Natural population die-offs: lessons for conservation
Eric Isaí Ameca y Juárez
A thesis submitted for the degree of Doctor of Philosophy
from the Division of Biology, Department of Life Sciences,
Imperial College London
Contents
Abstract
Declaration
Acknowledgements
Chapter 1 12 Natural population die-offs: causes and consequences for terrestrial mammals
Extreme natural events and biodiversity loss
Defining abnormally high population losses Extreme natural events, species biology & incidence of natural population die-offs Predicting vulnerability to natural population die-offs Natural population die-offs, climate change & the implications for conservation
Chapter 2 23 Identifying species biological characteristics shaping natural population die-offs Abstract Introduction Methods Results Discussion Chapter 3 37 Assessing exposure to extreme climatic events for terrestrial mammals Abstract Introduction Methods Results Discussion
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Chapter 4 50 Vulnerability assessment to cyclone-driven population declines: an implementation for Mexican terrestrial endangered mammals
Abstract Introduction Methods Results Discussion Chapter 5 62 Quantifying the impact of cyclones on extinction probability of a primate population: insights into island extirpations Abstract Introduction Methods Results Discussion Conclusions 78 List of references 83 List of tables 114 Table 1.1 Examples of extreme natural events reported in the literature as related to recent natural population die-offs 114
Table 2.1 Summary of species’ biological traits predicted to shape severity of natural population die-offs (NPDO severity) 115
Table 2.2 PGLS model of Home range size and Adult body mass as predictors of NPDO severity 116
Table 2.3 PGLS model of Home range size and Foraging strategy as predictors of NPDO severity 117
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Table 2.4 PGLS models of severity of NPDO severity as ranked by the Akaike Information Criterion corrected for small samples (AICc) 118
Table 3.1 “Threatened” and “Non-Threatened” Terrestrial mammals at high exposure to cyclones and droughts by taxonomic Order 119
Table 4.1 Indicators of exposure, sensitivity and non-adaptability used to assess vulnerability to cyclone-driven population declines for non-volant mammals classed in danger of extinction by Mexican law (Nom-059-Ecol-2001) 120
Table 4.2 Non-volant mammals (n = 19) listed in danger of extinction by Mexican law (Nom-059-Ecol-2001) assessed for cyclone-driven population declines 121
Table 5.1 Parameters used in the PVA of A. p. mexicana population in Agaltepec Island 122
Table 5.2 Scenarios used in the PVA of A. p. mexicana population in Agaltepec Island 123
Table 5.3 Average population abundance of A. p. mexicana population for the scenarios 124 used to estimate quasi-extinction risk
Table 5.4 Quasi-extinction risk of A. p. mexicana population in Agaltepec Island 125
Table 5.5 Sensitivity analysis for the scenarios of different cyclone frequency and no cyclone impacts affecting quasi-extinction risk 126
List of figures 127 Figure 1.1 Theoretical example of vulnerability to NPDOs of black howler monkey (Alouatta pigra) in the Yucatán Peninsula exposed to cyclones 127
Figure 3.1 Global pattern of terrestrial mammals with significant exposure to cyclones (panel a) and droughts (panel e) classified by risk status following the IUCN Red List criteria 128
Figure 3.2 Global pattern of “Threatened” and “Non-Threatened” terrestrial mammals at high exposure to cyclones and droughts 129
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Figure 4.1 Criterion for assessing vulnerability to cyclone-driven population declines for non-volant mammals listed in danger of extinction by Mexican law (Nom-059-Ecol-2001) 130
Figure 4.2 Cyclone activity in Mexico for the period 1980-2006 131
Figure 4.3 Non-volant mammals categorized in danger of extinction by Mexican law (Nom-059-Ecol-2001) (n = 19) grouped by taxonomic Order assessed for vulnerability to cyclone-driven population declines 132
Figure 4.4 Spatial pattern of non-volant mammals categorized in danger of extinction by Mexican law (Nom-059-Ecol-2001) assessed for vulnerability to cyclone-driven population declines 133
Figure 4.5a Hierarchical agglomerative cluster analysis of the six indicators used to assess vulnerability to cyclone-driven population declines in non-volant Mexican mammals (n = 20) listed in danger of extinction by Mexican law (Nom-059-Ecol-2001) 134
Figure 4.5b Hierarchical agglomerative cluster analysis for non-volant mammals listed in danger of extinction by Mexican Law (Nom-059-Ecol-2001) 135
Figure 5.1 Transition matrix for Agaltepec population using demographic data for the period 1988-2002 136
List of Boxes 137 Box 1.1 Conceptual model for assessing species’ vulnerability to natural population die-offs 137
Appendices 138 Appendix Table S1 Dataset with the 72 natural population die-off s used in PGLS analysis (Chapter 2). Database online: www.dropbox.com/sh/4mc1xawn60q2p8t/VwXnWdw76L
Appendix Table S2 Selection of evolutionary models using Akaike Information Criterion (AIC) prior PGLS modeling of NPDO severity.
Appendix Table S3. a,“Threatened” and b, “Non-Threatened” terrestrial mammals with high exposure to cyclones and droughts (Chapter 3)
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Appendix Table S4. Terrestrial mammals categorized at threat of a, storm/floods (n = 69) and b, droughts (n = 81) on the IUCN Threats Classification Scheme Version 3.0. (Data accessed July 2012) (Chapter 3)
Appendix Table S5. Non Volant terrestrial mammals (n = 19) listed in danger of extinction by Mexican laws assessed for cyclone-driven population declines (Chapter 4)
Appendix Figure S1 Spatial correlogram Moran’s I plotted against distance classes (spatial lags) in the residuals (Chapter 2)
Appendix Figure S2. Moran scatter plot for evaluation of spatial autocorrelation in the residuals (Chapter 2)
Appendix Figure S3. Plots of quasi-extinction probabilities for Agaltepec Island population (Chapter5).
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Abstract
Population die-offs generally refer to the loss of a large number of individuals in a population over a short time interval. When die-offs are attributable to one or more extreme natural events, they are referred to as “natural” population die-offs (also abbreviated as “NPDOs”).
