are recessive and rare, and because of their rarity, usually carried only in the heterozygous situation can become just common enough within a small population that they occur homozygously. Inbreeding increases the chance of homozygosity, by pairing family carried alleles with themselves and when recessive alleles occur in the homozygous situation, they achieve expression. If they are harmful recessives, what they express is harm. The total sum of harmful
12 recessive alleles, within any given populations, is known as the genetic load. Since small populations are often forced toward inbreeding, they frequently suffer from the expression of those harmful recessive alleles. The result is called inbreeding depression (Shaffer, 1981, 1987). Natural catastrophes such as floods, fires, droughts, hurricanes, earthquakes, and volcanic eruptions can cause dramatic fluctuations in population levels. Such events are not totally random but are unpredictable. Natural populations can kill part of a population or even eliminate an entire population from an area (Shaffer, 1981, 1987). In populations below a certain size or density, individual may suffer reduced fitness from insufficient cooperative interactions with conspecifics. In many animal species, small populations may be unstable due to the inability of the social structure to function once the population falls below a certain size; this is known as the Allee effect. Cooperative social behaviors occur in many animal species, including group defense against predators, communal nesting, and increased per capita efficiency of group foraging. More generally in small populations, individuals may have difficulty encountering potential mates. These effects can produce negative rates of population growth in small populations increasing their likelihood of becoming extinct (Lande, 1998; Hanski, 1999). The smaller a population becomes, the more vulnerable it is to further demographic, environmental, and genetic chance events that tend to reduce
13 population size even more and drive the population to extinction. This tendency of small populations to decline toward extinction has been compared to a vortex (the closer it gets to the center, the faster it moves). Once caught in such a vortex, it is difficult for a species to resist the pull toward extinction. There are four vortices: the demographic vortex, the inbreeding vortex, the fragmentation vortex, and the adaptation vortex. If a species fell into the ambit of one vortex, it is more likely to be swept into the others. However, each species would differ in its vulnerability to each of the four vortices (Gilpin & Soulé, 1986)
Population Viability Analysis In this context, population viability analysis (PVA) has become a widely used tool for conservation biology (Ambruster & Lande, 1993; Akçakaya et al., 1995; Goldingay & Possingham, 1995; Lindenmayer & Lacy, 1995a, 1995b; Horino & Miura, 2000; Kelly & Durant, 2000; Plissner & Haig, 2000). PVA is a way to predict the probability that a population (or species) of a specified size will persist for an arbitrary length of time, by inputting actual life-history information and projecting it forward in time using stochastic computer simulations (White, 2000). PVA uses a range of input data that can be grouped on three interacting categories: a) population phenotype (e.g. morphology, physiology, and behavior) that includes all of the physical, chemical and biological manifestations of the
14 population; b) the environment that represents the context of the analysis and includes abiotic and biotic factors that affect the population (e.g. habitat quantity and quality, and patterns of disturbance); and c) population structure (e.g. population size, age classes, sex ratio, growth rates, and carrying capacity) that represents the manifestation of the dynamic consequences of the interaction of the two previous categories (Gilpin & Soulé, 1986). Based on these data, PVA estimates persistence for individuals through generations and simulates the fate of the population many times to give a frequency distribution of extinction or survival, providing population statistics such as population size, and genetic variation. PVA is a very complex model because it attempts to consider every factor that affects natural populations and their likelihood of extinction. PVA as a comprehensive methodology has several advantages and applications in wildlife management; however, PVA is not free of weaknesses and limitations. Due to the comprehensive nature of PVA as a methodology, it has several advantages intrinsic to the process itself. In order to perform a PVA a complete review of the ecology of the target species of the analysis is required. This results in an exhaustive assessment of the knowledge about a species and identifies limitations of existent data that measure the general ecological understanding for a given species (Boyce, 1992; Lindenmayer et al., 1993; Ruggiero et al., 1994; Matsuda et al., 2000). This assessment is an important step in formulating new research questions because weaknesses and gaps in basic ecological knowledge are
15 identified. PVA produces an estimate of the relative importance of various interacting factors that may cause the extinction of a population. These types of estimates are not possible to obtain with methods other than PVA. This is important because processes threatening the species are identified and current conservation policies and conservation programs can be evaluated. PVA also allows the quantification and identification of trends in population responses to the environment. PVA identifies trends for declining populations early in time allowing preventive and adaptive management before these populations become endangered. This is useful because attempts to conserve small endangered populations are expensive and most of the times unsuccessful (Lindenmayer et al., 1993). PVA and MVP are very related concepts so that they can be used to estimate minimum dynamic areas and design protected areas. As the list of threatened and endangered species continues to grow, it becomes imperative that conservation strategies focus on preserving ecosystems. One of the most effective ways to achieve this at present is to focus on conserving vertebrates that have large area requirements (Ambruster & Lande, 1993; Lindenmayer et al., 1993; Goldingay & Possingham, 1995; Akçakaya et al., 1995). The link between PVA and protected areas is based on the understanding of several factors, including the habitat and area requirements of the species, the spatial and temporal availability of suitable habitat, and the effectiveness of remaining habitats such as corridors in fragmented habitats (Beier, 1993; Frank & Wissel, 1998; Sweanor et al., 1999).
16 However, currently the habitat requirements of the vast majority of species are poorly understood or completely unknown. PVA may also be used with other procedures (e.g. remote sensing, GIS) to assess the adequacy of nature reserves and to determine the human impact on wildlife populations. The problem of PVA is not the model per se, but obtaining the data to drive the model. As a data intensive technique, the more data the better, but often data are incomplete or nonexistent, especially for rare and endangered species. This major limitation to the meaningful application of PVA has been known for the last two decades, although little has been done to address this problem and still may take decades to collect (Boyce, 1992; Hamilton & Moller, 1995). There are a number of software packages and demographic models that have been developed for the application of PVA. These packages and models have been accepted and applied by conservation biologists although they have not been empirically tested (despite being first developed approximately 20 years ago, the predictions of PVA models have rarely been tested). However, testing PVA with the threatened and endangered species for which these software packages and models were designed is logistically impossible, and even common species cannot provide the necessary replicated field populations for testing stochastic models (Boyce, 1992; Lindenmayer et al., 1993; Ludwig, 1999). Real world is more complex that can be simulated by computers and models are simplifications of real world processes. PVA models necessarily include a large
17 number of assumptions and simplistically model the responses of populations or species to environmental conditions and variation (Ruggiero et al., 1994; Ludwig, 1999; Brook et al., 2000). These limitations may cause that PVA predictions may be not only too optimistic if important threats are not considered, but also too pessimistic if data redundancy in important variables is included in the analysis (Lindenmayer et al., 1993; Brook, 2000). Although, PVA provides only a relatively simple estimation of the actual dynamics of wild populations and caution should be used when interpreting and applying the results of the models, PVA reflects quantitatively trends in the behavior of populations under certain environmental conditions.
18 CHAPTER III METHODS
Study Area Ecuador extends across the equator from 1o 30’ N to 5o S. It is traversed from north to south by the Andes that divide the country into three natural regions: the Pacific Coastal Plains, the Andean Highlands, and the Amazon (Figure 1). Despite its small size (only 250,000 km2 or 1.5% of the total area of South America), biologically it is one of the most diverse countries in the world, measured both by the absolute number of species and by the number of species per unit of area (WRI, 1998; Sierra et al., 1999). Ecuador has over 25,000 known plant species (Jørgensen & León, 1999), 460 amphibian species, 410 reptile species, more than 1550 bird species, and 369 mammal species (Tirira, 1999). This makes Ecuador one of the most species-rich countries on earth, with many of these species being endemic (Myers, 1988; Dobson & Gentry, 1991). In contrast to this unique biological wealth, the country has some disturbing demographic characteristics. The human population, approximately 13 million, is already one of the densest (52 persons/km2) and fastest growing (2.3% per year) in South America (Sierra, 2000), and the rate of expansion of agricultural land is the second highest among twenty countries in Latin America (Pichón, 1997a). This expanding population is threatening the long-term sustainability of these biological
19 resources. Recent research estimates that the rate at which this population is clearing primary forests (2.4% per year) is one of the highest in Latin America (Marquette, 1998; Sierra, 2000). The Ecuadorian Amazon region comprises part of the upper basin of the Amazon covering an area of approximately 135,000 km2. The climate is perpetually wet with annual precipitation ranging from 2500 mm to 6000 mm. The mean temperature for all months is about 25oC. In general, the soils of the area constitute a mixture of old weathered and more recently deposited fluvial sediments (Balslev & Renner, 1989). In the late 1960s and early 1970s, large oil deposits were found in the region. As a result, the population in the region has increased from approximately 70,000 in the 1960s to almost 400,000 in the 1990s, with most of this growth due to immigration. This represents an enormous increase of 432% or 10.8% growth per year. Immigrants, mainly small farmers from other Ecuadorian regions, follow the construction of oil roads that provide access facilitating and directing their settlement. By the 1990s, the northeastern Ecuadorian Amazon had one of the highest estimated deforestation rates in the entire Amazon with approximately 2% of forest lost each year (Uquillas, 1985; Pichón, 1997a, 1997b; Sierra, 2000). Since forest clearing due to oil company expansion is limited, deforestation in the region is closely linked to the activity of small farmers using slash-and-burn practices
20 following a “peasant pioneer cycle” in which land is quickly cleared to plant crops and graze cattle (Pichón, 1996; Marquette, 1998). The northeastern Ecuadorian Amazon possesses a potentially valuable protected area system that includes two large reserves: Yasuní National Park and Cuyabeno Fauna Production Reserve. The focus of this research is these two protected areas (Figure 2). The cycle of petroleum and frontier agricultural development is especially acute along the periphery and just inside the borders of these areas. In 1979, the Ecuadorian government declared Cuyabeno and Yasuní protected areas. Cuyabeno has an area of approximately 6,030 km2 and the altitude ranges from 200 to 280 m above sea level. Yasuní has an area of 9,820 km2 and the altitude ranges from 300 to 600 m above sea level. Yasuní is the largest protected area in Ecuador. In both areas, three main types of vegetation occur: “terra firme” that are upland forests not subjected to flooding, “varzea” a forest type subject to non-periodic flooding by white waters from the Andes, and “igapó” a forest type exposed to seasonal flooding by black waters originated in the flat plains of the Amazon (WCMC, 2000). Although extensive in area and legally protected, Cuyabeno and Yasuní in practice are virtually unprotected. The Park Service is underfunded, understaffed, and lacking political support. The threats faced by both reserves are numerous. They include invasion by non-indigenous colonists, illegal timber harvesting, illegal hunting and fishing, and oil industry activities. Consequently, the
21 northeastern Amazon region of Ecuador is a particularly important site for conducting research for two main reasons: a) its rich biological diversity – the area is considered one of the worlds 10 “hot-spots” in terms of biodiversity (Myers, 1988; Mittermeier et al., 1998; Myers et al., 2000) and a globally outstanding ecoregion in Latin America (Dinerstein et al., 1995); and, b) the direct contradiction between development and conservation needs – over half of both government revenues and the country’s foreign exchange earning are derived from the oil which is extracted precisely from that part of the Ecuadorian Amazon (Pichón, 1997b).
Geographical Data Sources The primary materials required to accomplish the initial stages of the analysis were four Landsat-5 TM images covering the entire study area: path 9 row 60 (October, 1996), path 9 row 61 (September, 1995), path 8 row 60 (December, 1996), and path 8 row 61 (February, 1996). Other materials included four 1987 topographic maps, scale 1: 250,000 and three thematic maps of the protected areas (Dubaele, 1998, 1999; Canaday et al., 2000). These materials were used in georeferencing the satellite images, classifying vegetation types, digitizing the boundaries of the areas set aside for the protection of biological diversity, and estimating the area of suitable habitat for the indicator species.