Despite the increased number of studies reporting natural population die-offs we still have only a limited understanding of the processes driving such phenomena. The first section of this thesis, a review chapter, highlights the variety of pathways by which extreme natural events (including extreme climatic events) can drive episodes of high mortality. It also introduces a framework for assessing vulnerability to population declines applied to terrestrial mammals. The vulnerability to these declines can be heightened if species have high susceptibility and/or a poor adaptive capacity. Using a database of 72 natural population die- offs (NPDOs) of terrestrial mammal species we tested a set of hypothesis related to species’ biological traits (body mass, home range, foraging strategy, territoriality) the main aim of chapter two investigate whether these traits are significant predictors of the observed degree of population losses caused by extreme natural events (“NPDO severity”).
In parallel with intrinsic biology, the degree of exposure to threat processes determines the fate of species and their constituent populations. In this context, chapter three maps the degree of overlap between the extant geographical distribution of terrestrial mammals and areas of recent past exposure to cyclones and droughts. Species whose distributions have a high overlap with extreme climatic events are expected to be at greater threat than currently recognized by their biology alone. Chapter four then introduces a vulnerability assessment to cyclone-driven population declines based on the interactions between species biology, the species’ extant geographical range and recent past exposure to tropical cyclones. This is with the aim of determining species that are both, intrinsically vulnerable and highly exposed. This objective is addressed for the 19 terrestrial mammals listed as critically endangered by
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Mexican Law: Among Mexican mammalian fauna they stand already at the first line to disappear due to human-mediated impacts and extreme climate-related phenomena can increase such risk. Therefore there is a critical need to identify those further jeopardized by climatic impacts and implement comprehensive management practices to protect them.
Great changes in the environment have the potential to generate large mortality events.
However, the processes by which extreme climatic events shape population growth and trajectory are overlooked. In the final chapter of this thesis a population viability analysis is performed for a predator-free, well-monitored and undisturbed primate population harboured in Agaltepec Island, Los Tuxtlas, Mexico. This translocated population share ecological and demographic features within the range of variation reported for the subspecies in Los Tuxtlas region. In light of the expected increment in the frequency of extreme climatic events (e.g., cyclones for Los Tuxtlas region), systems as the one presented here can help to understand demographic consequences that extreme agents of disturbance could trigger on isolated populations embed in hostile landscapes where data is still not available.
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Declaration
All work presented in this thesis is my own. Where data sources were collected by others, I cite the original repositories/authors with the following acknowledgments.
Chapter 1. Published in Trends in Ecology and Evolution as lead author. The idea of the research theme natural population die-offs came from Guy Cowlishaw (co-author). I carried out the literature review on this topic and was responsible of the presented vulnerability framework to assess die-offs which was improved thanks to thoughtful comments of Nathalie Pettorelli and Georgina M. Mace (co-authors). Constructive criticism to the final manuscript came from lab members: Alienor Chauvenet, James Duffy and William Cornforth.
Chapter 2. Accepted for publication in Animal Conservation as lead author. I was responsible of the data collection on population die-offs for the species assessed as well as of the analysis and writing of the manuscript. Georgina M. Mace, Guy Cowlishaw and Nathalie Pettorelli provided guidance in the analysis of data. Kamran Safi provided the R code to extract the species phylogeny used in this chapter. Alana Maltby and Nathalie Cooper provided valuable suggestions on modelling techniques. Alienor Chauvenet, James Duffy, and William Cornforth provided valuable comments on early drafts of the research.
Chapter 3. Published in Conservation Letters as lead author. I was responsible for the design, analysis and writing of the paper. Nathalie Pettorelli and William Cornforth (co- authors) provided guidance in data processing. Georgina M. Mace and Guy Cowlishaw (co- authors) contributed thoughtful criticism in defining the cut-offs to assess exposure to extreme climatic events.
Chapter 4. In preparation for Biodiversity and Conservation as lead author. Georgina M. Mace, Guy Cowlishaw and Nathalie Pettorelli contributed in the design of the vulnerability ranking of my framework to assess cyclone-driven population declines. The chapter was written by me with guidance of my three supervisors.
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Chapter 5. This chapter is in preparation to be submitted to Endangered Species Research as lead author. I was responsible of the design, analysis and writing of this study. Georgina M. Mace and Nathalie Pettorelli provided guidance in the PVA modelling using the RAMAS software.
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Eric Isaí Ameca y Juárez
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Acknowledgements
Throughout my PhD Prof. Georgina M. Mace, Dr. Nathalie Pettorelli and Dr. Guy
Cowlishaw provided guidance and valuable criticism of the concepts and methods contained in this PhD research which was supported by the National Council of Science and
Technology of Mexico (Ref. 209160) the World Wildlife Fund (Ref. RM60), the Secretary of
Public Education of Mexico (DGRI-SEP), and the University of Veracruz.
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Chapter 1
Natural population die-offs: causes and consequences for terrestrial mammals
The manuscript in this chapter was published as: Ameca y Juárez, E.I., Mace, G.M., Cowlishaw, G., & Pettorelli, N. (2012) Natural population die-offs: causes and consequences for terrestrial mammals. Trends in Ecology and Evolution, 27, 272-277.
Extreme natural events and biodiversity loss
Although there is no strict consensus on how to measure biodiversity and biodiversity loss, most conservation decisions are taken at the species level on both global and regional scales
(Mace 2004; Isaac et al. 2007; Pimm & Jenkins 2009; Ceballos et al. 2010). Species-focused conservation strategies alone, however, may overlook threats operating at other levels of biological organisation, such as the population level. Processes operating at the population scale can generate significant bottom-up impacts on biodiversity and so compromise the optimal provision of goods and services to human society (Ceballos et al. 2010). To improve the efficacy of conservation efforts, it is necessary to better understand these underlying population processes and how they relate to patterns in species extinctions (Cowlishaw et al.
2009; Collen et al. 2009; Ceballos et al. 2010).
Fluctuations in the size of a population are the reflection of the birth, death and migration dynamics of its individual components (Benton et al. 2006). Such dynamics are shaped by density-dependent mechanisms, variation in individual characteristics and environmental variability (Benton et al. 2006; Vasseur & McCann 2007). Extreme natural events, such as droughts, wildfires, hurricanes, insect infestations or epidemics, are one such component of variability in environmental conditions, to the extent that they are classified as a subtype of natural hazards by the United Nations International Strategy for Disaster Reduction
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(UNISDR 2009; http://www.preventionweb.net; Table 1.1). These events can result in the loss of numerous individuals, which in turn is expected to affect directly and indirectly the dynamics and overall viability of wildlife populations (Lande et al. 2003). This is especially true when extreme events are coupled with other mechanisms, such as density dependence or increased anthropogenic pressure. Drastic population size reductions can leave populations more susceptible to additional threat processes (such as Allee effects, increased demographic stochasticity, higher risk of genetic bottleneck, and habitat loss or habitat fragmentation) that can ultimately lead to population extinction (Fagan & Holmes 2006; Courchamp et al. 2008).