22 Analysis of Spatial Information Figure 3 presents a schematic diagram of the image analysis procedures and cartographic modeling used to obtain the area of forest inside the protected areas. The initial stage entailed the interpretation and analysis of the satellite images to classify the landscape under study into three general land-use/land-cover classes (forested areas, non-forested or disturbed areas, and water) and to calculate the area of forested area in the study area. The image interpretation and analysis involved rectifying, mosaicking, and classifying the images. The process was carried out using IDRISI 32, an image processing and geographic information system software (Eastman, 1999a, 1999b). Rectification is the process of georeferencing the image files so that true geographic location of features can be determined. Prior to rectification, the images have only a column and row referencing system. Rectification assigns the known geographic location of features on the landscape to the same features discernible in the images. Rectification involved the use of topographical maps to establish geographic control points. These control points were selected from readily identified features on the images (mainly river junctions) and marked on the topographical maps. Of the initial 52 control points, 34 were ultimately used in the image rectification. These gave a total RMS (root-mean-square) error of 0.76 pixels, equating to an error of approximately 23 m. The RMS error describes the probability that a mapped position will vary from its true location on the ground.
23 According to U.S. national map accuracy standards, the RMS error should be less than 15 m or 0.5 pixels (Jensen, 1996; Eastman, 1999c). The 23 m error suggests that the reference topographical maps are not accurate enough and a GPS field verification effort is necessary to meet the accuracy standards. The mosaicking process joined together the four images into a larger one based on the UTM reference system adopted during the rectification process. The image classification process refers to the computer-assisted interpretation of the images based on mathematical algorithms to classify the spectral signatures of the images into a number of land-use/land-cover classes. The classification was performed using a supervised classification. The first step in the classification was to identify examples (known as training sites) of the information classes (water, forest, and disturbed areas) of interest in the image. This was done by examining various three band false-color composite images of the study area until the band combination with the best visual contrast (bands 2, 4 and 5) between forest and non-forest was found. After the selected sites were trained (digitized), the signature files were created. Signature files contain statistical information about the reflectance values of the pixels within the training sites for each class. After the statistical characterization was done for each informational class, the image was classified by examining the reflectance for each pixel and making a decision about which of the signatures it resembles most. The algorithm used to make this decision was the minimum distance to means classifier
24 with standardized distances. This algorithm is based on the mean reflectance of each signature, and assigns pixels to the class with the mean closest to the value of that pixel. This classifier was chosen because there were concerns about the quality of the training sites (particularly their uniformity throughout the mosaic of four images) and this option is a very strong classifier under this circumstance (Jensen, 1996; Eastman, 1999b). The last step of the classification process involved an accuracy assessment. This was done by generating a random set of 100 locations on the image for verification of the true land cover type. The remote sensing derived classification was compared to a reference source of information. The reference for the assessment was a remnant vegetation map of the entire country because there is no detailed information of the vegetation in the study area (Sierra, 1999). The relationship between these two sets of information produced an overall accuracy level of 92%. Table 1 shows the error matrix with the accuracy of each land-use / land-cover category along with both the errors of inclusion (commission errors) and errors of exclusion (omission errors). Once the mosaic of images was classified, a GIS query operation using IDRISI 32 was undertaken. The first step was a reclassification of the classified image to create a binary image of forest / non-forest. Once the forest layer was created, a layer with the boundaries of the two protected areas was digitized and transformed from vector to raster file, and both layers were combined using an
25 overlay operation. Lastly, the area of forest inside each protected area was calculated with the area analysis tool of IDRISI 32.
Biological Data Sources The variables needed to carry out the biological analysis for the target species of the PVA analysis were based on their biological and ecological characteristics. The life-history characteristics used in this research (Table 2) were obtained from the best available data on the biology and ecology of the indicator species (Eisenberg & Thorington, 1973; Thorington & Heltne, 1976; Eisenberg, 1978; Schaller & Crawshaw, 1980; Wolfheim, 1983; Montgomery, 1985; Mondolfi & Hoogesteijn, 1986; Robinson & Janson, 1986; Robinson & Redford, 1986a, 1986b; Crockett & Eisenberg, 1987; Crockett & Rudran, 1987a, 1987b; Happel et al., 1987; Shaw et al., 1987; Van Roosmalen, 1988; Seymour, 1989; Robinson & Redford, 1991; Hayssen et al., 1993; Padilla & Dowler, 1994; Frankham, 1995; Rylands et al., 1995; Nowell & Jackson, 1996; Voss & Emmons, 1996; Emmons, 1997; Eisenberg & Redford, 1999; Nowak, 1999; CITES, 2000; Chiarello, 2000; IUCN, 2000; Tirira, 2001).
The PVA Model To determine if the protected areas are protecting the indicator species in the long-term, a theoretical population size that can be supported in each one of the
26 protected areas was estimated. This was calculated using the information of forest area (suitable habitat) in 1996 inside both parks obtained from the image analysis and the theoretical population density obtained from the literature (Table 3). This information was included in a PVA model to calculate the likelihood of extinction of the indicator species inside the protected areas. The population dynamics of the indicator species were modeled using the VORTEX computer simulation model, version 8.41 (Miller and Lacy, 1999; Lacy, 2000b). VORTEX has been applied extensively in studies of endangered species worldwide (Lacy et al., 1989; Foose et al., 1993; Lindenmayer & Lacy, 1995a, 1996b; Horino & Miura, 2000; Plissner & Haig, 2000). The program is an individual-based simulation that models demographic, environmental variation, short-term catastrophic declines in survival or reproduction, and the genetic variation present within and among individuals. Feedback between genetic and demographic processes can be modeled by incorporating inbreeding depression. Linear trends in habitat availability and habitat limitations simulations use a ceiling model where additional mortality is imposed across all age classes when the population exceeds the habitat carrying capacity. Multiple populations exchanging migrants, and harvest and supplementation of populations can also be modeled with VORTEX (Miller & Lacy, 1999; Lacy, 2000b). The modeling undertaken was very crude as it depended on the fragmentary data available for the indicator species, as well as several unavoidable assumptions.
27 The population structure examined was simple and hypothetical. The analysis assumed that the total area of forest in both protected areas (obtained from the image analysis) is suitable for wildlife survival. The initial population sizes for the analysis were calculated using this assumption and based on density estimates from the literature. Also, each park was assumed to support only one population of each species and individual supplementation from outside the parks was not allowed, thereby considering the parks “islands” of forest surrounded by a “sea” of other forms of land-use. It was also assumed that the probability of dispersal was identical for males and females; however, it appears that the indicator species exhibit differences between the sexes, in the age of dispersal, and the distance that they move. The PVA did not model catastrophes such as fire and prolonged drought because these types of events do not commonly occur in the study area. The PVA involved stochastic and deterministic simulations in five different scenarios for each indicator species: a) a single population simulation in Cuyabeno including only stochastic factors of extinction, b) a single population simulation in Yasuní including only stochastic factors of extinction, c) a single population simulation in Cuyabeno including stochastic and deterministic factors of extinction, d) a single population simulation in Yasuní including stochastic and deterministic factors of extinction, and e) a hypothetical metapopulation simulation occurring in a reserve network, joining both protected areas (C-Y Network) including stochastic and deterministic factors of extinction.
28 For each population of each indicator species, survival was simulated for 100 years with extinction reported every five years and each simulation was run 1000 times to estimate the mean probability of extinction. The minimum viable population was considered to have a probability of extinction of 0.01 after 100 years. In the deterministic simulations, annual habitat loss rates were calculated assuming that in 1979 there was a 100% of forest cover in the study area, when oil activities were not as intensive as they became later. In the deterministic simulations, based on the habitat loss rate estimated from the image analysis, the effect of decreased carrying capacity caused by habitat reduction was modeled. In the metapopulation simulation, for modeling purposes, the link between the two protected areas was a non-spatial connection allowing gene flow and dispersal. A one-way analysis of variance (ANOVA) was carried out to compare the results of the deterministic scenarios in Cuyabeno, Yasuní and the reserve network. The null hypothesis tested was that there is no difference between the persistence probability of the indicator species between the parks and the reserve network; and, therefore the species do not benefit from the creation of the network.
29 CHAPTER IV RESULTS
Figure 4 shows non-forested areas inside and surrounding the protected areas, as well as examples of the types of human disturbance occurring in the study area. According to the PVA results (Table 4), neither Cuyabeno nor Yasuní are protecting minimum viable populations for all the indicator species. Cuyabeno only protects howler monkey populations and Yasuní does not protect jaguar populations; clearly, “one size does not fit all.” The results suggest that the populations of giant anteaters, jaguars, and tapirs have a high risk of extinction in Cuyabeno and the populations cannot pass the test of the minimum viable population size concept (Table 4), and therefore Cuyabeno cannot be considered a minimum dynamic area, under current conditions. In the case of the spider monkey, the only species that does not occur in both protected areas, the growth rate is negative although it complies with the standard (p < 0.01) of minimum viable population (Table 4). Although the likelihood of survival increased for all four mammalian species used in the metapopulation simulation, in which migration and gene flow is allowed between the parks (C-Y network), their survival is not ensured (Table 4). In the case of the tapir and the jaguar the mean growth rate is negative even in the C-Y network model suggesting that in simulations of more than a hundred years
30 both populations will become extinct locally. The giant anteater and the howler monkey benefit from the connectivity between the two reserves. Thus, over the next one hundred years the indicator species might be lost from extensive parts of its present range within the protected areas. Although this model is conservative, due to a number of assumptions and limitations of the data as mentioned in Chapter III, population trends are quite clear and the results are similar, regardless of the demographic data used as input of the model (Table 5 – 7). There is a strong relationship between population decline of the indicator species and changes in habitat carrying capacity when stochastic and deterministic simulations are compared (Figures 5 – 9). A general trend for the relationship between population size and changes in habitat carrying capacity revealed a sharp decline in population size with higher deforestation rates (Figures 5 – 9). This trend remained virtually unchanged when different population sizes are applied (Figures 5 – 9). The results also indicate that a metapopulation structure in which gene flow and migration is allowed could have a positive effect on subpopulation persistence. PVA results show that the indicator species are benefited from the creation of the reserve network as shown in Figures 10 to 14. Under current rates of deforestation, extinction risk is lower in the network and persistence probability increases for the subpopulation in Cuyabeno.
31 According to the results of the ANOVA, persistence probabilities are lower and significantly different (F-ratio = 6.69, p = 0.016) in Cuyabeno compared to Yasuní and the reserve network (Figure 15). This result is caused by differences in size (Cuyabeno is smaller in area) and differences in deforestation rates between the protected areas (1% of Cuyabeno v. 0.3% of Yasuní). The results highlight a metapopulation model where Yasuní functions as a “source” and Cuyabeno as a “sink.”
32 CHAPTER V DISCUSSION
PVA results provide clear, consistent predictions and a time frame for the probability of extinction of the indicator species populations (Table 4, Figures 5 – 14). In the study area, the main factors of population persistence are those under human control, habitat loss and the presence of a corridor allowing migration. The protected areas, under current trends of habitat loss, likely will not support the indicator species for the next 100 years. Without dispersal among them, these populations are not likely to survive. It is still unclear if different PVA software packages produce similar predictions, and whether the predictive models realistically describe the behavior of wildlife populations. An example of this controversy is presented in Brook et al., 1999 and 2000; when PVA software packages (GAPPS, INMAT, RAMAS and VORTEX) were compared (Brook et al., 1999) large differences were found between packages, and even versions of the same package. The pattern of similarities and differences varied depending on the species examined; and when completely standardized, a consistent difference was revealed between the predicted extinction probabilities of the matrix-based packages (INMAT and RAMAS) compared to those that were individual-based (GAPPS and VORTEX), caused mainly by differences in the way demographic stochasticity is modeled. The
33 conclusions of this study were that caution must be exercised when interpreting results of a PVA and that current PVA packages cannot be relied upon to produce accurate quantitative predictions. One year later Brook et al. (2000), contrary to their previous findings, reported that PVA predictions (using the same software packages) were surprisingly accurate. Populations decline closely matched observed outcomes in the field without significant bias, and population size projections did not differ significantly from reality. Furthermore, there was high correlation between the predictions of the different PVA software packages. The conclusion was that PVA software packages are accurate tools for categorizing the vulnerability of endangered animal species and evaluating options for their recovery. PVA focuses on the probability of extinction of a population, but does not consider the functional role of taxa or the interdependence among organisms on an ecosystem. Biodiversity conservation should be based on the maintenance of ecological functions in ecosystems, such as pollination, seed dispersal, and nutrient cycles. However, the estimation of ecologically functional populations is currently not possible using PVA (Lindenmayer et al., 1993). Therefore, ecosystem viability must be determined by the viability of bioindicators (e.g. umbrella species, keystone species). This approach assumes that the conservation of such organisms will also result in the conservation of a significant proportion of other species. Thus, PVA may be most effective if focused on these species.