Abnormally high population losses can also have consequences that reach far beyond the population scale, with a sharp reduction in the size of the population of one species potentially leading to an extinction cascade (Thébault & Fontaine 2010). In ecological networks with strong species interactions, major changes in the population dynamics of one species may alter the entire stability of the network (Waite et al. 2007). If, in addition, the species experiencing the drastic loss of individuals is a keystone component of an ecosystem, its decline could reshape the entire structure and function of that ecosystem (Estes et al.
2011).
There is currently a lack of knowledge on the mechanisms through which extreme natural events affect biodiversity in the form of populations, species and ecosystems (Yu et al. 2010;
Thomas et al. 2004; Jentsch & Beierkuhnlein 2008; Keith et al. 2008). In particular, little is known about how wildlife populations inhabiting areas already impacted by anthropogenic disturbances might respond to extreme natural events. This is unfortunate, as global and regional climate change models predict an increase in both the frequency and intensity of climatic anomalies (Krawchuk et al. 2009; Grossmann 2009; Wang et al. 2010; Sienz et al.
2010) and, thus, of anthropogenic extreme events that resemble extreme natural events. The
International Union for Conservation of Nature (IUCN) recently assessed the susceptibility of
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(e.g. dispersal ability or habitat specialisation) associated with enhanced susceptibility to climate change. Here, we introduce a framework for such an initiative, aiming at identifying species whose populations are particularly susceptible to abnormally high losses under natural disturbances. We believe that such information could be used to infer how the extinction risk of species might increase as anthropogenic extreme events become more frequent and more severe. Before introducing our framework, we propose a workable definition of abnormally high population losses, and explore the links between the biology of species and the incidence of these population losses.
Defining abnormally high population losses
“Population die-offs” generally refer to the loss of a large number of individuals in a population over a short time interval. When die-offs are attributable to one or more extreme natural events (Table 1.1), they are referred to as “natural” population die-offs. A growing number of studies report natural population die-offs in terrestrial mammals, yet few of them provide a quantitative definition of what a “natural population die-off” actually is. Without a clear definition that can be used across taxa, the usefulness of the ‘natural population die-off’ concept might be limited for conservation purposes.
The first attempt to provide an across-species, quantitative definition of natural population die-off was provided by Young (1994) who defined them as “monotonic drop in population numbers that occurs between two or among more than two population surveys with at least a
25% reduction in population size”. Later, Reed et al. (2003) proposed another definition, namely, “any 1-year decrease in population size of 50% or greater”. As illustrated by these definitions, there are various challenges associated with defining natural population die-offs.
The first issue concerns the setting of a temporal span threshold, because a population loss of
14 | P a g e sufficient magnitude could occur over a range of potential timescales. In the context of the ecology and dynamics of the population, setting such thresholds should allow natural population die-offs to be differentiated from other patterns of mortality. The second problem involves defining a mortality threshold beyond which a population loss is considered to be a die-off. Choosing a fixed mortality threshold to identify die-offs (as proposed by Young and
Reed) overlooks the fact that the same population loss can be more severe for some species than for others owing to differences in their life histories (Liow 2009; Taylor et al. 2006;
Worden et al. 2010). In the context of long-term persistence, populations of long-lived species with late sexual maturity and low reproductive rates may have less chance to recover than might species with fast life-history strategies (Frankham & Brook 2004).
A workable definition of natural population die-off should thus ideally take into account the variability in average mortality rates for a given population and time interval. The importance of taking into account differences in life histories while comparing susceptibility to die-offs across species was previously acknowledged by Reed et al (2003) who compared the frequency of natural population die-offs in vertebrates by dividing the number of die-offs
(50% population decrease) observed during a census period by the approximate number of generations elapsed for that same period, for each species considered. We therefore propose to reformulate the definition of natural population die-off as: a 1-year decline in the number of individuals within a population derived from one or more extreme natural events, where individual losses increase by at least 25% in comparison to that expected from the annual average mortality rate reported for the species. We aim here to formulate a definition that could help assess the vulnerability of populations to natural population die-offs, as well as making it possible to consider such vulnerability as a component of the current risk for a species when estimating its potential to become extinct. This quantitative definition attempts to differentiate drastic population size reduction from background population variability,
15 | P a g e while taking into account the fact that the same population loss associated with the impact of an extreme natural event can be more harmful for some species than for others. In accordance with Reed and colleagues, it moreover supports the decision to exclude die-offs reported over just 1 year. This exclusion is based on the assumption that a population could experience a further decline and recover, or vice versa, owing to: (i) processes normally shaping population size variation (e.g. immigration or emigration of individuals); (ii) human intervention (e.g. harvesting); or iii) the combined effect of natural and anthropogenic disturbance (e.g. habitat change and food shortage; drought and hunting).
Extreme natural events, species biology & the incidence of natural population die-off
Comparative analyses have shown that species extinction risk is shaped by intrinsic and extrinsic factors (Mace et al. 2008; Cowlishaw & Dunbar 2000; Fisher et al. 2003; Collen et al. 2006). Applying such reasoning to population extinction risk, the probability for a population to experience a natural die-off is likely to be a function of the nature of the extreme natural event affecting this population, as well as biological differences among species (Cardillo et al. 2008; Price Gittleman 2007). What might be a “catastrophe” for some species could be a “bonanza” for others. For example, with respect to extrinsic changes in key ecological resources associated with natural population die-offs, Widmer et al. (2004) found that the winter hurricane Lothar that devastated woodlands in northeast France in 1999 did not increase mortality rates of roe deer (Capreolus capreolus) but rather increased the availability of one of its principal winter foods. Conversely, a population of black howler monkeys (Alouatta pigra) inhabiting a tropical forest of Belize was severely deprived of its primary food items by hurricane Iris (Pavelka & Behie 2005) resulting in a 42% population loss.
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Intrinsic biological features such as body mass, diet breadth or dispersal capacity have already been shown to affect species extinction risk (Cowlishaw & Dunbar 2000; Purvis et al.
2000; Cardillo et al. 2005; Fritz et al. 2009). Such traits could also be involved in shaping
"vulnerability" to occurrence of natural population die-offs. During a wild fire, for example, survival should depend initially on the ability to cope with the disturbance: this can be achieved through good dispersal abilities or through access to refuges (Lyon et al. 2000;
Cunningham & Ballard 2004; Peres et al. 2003; Liow et al. 2009). Species with specialized diet may then experience malnutrition due to the local post-fire reduction of key food resources (Lunney et al. 2004; Barlow & Peres 2008). This should lead to populations with limited dispersal abilities and high habitat specialization to be more at risk to undergo population die-offs if a wildfire occurs than species with high dispersal abilities and low habitat specialization. Species body mass may also matters since it is tightly correlated with these and other intrinsic traits in shaping species’ extinction risk. For example, Cardillo et al.
(2005) found that large mammals are generally more extinction-prone due to anthropogenic factors with large species requiring larger home ranges and typically occurring at low densities making individuals more exposed to hunting or derived effects of habitat fragmentation. However, pertaining to population losses not directly related to human activities, these ecological traits strongly related with being large sized may actually reduce physical exposure to extreme natural events and thus decrease vulnerability to population die- offs. For the hypothetical example of a wild fire, populations of large mammals (e.g., Ursus arctos) are more likely to avoid fire-related population die-offs through a combination of (i) efficiency in dispersing to safe areas, (ii) a potential access to a variety of alternative resources and (iii) low intraspecific competition for such resources given their distinctive spacing and mutual avoidance between individuals.
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Social ecology could then play a role in shaping the occurrence of natural population die- offs. In highly sociable species, individual survival can rely upon the maintenance of large social units (Cowlishaw & Dunbar 2000; Fisher et al. 2003; Courchamp et al. 2008). Yet such units can potentially exacerbate disease outbreaks when the successful transmission of a pathogenic agent within a population depends on the frequency of contact between individuals (De Castro & Bolker 2005; Kuiken et al. 2006). Territoriality can moreover facilitate transmission by increased exposure to pathogens prevailing within defended areas
(Nunn & Dokey 2006). Bearing in mind the above, territorial organisms aggregated in dense populations might be particularly vulnerable to natural population die-offs driven by disease outbreaks. Leadership and group coordination might matter too. A recent study published by
Foley et al. (2008) showed that African elephant (Loxodonta africana) family groups with older matriarchs had fewer calf losses than groups with young matriarchs during an atypical severe dry season. This observation led the authors to hypothesize that older individuals which had acquired behavioural coping strategies from past exposures to drought may have informed the behaviour of their group, increasing its chances to persist when similar conditions arose.
Predicting vulnerability to natural population die-offs
With increasing risks and challenges posed by climate change, increased attention has been devoted to assessing and predicting the vulnerability of human societies to extreme natural events, including extreme climatic events (IPCC 2007, IPCC 2012). Such knowledge could contribute to the development of an integrated framework for predicting vulnerability to natural die-offs in wildlife populations. According to such recent works (e.g., Birkmann
2006; Harmeling 2010; Benzie et al. 2011) vulnerability to natural population die-offs can be considered as a function of three main components: sensitivity, exposure, and adaptability
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(Box 1.1). Population sensitivity can be expected to be shaped by species traits (e.g. body size, home range area, and sociality). Adaptability (in a non evolutionary sense) will refer, in this case, to plasticity in traits (Geiser & Turbill 2009), that is, whether or not mechanisms such as behavioural and/or physiological flexibility exist to allow a given population to avoid or withstand the effects of particular extreme natural events (Canale & Henry, 2010; Munn et al. 2010). Finally, exposure is expected to indicate how often an extreme natural event will affect a given population and is largely a feature of the environment in which the population lives.
The exposure that a particular population is likely to face depends on prevailing conditions, many of which are exacerbated by human activities, and can therefore change quite rapidly. For example, the likelihood of fire or disease outbreaks may be increased by land use change. Therefore even if these extreme natural events are hard to predict, the overlay of information on regions linked to high exposure risk with information on the distribution of those species that are sensitive and have low adaptive capacity will allow identify areas where the background level of risk for natural population die-offs to occur is elevated. Priority can then be given to develop strategies enhancing populations’ resilience to extreme natural events in these areas. Such strategies may include the creation of a network of waterholes (where droughts are a high risk), vaccinations (where disease is high risk) and fire breaks/clearing of undergrowth (where wildfires area a risk). However, there will be particular circumstances where the direct impacts will be more difficult to mitigate (e.g., hurricanes). Importantly, a key aspect to this “vulnerability framework” is the need to take into account any spatial variation in the local attributes of the environment, as populations of species exposed to similar extreme natural events but occurring in different habitats may display different patterns of vulnerability (Figure 1.1).
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Vulnerability to natural population die-offs can contrast radically across populations of a single species depending on the population’s location relative to the source of disturbance; intrinsic features of the phenomenon experienced by various populations such as intensity and duration; and differences in populations’ exposure shaped by differences in habitat structure. For example, populations of the black howler monkey (Alouatta pigra) have been reported to occupy areas of evergreen rainforests and mangroves among other vegetation types within the Yucatán Peninsula, Mexico (Serio-Silva et al. 2006). This region is frequently exposed to cyclones which, although of short life-span, have the potential to generate substantial ecological disturbance (Boose & Foster 2006). As with other members of the genus Alouatta, howlers are highly arboreal, making them particularly sensitive to cyclones in any type of habitat. Yet an ecological advantage of howlers is their ability to survive on very low-quality diets (Pozo-Montuy & Serio-Silva 2007). Such high ecological adaptability may assist in the fulfilment of their nutritional needs during harsh conditions, including those occasions when cyclones cause extensive defoliation and tree mortality
(Gilman et al. 2008). Mangrove forests are located closer to the shore than rainforests, and are thereby directly exposed to cyclones (Whigham et al. 2010). Howler populations can thus be expected to be better buffered against the impacts of cyclone damages in rainforests than in mangroves, given their natural inland horizontal zonation. Physical protection for howlers is then substantially lower in mangroves than in rainforests (Figure 1.1, Case II), with vegetation composition in mangroves being less heterogeneous, and wood density being relatively low, resulting in reduced resistance against strong winds (Imbert et al. 1996).