34 Population Viability Analysis uses only biological information to assess probability of extinction, threats, and effects of possible actions; however, the conservation of biodiversity is mainly an issue of addressing non-sustainable activities of human populations. The linkages between human demographic, economic, and social systems and wildlife population biology must be identified. It is necessary to integrate analysis of human systems with PVA models, but integrating human and natural systems may be very complex because requires a broader range of expertise, models, and data. The present research effectively illustrates how population modeling can be used to explore links between demographic processes and the environment, and to evaluate management strategies for umbrella species. Effective population modeling, however, requires detailed information about the demographics, density dependence, dispersal characteristics, habitat requirements, population sizes, and distribution of required habitat of the species (Boyce, 1992; Ruggiero et al., 1994; Ludwig, 1999; Akçakaya, 2000; Lacy, 2000a). It is important that protected areas ensure persistence of large mammalian predators (e.g. jaguars) and large herbivores (e.g. tapirs) because these species exert a strong influence on community structure. Their extirpation from an ecosystem can lead to severe modification of biological communities and ecological interactions, altering the prospects for forest maintenance and regeneration
35 (Terborgh, 1988; Dirzo & Miranda, 1990; Redford, 1992; Malcolm, 1997; Wright et al., 2000). The results are conservative for a number of reasons. In this model, habitat types were assumed to be independent. Therefore, no indicator species populations were simulated as moving among land cover types. However, these populations are likely to move into subpopulations in undisturbed and slightly disturbed forests (e.g. secondary forests, shaded plantations) and to be successful in areas outside the administrative borders of the protected areas. Essentially ignored in this model were any context effects of the surrounding landscapes. Surrounding landscapes are likely to reduce or increase survival. Because other individuals from different populations outside the protected areas may move into the protected areas populations, such a rescue effect is likely to have a large impact on the long-term viability of the indicator species. A better approach would include data obtained directly in the field from populations in the study area. Alternatively, hunting data would be useful for evaluating the effects of other deterministic causes of extinction. Several assessments of the impact of hunting throughout the Amazon have been carried out (Alvard et al., 1997; Bodmer et al., 1997; Cullen et al., 2000; Novaro et al., 2000; Peres, 2000) and have concluded that these populations when hunted are almost invariably overhunted. If, in addition to the potential carrying capacity reduction,
36 actual hunting pressure is considered (not accounted in the model) the indicator species very likely will fail to survive for 100 years at a very high probability. The sensitivity of the model to decreased carrying capacity due to habitat reduction (Tables 5 – 7, Figures 5 – 9) clearly indicates that wildlife persistence in both protected areas is not based on current reserve size but in the level and efficiency of protection from deterministic forces of extinction and the presence of a corridor allowing connectivity between the protected areas. Habitat quantity and quality (land-cover type) is an important factor in the long-term viability of the indicator species within the next 100 years. Simulated populations went extinct without connectivity between protected areas. The importance of quality habitat is supported not only by other simulation models (Dale et al., 1994; Bush, 1996; Cuarón, 2000; Peterson et al., 2000) but also by empirical work on mammal populations throughout the Amazon basin and other parts of the Neotropics (Emmons, 1984; Ferrari & Diego, 1995; Downer, 1996; Estrada & Coates-Estrada, 1996; López Ornat, 1996; Chiarello, 1999, 2000; Roldán et al., 2000), and is similar to findings for other threatened and endangered species worldwide (Ambruster & Lande, 1993; Beier, 1993; Goldingay & Possingham, 1995; Harcourt, 1995; Wikramanayake et al., 1998; Lindenmayer et al., 1999; Kelly & Durant, 2000; Horino & Miura, 2000). The research results also suggest that the scale of connectivity also may be an important consideration when evaluating long-term viability. Without the influx
37 of new individuals from other areas, slowly reproducing species such as giant anteaters, spider monkeys, jaguars and tapirs are likely to experience local extinction, especially under increased deterministic local conditions (e.g. hunting and deforestation). The importance of this connectivity for long-term viability has also been demonstrated for other mammal species (Beier, 1993; Sweanor et al., 1999; Carrillo et al., 2000; Escamilla et al., 2000). Although it is a short time period for long-term conservation objectives, a hundred years time horizon was selected for the PVA model because it is a realistic time frame for management purposes. In addition, if current deforestation rates remain constant or increase in the study area, the remaining forest would disappear during the next one hundred years. This situation requires constant monitoring of wildlife populations in an adaptive management framework. This will allow constant feedback of management actions producing more responsive and more effective conservation and management programs. Species can be classified according to their vulnerability to extinction. The threatened species categories in Red Data Books and Red Lists that are now used in a whole range of publications and lists, produced by IUCN as well as by numerous governmental and non-governmental organizations, have become widely recognized internationally. The current IUCN scheme classifies populations and species according to an x % chance of extinction in y years or z generations, such as the “critical” criterion, a 50% chance of extinction within 10 years or 3 generations
38 (UICN, 2000). The classification procedures are, in general, subjective and qualitative, based on expert judgment. Mace and Lande (1991) recommended the application of PVA to the classification of threatened and endangered species into risk categories. More appropriate classification criteria are expressed in terms of quantitative analyses based on PVA that estimates the extinction probability of a taxon or population based on the known life history and specified management or non-management options. In Ecuador, the Mammal’s Red Data Book (Tirira, 2001) includes 120 species. Based on the PVA results the giant anteater should be classified as a “vulnerable species” (currently in Ecuador this species is included in the “data deficient” category); the Brazilian tapir should be included in the “endangered species” category instead of the “near threatened” category and the jaguar also should be moved, from the “vulnerable,” to the “endangered” category. PVA results support the classification of the white-bellied spider monkey in the “vulnerable” category. The red howler monkey, a species not considered in the Red Data Book (Tirira, 2001), is a species with a low risk of extinction, according to the PVA results, and should be considered in the “less concern” category. However, due to the model limitations and based on the precautionary principle (when there is uncertainty in the estimate of the extinction risk, it is legitimate to use the highest credible estimate), the howler monkey could benefit more if included in the “conservation dependent” category.