Altogether, howlers in rainforests (Figure 1.1, Case I) might thus be less exposed to cyclones than howlers in mangroves. Anthropogenic pressure on howler populations’ habitat will also affect its vulnerability to cyclones. Howlers living in habitats degraded as a result of human activities (e.g., small fragments of secondary rainforest) and impacted by cyclones are
20 | P a g e expected to spend more time travelling the ground, making them more prone to additional threats such as predation than howlers living in continuous and non-degraded habitats. Based on these scenarios, and following our theoretical framework, vulnerability to natural population die-offs is expected to be greatest for howler populations occupying degraded rainforest and mangrove habitats (Figure 1.1, Case III), since the species’ high adaptive capacity would not be sufficient to increase individuals’ resilience to the disturbances generated during or in the aftermath of a hurricane.
Natural population die-offs, climate change and implications for conservation
With extreme natural events of anthropogenic origins expected to occur more frequently and with greater intensity in the coming decades, there is increasing awareness that such events represent a growing threat to biodiversity (Lee & Jetz 2010; Brook et al. 2008). This might be particularly true for populations currently under pressure owing to human-related processes, such as habitat loss and degradation, overexploitation or spread of invasive species. Despite the increased importance of extreme events as drivers of biodiversity loss across the globe, current understanding of what processes drive such phenomena is limited and the ability to anticipate population die-offs is low. Yet, determining which sets of factors affect the vulnerability of a population to undergo a natural die-off would enable the important distinction to be drawn between those populations that are experiencing and might continue to experience severe losses (and thus are in need of tailored response strategies) and those that are less vulnerable. This work therefore underlines the critical need to advance the current ability to link climatic and ecological modelling at subglobal scales if researchers are to provide the most appropriate strategies to mitigate the expected negative impacts of climate change on biodiversity at the scale at which prevalent threat processes compromise the provision of goods and services to human society (Ceballos et al. 2010).
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We propose a framework for assessing vulnerability to natural population die-offs. Such an exercise could be particularly valuable at the national scale, helping governments to identify populations most at risk (e.g. informing initiatives such as the National Red List).
This could also support previous efforts to assess the vulnerability of species to climate change (Foden et al. 2008) by providing a framework allowing the quantification, for each species, of the expected changes in IUCN risk categories resulting from the increasing occurrence and severity of anthropogenically caused extreme events that resemble extreme
‘natural’ events. This could involve, for example, assessing how increased occurrence and severity of extreme events may impact the geographical range size of a species and then translating such changes to changes in individual numbers. Although we have used terrestrial mammals as a focus here, we believe the proposed framework could be used as a tool to generate response strategies for other taxa. For those species whose populations already face such circumstances, there is an urgent need to incorporate their vulnerability to natural population die-offs in the current assessment of their extinction risk.
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Chapter 2
Identifying species’ characteristics associated with natural population die-offs in terrestrial mammals
The chapter is accepted for publication in the journal Animal Conservation as: Ameca y Juárez, E.I., Mace, G.M., Cowlishaw, G., & Pettorelli, N. Identifying species’ characteristics associated with natural population die-offs in mammals.
Abstract
Extreme natural events such as cold waves, droughts, floods or hurricanes can drastically impact wildlife populations, and are expected to become more frequent and more intense in the coming decades. When populations experience abnormally high declines within a short time interval due to such phenomena, the losses can be referred to as natural population die- offs (NPDOs). Although such events have been observed and population declines recorded, there is currently little known about which species might be most affected. Using a database of 72 NPDOs from 31 terrestrial herbivorous mammals, we modelled the effects of 4 biological traits (adult body mass, foraging strategy, home range area, territoriality) on the degree of population loss (severity) caused by extreme natural events. We found that the susceptibility to large NPDOs decreases with increased home range size for a given body mass. Foraging strategy was also found to be significantly associated with NPDO severity, with grazers and mixed feeders experiencing larger declines than browsers. Our analyses moreover suggested that wide-ranging browsers might be less susceptible to large NPDOs than browsers with small home ranges. Identifying the traits shaping high biological sensitivity and/or limited adaptive capacity to extreme natural events can help us to identify those populations most likely to become increasingly vulnerable to NPDOs, allowing tailored interventions to be implemented to avoid local extinctions. This will be of the utmost
23 | P a g e importance for those populations already experiencing high levels of anthropogenic impact and distributed in regions where exposure to extreme natural events is expected to increase in the coming decades.
Introduction
The current extinction crisis is a world-wide phenomenon driven directly and indirectly by the unsustainable use of the natural capital that biodiversity represents to human societies
(Vitousek et al. 1997). In response to this crisis, the scientific community has provided managers and practitioners with a wide array of strategies and tools targeted at quantifying losses, detecting symptoms of threat, reducing risk factors, and ultimately averting and/or mitigating the current speed of biodiversity loss (Pimm & Jenkins 2009; CBD 2010). Species are important indicators of biodiversity status (Butchart et al. 2010), but increases in extinction risk may not be the most appropriate signal of decay, given that a species can lose a great number of individuals and populations before becoming listed as globally threatened
(Yackulic et al. 2011). Conservation biologists therefore monitor population numbers as a means to highlight (i) potential species extinctions (Ceballos et al. 2010; Collen et al. 2011),
(ii) disruption of ecological processes, and (iii) loss of ecosystem goods and services (Luck et al. 2003).
Habitat destruction, overexploitation pollution and species invasion are considered the main causes of current and recent severe population declines and extirpations (MA 2005;
CBD 2010) and much of the scientific focus has been placed on these processes and their consequences. However, to date much less attention has been directed to the study of extreme natural events, which can generate sudden episodes of high mortality (Dunham et al. 2003;
Scorolli et al. 2006). Based on the terminology of the United Nations International Strategy for Disaster Reduction (UNISDR 2009), we define the term extreme “natural” events as
24 | P a g e phenomena arising from a variety from geological, meteorological, hydrological or biological processes (including those enhanced by anthropogenic climate change) that can negatively impact wildlife populations. Population losses caused by extreme natural events are referred to as natural population die-offs (hereafter NPDOs) but are rarely recorded in the conservation literature. NPDOs can have equal or even greater impacts on the viability of wildlife populations than direct anthropogenic processes. Being hard to predict, they typically take place suddenly and within a short time interval. Extreme natural events such as cold waves, cyclones, floods, heat waves or wildfires often cannot be prevented and populations of species most likely to be impacted will require radical models of intervention for reducing vulnerability to NPDOs. These types of population declines represent a latent, overlooked threat to species’ persistence, which might become much more significant in coming decades due to the anticipated increase in the frequency and severity of extreme events under anthropogenic climate change (IPCC 2012). NPDOs can take place if intrinsic biology proves to be insufficient to cope with frequent exposure to harsh environmental conditions (Ameca y
Juárez et al. 2012). Occurrence of NPDOs enhances the risk of populations’ extinctions: in worst-case scenarios as population numbers are reduced, the species’ historic geographic ranges will be progressively lost increasing susceptibility to pre-existing threats for remnant populations (Yackulic et al. 2011). Identifying biological traits predisposing species to
NPDOs can help us to complement existing conservation strategies in preventing population deletions and range extent.
Intrinsic biology has been shown to influence vulnerability to particular human-driven threatening processes at both species and population levels (Owens & Bennett 2000; Isaac &
Cowlishaw 2004; Cardillo et al. 2006). It is therefore possible that certain biological traits might be associated with lower resilience to extreme natural events. Previous studies have found that adult body mass, home range size, foraging strategy, and territoriality are
25 | P a g e significant indicators of extinction risk across a range of mammalian groups (Purvis et al.
2000; Isaac & Cowlishaw 2004; Cardillo et al. 2008). In this study, we derived a set of hypotheses related to these biological traits (Table 2.1) and investigated whether these traits are significant predictors of the observed degree of population losses caused by extreme natural events (NPDO severity). We focused our work on terrestrial mammals, given their worldwide distribution and the relatively good information available on their population size fluctuations.
Methods
NPDOs quantification and data search
The identification of potential traits promoting NPDOs is complicated by a range of different definitions in use as well as sampling techniques (Young 1994, Reed et al. 2003). Here, we define NPDO as a decline in a population within a year, derived from one or more extreme natural events, where individual losses are at least 25% higher than expected from the annual average mortality rate reported for the species (Ameca y Juárez et al. 2012). This definition exclude die-offs caused over a year based on the assumption that there could be more chances to observe population size variations due immigration or emigration of individuals (Schaefer et al. 2001), human interventions or a combination of several sources of disturbance (e.g., habitat change and food shortage: Struhsaker 1976; drought and human utilization: Ottichilo et al. 2001). We researched reports of NPDOs (as defined above) in the peer-reviewed literature published between 1945 and 2010, making use of the ISI Web of Knowledge
(www.isiknowledge.com). Titles, abstracts, and keywords were examined using systematic search strings related to population declines in mammals and extreme natural events. We selected only publications providing the following information: cause of the decline, percentage of population decline (which had to be at least 25% higher than the annual
26 | P a g e average mortality rate), approximate start/end dates of the decline, location, and methods used to collect the population data. We also used the compilation of die-offs reported by
Young (1994) and excluded from this compilation die-offs caused by diseases and/or reported for marine mammals, as these were out of the scope of our analysis. Likewise, we omitted any decline that was reported more than once (duplicates), as well as declines obtained from personal communications and/or associated with insufficient data, preventing us from determining whether the population decline was a die-off or not according to our definition.
Throughout our search for NPDOs, we aimed to find reports of a population which experienced a decline due to extreme natural events while being defined by the researchers as a geographic grouping of individuals occupying an area that can be clearly delineated by ecological and/or geographic factors. In this regard we assumed this was the case where this information was not explicitly mentioned by the researchers provided that ecological and geographic conditions of the area could support this assumption (e.g. the population is markedly separated from others as a consequence of factors shaping landscape impermeability). It is for this reason that we decided to discard die-offs reported for marine mammals as populations are much more difficult to delimit due to the greater permeability of the system they live in as compared with terrestrial mammals. In order to increase the detection of NPDOs, we checked through the papers collated from these two search procedures and tracked any citations of NPDOs that were not already in the database. These dataset contained 76 NPDOs across 35 terrestrial mammals belonging to the Orders
Artiodactyla, Carnivora, Diprotodontia, Primates, and Proboscidea. For the purpose of this study (Table 2.1), we excluded NPDOs identified for omnivores (information only available for 3 primate populations) and carnivores (information only available for 1 carnivore population), given their small representation in the dataset. Consequently, the resulting
27 | P a g e dataset contained 72 NPDOs pooling together 31 terrestrial mammal herbivores (See
Appendix Table S1, www.dropbox.com/sh/4mc1xawn60q2p8t/VwXnWdw76L).
Biological traits and NPDO severity
Home range size and adult body mass were extracted from the online database PanTHERIA;
Jones et al. 2009) whereas territoriality and foraging strategy where extracted from the
Walker's encyclopaedia of living mammals (Nowak 1999). Accordingly, home range size was defined as the size of the area within which everyday activities of individuals or groups are typically restricted. Adult body mass was defined as the mass in kilograms of an individual adult of the species. Territoriality was defined as the species ability of defending particular areas within its home range for exclusive use of shelters, foraging or breeding grounds. Foraging strategy was defined as the species’ preferred dietary selectivity for plants.
In this vein, species in our study were classified into three categories: grazers (species whose diet is mainly based on grass plant species), ii) browsers (species whose diet is mainly supported by broadleaf herbaceous forbs, shrubs, trees, and vines) and mixed feeders (species whose diet is based on graze and browse plant species without showing a marked preference of one over another). We pooled together mixed feeders and grazers based on their similar anatomical adaptations related to food intake, which contrast with those of browsers (Pérez-
Barbería & Gordon 2001). The response variable NPDO severity was defined as the proportion of population lost due to one or more extreme natural events. Examination of the frequency distribution of NPDO severity (expressed in percentage) revealed a leptokurtic pattern. As normal distribution has zero kurtosis, our raw data for NPDO severity required a transformation to correct for this pattern and an arcsine square root transformation (given in radian units) revealed to reduce this pattern achieving distribution more close to normal. In the same vein, home range size and adult body mass were +1 log-transformed, to stabilize the
28 | P a g e variance and ensure near normality and homoscedasticity in our models (Gotelli & Ellison
2004; Kleinbaum et al. 2008). Examination of residuals versus fitted values after transformations revealed a more consistent spread around cero ensuring near normality and homoscedasticity in our models. We discarded presence of collinearity among our variables using the variance inflation factor (VIF) (Heiberger & Holland 2004) using the “vif” function on R (VIF estimates > 1 < 2).