39 Genetic modeling was not included in the results because many have argued against the use of genetic modeling in PVA since the link between genetic variability and population viability remains to be established (Lande, 1988, 1998; Simberloff, 1988; Walters, 1991; Nunney & Campbell, 1993). For threatened species, it is important to evaluate the factors that influence long-term viability. PVA is a valuable tool for evaluating potential threats that face rare species and for suggesting future research avenues. Large mammal species are likely to be influenced by habitat loss and fragmentation. Such species also are vulnerable to stochastic causes of extinction. PVA provides the means to examine these issues and evaluate potential impacts of future threats. Therefore, it is important to collect spatial, as well as demographic data for conservation planning. PVA is a dynamic process and it should be modified to meet the particular requirements of any given project, and it is more effective if integrated with other techniques and technologies, such as remote sensing and GIS, and applied with and adaptive management philosophy.
Management Considerations Because the establishment of protected areas is a long and difficult process, the creation of reserve networks takes advantage of existing reserves and represents an important strategy for the optimization of the conservation potential of individual reserves (Sierra et al., 1999). The final objective should be to gradually
40 build a reserve network that ensures long-term biodiversity protection. Based on the results of this study several recommendations for management of the indicator species, other long lived, slowly reproducing species, and habitat specialist species are proposed. These recommendations can be summarized in two broad categories: a) acquire and restore forested habitats to optimal conditions within and surrounding the protected areas; and b) maintain dispersal by connections between the protected areas and among patches within populations and among populations. These considerations support long-standing recommendations to protect suitable habitat (Diamond, 1975; Lovejoy et al., 1978; Pickett & Thompson, 1978; Higgs, 1981; Simberloff & Abele, 1982; Soulé & Simberloff, 1986; Zimmerman & Bierregaard, 1986; Terborgh, 1992) and arguments for increased connectivity among habitat patches (Fahrig & Merriam, 1985; Lindenmayer & Nix, 1993; Naiman et al., 1993; Rosenberg et al., 1997). In the study area, these recommendations involve several management strategies. With adequate political support, an effort that integrates these recommendations into a socio-economically viable conservation program is not unrealistic and therefore might be attainable: •
Remaining forested areas within the limits of Cuyabeno and Yasuní should be off limits for any kind of development (road construction, human settlements, oil exploration and exploitation, and logging),
41 •
The total area of Cuyabeno should be extended to the northeast, where a large forested area between Papaya Grande, Putumayo and Güepí rivers still remains free of anthropogenic disturbance (Figure 16),
•
The eastern part of the Huaorani Ethnic Reserve should be legally annexed to Yasuní National Park in order to increase its protection status. This consideration does not undermine indigenous rights since they can maintain their activities in the area and, in the long-term, wildlife and indigenous people will be benefited (Figure 16),
•
A landscape linkage should be added between Cuyabeno and Yasuní (Figure 16). This area will provide dwelling habitat in addition to the corridor function for species with high dispersal abilities (the Napo River is a barrier for many species). This linkage includes an already existing reserve (Pañacocha Protected Forest) and the territory of several Quichua communities (Samona Yuturi, Chonta Urcu, San Roque, and El Edén).
•
In already disturbed areas, natural regeneration and reforestation might be the only rational and sustainable alternative to the continued generation of disturbed areas,
•
The signing of a definitive peace agreement with Peru in 1998 should allow new conservation efforts like Transfrontier Conservation Areas (Hanks, 2000) encompassing the existing protected areas and joining new areas in northern Peru, where large tracks of suitable habitat for wildlife exist.
42
The network was designed using the information of forest available in 1996 from the image analysis and maps including information about land tenure in the study area (Dubaele, 1998; Canaday et al., 2000). Several rivers cross the study area from West to East (Figure 2 & 16); however, almost all the indicator species could potentially benefit from the creation of this network since they have very large area requirements and long dispersal distances. In addition, they can easily cross rivers the size found in the study area. For example, the jaguar and the giant anteater have daily movements of several kilometers and howler monkeys cross rivers more than 500 m wide (Milton & May, 1976; Schaller & Crawshaw, 1980; Montgomery, 1985; Seymour, 1989; Padilla & Dowler, 1994; Nowak, 1999). The spider monkey is an exception because Napo River is the northern boundary of its distribution range in northeastern Ecuador (Tirira, 1999).
Final Considerations The results of the PVA provide information on the probability of extinction given certain assumptions about the biology and current status of the indicator species populations. As a result, it is not possible to predict what will happen to the populations with any absolute certainty. This has implications when conservation strategies are developed to reduce the risks of extinction in the population. It should be recognized that it is not possible to formulate and implement recommendations
43 that will guarantee the survival of any population. It is only possible to formulate and implement recommendations that will decrease the likelihood of extinction in populations over a given time period; however, based on the results of the PVA a new design of a reserve network and a new management plan are needed in order to allow connectivity and reduce the impact of habitat loss. The current status of wildlife conservation in Cuyabeno and Yasuní is dire, but not without hope. The prognosis for wildlife long-term survival in the study area is good if there is continued acquisition of as much habitat as possible for conservation, careful management of existing habitat, with emphasis on restoration, maintenance of the connectivity of the habitat to allow dispersal throughout the system, and continued monitoring using empirical and simulation data of the ever changing factors that affect long-term success of wildlife. This project provides a regional overview of the conservation potential of the Northeastern Ecuadorian Amazon. It would be unwise to miss the still remaining opportunity that these protected areas offer for strategic conservation planning.
44 LITERATURE CITED
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59 Table 1. Error matrix of the land use/land cover classification.
Forest
Water
Disturbed
Total
Forest
70
0
4
74
Water
0
10
1
11
Disturbed
3
0
12
15
Total
74
10
16
100
Rows = land use/land cover data
Columns = reference data
92/100
Overall accuracy
92%
Overall error
8% Omission error (%)
Forest Water Disturbed
70/74 10/10 12/16
5.4 0 25 Comission error (%)
Forest Water Disturbed
70/74 10/11 12/15
5.4 9 20
Table 2. Biological and ecological attributes of the target species based on data from the literature.
Biological attributes
Giant anteater
White-bellied
Red howler
spider monkey
monkey
Primates
Jaguar
Brazilian tapir
Carnivora
Perissodactyla
A. Taxonomic Information Order
Xenarthra
Primates
Family
Myrmecophagidae
Cebidae
Cebidae
Felidae
Tapiridae
Genus
Myrmecophaga
Ateles
Alouatta
Panthera
Tapirus
Species
tridactyla
belzebuth
seniculus
onca
terrestris
B. Basic biology Body mass (g)
27000
7500
6200
68000
150000
Time of activity
diurnal/nocturnal
diurnal
diurnal
diurnal/nocturnal
nocturnal
Trophic category
myrmecophage
frugivore
folivore
carnivore
herbivore
terrestrial
middle/upper
upper strata
terrestrial/lower
terrestrial
solitary
social
social
solitary
solitary
Population density (n/km )
0.04 - 2.3
1.6 - 14.3
2.5 - 34.5
0.02 - 0.1
0.3 - 2.6
Home range (km2)
2.5 – 4.5
2.6 – 3.9
0.3 – 2.5
25 - 87
~ 11
Age first reproduction females (yrs)*
3
5
4
3
4
Age first reproduction males (yrs)*
4
7
5
4
5
Forest strata Social system 2
Maximum life span (years)*
16
25
20
14
23
Sex ratio (%males)*
0.1
0.3
0.3
0.4
0.38
1
1
1
2
0.5
polygynous
polygynous
polygynous
polygynous
polygynous
Young per litter / year* Mating system* C. Conservation Status IUCN Red List
vulnerable
vulnerable
data deficient
near threatened
near threatened
CITES
Appendix II
Appendix II
Appendix II
Appendix I
Appendix II
Local status (Ecuador)
data deficient
vulnerable
not evaluated
vulnerable
near threatened
* PVA parameters
61 Table 3. PVA parameters based on the image analysis.
Parameters
Cuyabeno
Yasuní
C-Y Network
Total area (km2)
6030
9820
15850
Remnant forest (km2)
5005
9230
14235
Remnant forest (%)
83
94
89
Habitat loss 1979-1996 (%)
17
6
----------
1
0.3
----------
200
369
----------
----------
14768
----------
12512
23075
----------
100
184
----------
1501
2769
----------
Spatial
Habitat loss / year (%) *
Population size**
Giant Anteater
White-bellied spider monkey
Red howler monkey
Jaguar
Brazilian tapir
* From 1979 to 1996, assuming that in 1979 a 100 % of the area was covered with forest. ** Based on remaning forest in 1996.
62 Table 4. Results of the Population Viability Analysis.
Species / PVA results
Cuyabeno
Yasuní
C-Y Network
0.89 (0.0098 SE)
0.008 (0.0028 SE)
0.00 (0.00 SE)
Giant Anteater Extinction probability Persistence probability
0.11 (0.0098 SE)
0.992 (0.0028 SE)
1.00 (0.00 SE)
Mean t first extinction (yrs)
92.58 (0.32 SE)
74.25 (6.25 SE)
----------
Mean growth rate
-0.0018 (0.0004 SE)
-0.0017 (0.0004 SE)
0.0012 (0.0003 SE)
Extinction probability
----------
0.001 (0.001 SE)
----------
Persistence probability
----------
0.999 (0.001 SE)
----------
Mean t first extinction (yrs)
----------
99 (0.00 SE)
----------
Mean growth rate
----------
-0.0457 (0.0003 SE)
----------
0.00 (0.00 SE)
0.00 (0.00 SE)
0.00 (0.00 SE)
Spider Monkey
Howler Monkey Extinction probability Persistence probability
1.00 (0.00 SE)
1.00 (0.00 SE)
1.00 (0.00 SE)
Mean t first extinction (yrs)
----------
----------
----------
Mean growth rate
-0.0135 (0.0003 SE)
-0.0154 (0.0003 SE)
0.0034 (0.0002 SE)
Extinction probability
0.952 (0.0068 SE)
0.175 (0.012 SE)
0.101 (0.0095 SE)
Persistence probability
0.048 (0.0068 SE)
0.825 (0.012 SE)
0.899 (0.0095 SE)
Mean t first extinction (yrs)
85.1 (0.61 SE)
75.31 (1.23 SE)
82.09 (1.14 SE)
Mean growth rate
-0.026 (0.0006 SE)
-0.0184 (0.0005 SE)
-0.0176 (0.0004 SE)
Extinction probability
0.678 (0.0148 SE)
0.001 (0.001 SE)
0.00 (0.00 SE)
Persistence probability
0.322 (0.0148 SE)
0.999 (0.001 SE)
1.00 (0.00 SE)
Mean t first extinction (yrs)
82.95 (0.43 SE)
100 (0.00 SE)
----------
Mean growth rate
-0.0692 (0.0004 SE)
-0.0269 (0.0003 SE)
-0.0278 (0.0002 SE)
Jaguar
Tapir
63 Table 5. Results of PVA stochastic simulations using different population sizes.
Population Size in Cuyabeno
Persistence Probability
Population Size
Persistence Probability
in Cuyabeno
in Yasuní
in Yasuní
1.00 (0.00 SE)
442
1.00 (0.00 SE)
Giant Anteater 240 200
1.00 (0.00 SE)
369
1.00 (0.00 SE)
160
0.999 (0.001 SE)
295
1.00 (0.00 SE)
115014
1.00 (0.00 SE)
27690
1.00 (0.00 SE)
12512
1.00 (0.00 SE)
23075
1.00 (0.00 SE)
10009
1.00 (0.00 SE)
18460
1.00 (0.00 SE)
----------
----------
17721
1.00 (0.00 SE)
----------
----------
14768
1.00 (0.00 SE)
----------
----------
11814
1.00 (0.00 SE)
120
0.998 (0.001 SE)
220
1.00 (0.00 SE)
100
0.951 (0.0119 SE)
184
1.00 (0.00 SE)
80
0.892 (0.0107 SE)
148
0.998 (0.001 SE)
1801
1.00 (0.00 SE)
3322
1.00 (0.00 SE)
1501
1.00 (0.00 SE)
2769
1.00 (0.00 SE)
1201
1.00 (0.00 SE)
2215
1.00 (0.00 SE)