Statistical analyses
All the analyses were conducted using the “R” software (version 2.13.1; R Core
Development Team 2011). Because closely related species have biological similarity as a result of common ancestry (Harvey & Pagel 1991), they cannot be taken as independent data points. The non-independence of data due to shared evolutionary history was corrected for by incorporating phylogenetic information into the analyses (Martins & Hansen 1997; Paradis
2006). We generated a phylogenetic tree of the species included in this study using the
“ade4” library in R based on a modification of the phylogeny of mammals generated by
Bininda-Emonds et al. (2007) and provided by Fritz et al. (2009), which uses a more recent taxonomy. Because more than one NPDO was recorded for the same species, we represented the within-species data on NPDO severity pertaining to different populations as branches
(polytomies of length set to ~0) for each of the species (English et al. 2012). This generated
72 tips on the tree each corresponding to a documented NPDO. We used the Pagel's lambda test (λ) (Pagel 1999) to examine phylogenetic signal in continuous traits. λ is a branch length scaling parameter ranging from 0 (no phylogenetic signal) to 1 (reflecting phylogenetic signal in the form of a Brownian model of evolution). Phylogenetic signal was confirmed (both traits λ > 0.09) using the fit.Continuous function provided by the “geiger” package (Harmon et al. 2008). In parallel, the Fritz & Purvis’ (2010) D test (computed using the phylo.d
29 | P a g e function available in the “caper” package) revealed phylogenetic signal for the binary traits territoriality (D = -0.02) and foraging strategy (D = 0.11) (Orme 2012). There are a number of phylogenetic comparative methods available (Rohlf 2001; Purvis 2008; Bielby et al. 2009;
Freckleton et al. 2011). We used Phylogenetic Generalized Least Squares (PGLS) models, which are regarded as the most general and robust way of correcting for phylogenetic non- independence in data (Martins & Hansen 1997; Rohlf 2001; Freckleton et al. 2002). Using the Akaike Information Criterion, we determined that a Lambda model of trait change best described the evolution of NPDO severity computing maximum likelihood (Pagel 1999;
Orme et al. 2012) (see Appendix Table S2). Derived from our set of hypotheses (Table 2.1), we built PGLS models (using the pgls function in the caper package) in search of significant correlates of NPDO severity using p-values (p < 0.05). We began our analysis by examining the significance of each of the four biological predictors of NPDO severity singly as is being a practice in correlates of extinction risk for declining species (Purvis et al., 2000). In a second stage we built a maximal model and executed model simplification removing the most non-significant terms one at the time until reach a minimum adequate model (MAM)
(Crawley 2007). We used the adjusted r-square (R2) to quantify the amount of explained variation by the fitted PGLS models (Paradis 2006). Additionally, we performed Moran’s I
(Dormann et al. 2007) to check for spatial autocorrelation as our dataset included observations that took place within the same geographic areas. Values of Moran’s I range from -1 (indicating perfect dispersion) to +1 (perfect correlation). Moran’s I was computed using a permutation-based test (99 permutations at 5% significance level using the moran.cp function in the “spdep” package in R). We did not found spatial autocorrelation in the residuals (Moran’s I = -0.74) (see Appendix Figure S1 and S2), and therefore it was not necessary to correct for this type of spatial dependence.
30 | P a g e
Results
Distribution of NPDOs
Of the total 72 NPDOs examined in this study, 41 NPDOs were caused by droughts, 26 related to cold waves and the remaining by floods and a wildfire. Furthermore, NPDOs were not randomly distributed either among species (27 Artiodactyls, 3 Diprotodonts, and 1 species belonging to Proboscidea) or ecosystems: the largest concentration of documented
NPDOs was found in savannas and grasslands areas in the East and South parts of the African continent and concerned mainly Artiodactyls (See Appendix Table S1). This distribution pattern is coherent given that these regions (i) include a high number of Artiodactyls, and (ii) recurrently experience pronounced dry periods leading to increases in drought risk.
Correlates of NPDO severity
Focusing on the outputs from the univariate models, the hypotheses that NPDO severity is enhanced for grazers and mixed feeders (difference in intercept = 0.16 ± 0.08, t = 2.18, p <
0.05), and enhanced for species with small home range size (slope = -0.78 ± 0.19, t = -4.10, p
< 0.001) were both supported. We did not find significant effects of adult body mass or territoriality on NPDO severity singly (both p > 0.05). Our MAM revealed to be the one with the interaction between home range size and adult body mass (p = 0.003, Adjusted R² = 0.12;
Table 2.2). However the interaction between home range size with foraging strategy also proved to be significant (p = 0.03; Adjusted R² = 0.10; Table 2.3). A close examination of the parameter estimates associated with the p values of both interactions suggests that (1) the susceptibility of a population to undergo a large NPDO decreases with increased home range size for a given body mass; (2) home range size has little influence on NPDO severity experienced by mixed feeders and grazers; and (3) home range size significantly influences
31 | P a g e
NPDO severity for browsers, with wide ranging species experiencing less severe NPDOs than species with small home range sizes.
Discussion
As extreme events increase in frequency and severity, conservation planning of wildlife populations will need to take into account species-level characteristics shaping species’ vulnerability to NPDOs. This will permit more accurate risk assessment at the local scale focusing on populations requiring urgent intervention in order to avoid extirpations and contractions in range occupancy derived from direct and indirect impacts from natural events, anthropogenic threats or both in combination. Our study is a step towards acquiring this much needed information. The interaction between home range size and adult body mass was a main predictor of NPDO severity in our analyses, with severe NPDOs being less common for species with increased home range size for a given body mass. A plausible explanation of this finding is that animals ranging over small areas might experience increased loss of food supply and shelter due to extreme events. This might be particularly the case for large species
(Reynolds 2003; Fritz et al. 2009). The importance of the interaction between home range size and adult body mass in determining NPDO severity can however be expected to depend on the characteristics of the habitat (Ameca y Juárez et al. 2012). Interestingly, the described link between NPDO severity, home range size and body mass is unusual. A number of recent studies have reported a positive association between home range size and risk of population decline and extinction being mediated by direct anthropogenic forces, such as illegal hunting
(including bushmeat or poaching), habitat loss (including fragmentation and degradation) and spread of invasive species (Primates and Carnivora, Purvis et al. 2000; Chiroptera, Jones et al. 2003; Marsupials, Fisher et al. 2003). Our results suggest that the mechanisms underlying
32 | P a g e population losses from extreme natural events and direct anthropogenic forces are fundamentally different.