Howler Monkey
Spider Monkey
Jaguar
Brazilian Tapir
64 Table 6. Results of PVA stochastic simulations using different sex ratios.
Sex ratio (% males)
Persistence Probability
Persistence Probability
in Cuyabeno
in Yasuní
12
1.00 (0.00 SE)
1.00 (0.00 SE)
10
1.00 (0.00 SE)
1.00 (0.00 SE)
8
1.00 (0.00 SE)
1.00 (0.00 SE)
36
1.00 (0.00 SE)
1.00 (0.00 SE)
30
1.00 (0.00 SE)
1.00 (0.00 SE)
24
1.00 (0.00 SE)
1.00 (0.00 SE)
36
----------
1.00 (0.00 SE)
30
----------
1.00 (0.00 SE)
24
----------
1.00 (0.00 SE)
48
0.919 (0.0098 SE)
1.00 (0.00 SE)
40
0.951 (0.0119 SE)
1.00 (0.00 SE)
32
0.976 (0.0213 SE)
1.00 (0.00 SE)
45
1.00 (0.00 SE)
1.00 (0.00 SE)
38
1.00 (0.00 SE)
1.00 (0.00 SE)
31
1.00 (0.00 SE)
1.00 (0.00 SE)
Giant Anteater
Howler Monkey
Spider Monkey
Jaguar
Tapir
65 Table 7. Results of PVA stochastic simulations using different life span data.
Life span (yrs)
Persistence Probability
Persistence Probability
in Cuyabeno
in Yasuní
19
1.00 (0.00 SE)
1.00 (0.00 SE)
16
1.00 (0.00 SE)
1.00 (0.00 SE)
13
1.00 (0.00 SE)
1.00 (0.00 SE)
30
1.00 (0.00 SE)
1.00 (0.00 SE)
25
1.00 (0.00 SE)
1.00 (0.00 SE)
20
1.00 (0.00 SE)
1.00 (0.00 SE)
24
----------
1.00 (0.00 SE)
20
----------
1.00 (0.00 SE)
16
----------
1.00 (0.00 SE)
16
0.934 (0.034 SE)
1.00 (0.00 SE)
14
0.951 (0.0119 SE)
1.00 (0.00 SE)
12
0.972 (0.0273 SE)
1.00 (0.00 SE)
27
1.00 (0.00 SE)
1.00 (0.00 SE)
23
1.00 (0.00 SE)
1.00 (0.00 SE)
19
1.00 (0.00 SE)
1.00 (0.00 SE)
Giant Anteater
Howler Monkey
Spider Monkey
Jaguar
Tapir
66
Figure 1. Map of Ecuador showing its natural regions: the Pacific Coastal Plains, the Andean Highlands and the Amazon Region.
67
Figure 2. Map showing the borders of Cuyabeno Fauna Production Reserve (1) and Yasuní National Park (2). Red lines represent roads. Colombia and Peru are shown in gray. Map modified from Dubaele (1999).
68
Figure 3. Image analysis procedure and cartographic modeling: A) Landsat mosaic, B) study area window, C) image classification result, D) forest layer, E) borders layer, F) final layers showing forest (in gray) inside protected areas.
69
Figure 4. Map of the study area showing disturbed areas in green. Photos depict types of human disturbance in the region: a) road construction; b) non-indigenous colonization; c) human population growth (air photo of Lago Agrio city); oil production activities, d) pipeline construction, e) drilling rig, f) air pollution, g) gas-burning flares; h) fragmentation, i) edge effects, j) slash-and-burn practices (photos: Galo Zapata-Ríos).
70
a
b Figure 5. Comparison of anteater population trends under stochastic and deterministic (includes deforestation in the model) factors of extinction in Cuyabeno (a) and Yasuní (b).
71
a
b Figure 6. Comparison of howler monkey population trends under stochastic and deterministic (includes deforestation in the model) factors of extinction in Cuyabeno (a) and Yasuní (b).
72
Figure 7. Spider monkey population trends under stochastic and deterministic (includes deforestation in the model) factors of extinction in Yasuní (this species does not occur in Cuyabeno).
73
a
b Figure 8. Comparison of jaguar population trends under stochastic and deterministic (includes deforestation in the model) factors of extinction in Cuyabeno (a) and Yasuní (b).
74
a
b Figure 9. Comparison of tapir population trends under stochastic and deterministic (includes deforestation in the model) factors of extinction in Cuyabeno (a) and Yasuní (b).
75
a
b Figure 10. a) Comparison of anteater population trends under current rates of deforestation in Cuyabeno, Yasuní, and the reserve network; b) Comparison of extinction risk curves in Cuyabeno, Yasuní, and the reserve network (error bars are not shown due to the scale of the figure).
76
a
b Figure 11. a) Comparison of howler monkey population trends under current rates of deforestation in Cuyabeno, Yasuní, and the reserve network; b) Comparison of extinction risk curves in Cuyabeno, Yasuní, and the reserve network (error bars are not shown due to the scale of the figure).
77
a
b Figure 12. a) Spider monkey population trends under current rates of deforestation in Yasuní; b) Extinction risk curve (error bars are not shown due to the scale of the figure).
78
a
b Figure 13. a) Comparison of jaguar population trends under current rates of deforestation in Cuyabeno, Yasuní, and the reserve network; b) Comparison of extinction risk curves in Cuyabeno, Yasuní, and the reserve network (error bars are not shown due to the scale of the figure).
79
a
b Figure 14. a) Comparison of tapir population trends under current rates of deforestation in Cuyabeno, Yasuní, and the reserve network; b) Comparison of extinction risk curves in Cuyabeno, Yasuní, and the reserve network (error bars are not shown due to the scale of the figure).
80
Figure 15. One-way analysis of variance results comparing persistence probability among the protected areas and the reserve network.
81
Figure 16. Cuyabeno-Yasuní reserve network showing current protected area borders and proposed land additions (a and b) and eastern Huaorani Ethnic Reserve (c).