Foraging strategy was found to be significantly associated with NPDO severity, with grazers and mixed feeders experiencing larger declines than browsers. This can be partly explained by grasses being more vulnerable to extreme climatic stress than woody plants species (Codron 2007). Also, grazers and mixed feeders are anatomically and physiologically less efficient than browsers in acquiring and assimilating nutrients from woody plants
(Shipley 1999; Ekaya 2001; Pérez-Barberia & Gordon 2011). As a consequence, they might be outcompeted by browsers during episodes of acute food scarcity and thus become more susceptible to secondary threats such as predation or diseases (Owen-Smith & Cooper 1987;
Haynes 1988; Ekaya 1991; Mogotsi et al. 2011). Our results also revealed that foraging strategy (browsing versus mixed feeding and grazing) interacting with home range size had a different effect on NPDO severity. Specifically, the size of the home range is associated with
NPDO severity in browsers but not grazers and mixed feeders. The effect of home range size in browsers might relate to the amount of food resources available during extreme natural events and the level of foraging competition (Haynes 1988; Mogotsi et al., 2011; Jaber et al.
2013). Grasses are more abundant and widespread than browse vegetation with the former being the first to fade away during environmental stress (Owen 1982). In this scenario following an ENE, all grazers/mixed feeders are vulnerable regardless of home range size, but because browsers are more resistant they can reduce the severity of the NPDO they experience provided that they have sufficiently large home ranges.
Our findings could be further developed and improved upon in a variety of ways. First, it would be valuable to extend our analysis to a wider range of mammalian taxa once the necessary data are available. At present, our findings may have limited generality to other mammals that differ in life history and ecological strategy. Second, our analysis of NDPOs
33 | P a g e for terrestrial herbivorous mammals could also be expanded. This could be achieved by gathering more observations for these species and also by testing other candidate predictors that might influence NPDO severity. In this study, we followed the expectation that life history traits previously associated with mammal declines (such as those detailed in Purvis et al. 2000; Cardillo et al. 2008; Jones et al. 2009) could be expected to shape population vulnerability to NPDOs. We only considered a subset of these traits, and it could be argued that more could have been taken into account. However, some of these possible traits are generally assessed on a different scale, e.g. focusing on the species rather than population level. Additionally, some traits (e.g. gestation length, age at sexual maturity, and litter size) are more likely to be associated with the speed of recovery from low population density than to the sensitivity of a population to sudden episodes of mass mortality. Therefore, we advise considerable caution in the selection of predictors. It has also been suggested that physiology and behaviour should be considered when inferring extinction risk from extreme conditions related to climate change (Geiser & Turbill 2009; Liow et al. 2009). Indeed, the adaptive capacity conferred via physiological or behavioural flexibility might allow individuals to persist in situ during extreme conditions, lessening vulnerability to die-offs. Unfortunately, such adaptability traits (e.g., aestivation, dormancy, and torpor) were absent for the species in our dataset.
If certain traits increase a species vulnerability to severe NPDOs, then one might expect that species in areas with a history of exposure to extreme natural events should have a lower incidence of such traits and/or possess the adaptability to cope with such disturbances. Yet, for cyclones, there are reports of exposed species that possess biological flexibility to cope with cyclone impacts but that nevertheless suffer dramatic population losses (e.g., Tarnaud &
Simmen 2002; Waite et al. 2007). One may hypothesise that any short-term increase in the frequency and intensity of these extreme natural phenomena will increase the exposure of
34 | P a g e local species, to the extent that their intrinsic coping strategies will be compromised (Ameca y Juárez et al. 2013).
To prevent further biodiversity loss, a crucial task for conservation scientists is to determine the causes and consequences of population declines. The impact of climate change and particularly large-scale, earth-system extreme phenomena are expected to be major drivers of extinction in the next 100 years (Easterling et al. 2000; Thomas et al. 2004; Bellard et al. 2012). Although climate scientists have made enormous progress in predicting the frequency and intensity of extreme natural events (IPCC 2012), there is still a high level of uncertainty in estimating the probability of their occurrence (Jentsch 2006). Hence, increasing efforts will be needed to plan for the unknown, based on what we do know. In recent assessments intrinsic biological characteristics interacting with the degree of exposure to threat processes has been suggested as a means to assess vulnerability (Foden et al. 2008;
Dawson et al. 2011; Bellard et al. 2012). Certain regions of the world are known to be more likely to experience extreme natural events (including extreme weather) (Dilley et al. 2005) as well as experiencing more intense human encroachment (Davies et al. 2006). Intersections between both these processes may trigger an array of novel pressures upon ecological systems (Jiguet et al. 2011). As one of the consequences, species might become highly vulnerable to severe population losses driven by unprecedented synergies between natural and anthropogenic-extreme events. In this regard, exposed species that lack intrinsic adaptability while possessing traits associated with high sensitivity (such as those described here), might be less likely to survive population die-offs and hence be more at risk of local extinctions. Here we have begun the process of identifying those species traits that are associated with vulnerability to NPDOs. We hope that this information will help conservationists to identify those populations most at risk and implement tailored management actions to avoid local extinction. For example, translocation programs could be
35 | P a g e implemented to relocate populations with poor adaptive capacity against increased exposure to particular extreme events. Such conservation interventions may be of the utmost importance for those species already experiencing high human pressure while also located in regions where exposure to more frequent and intense extreme natural events is expected to increase in the coming decades as a result of global climate change.
Supporting Information
Appendix Table S1 Dataset with natural population die-off s used in PGLS analysis. Link to repository online: https://www.dropbox.com/sh/4mc1xawn60q2p8t/VwXnWdw76L
Appendix Table S2 Selection of evolutionary models using Akaike Information Criterion
(AIC) prior PGLS modeling of NPDO severity.
Appendix Figure S1 Spatial correlogram Moran’s I plotted against distance classes (spatial lags) in the residuals
Appendix Figure S2 Moran scatter plot for evaluation of spatial autocorrelation in the residuals
36 | P a g e
Chapter 3
Assessing exposure to extreme climatic events for terrestrial mammals
The manuscript in this chapter is published in Conservation Letters as: Ameca y Juárez, E.I., Mace, G.M., Cowlishaw, G., Cornforth, W., & Pettorelli, N. (2012) Assessing exposure to extreme climatic events for terrestrial mammals. Conservation Letters, Early view online (doi: 10.1111/j.1755-263X.2012.00306.x)
Abstract
There is robust evidence that climate change will modify the frequency and intensity of extreme climatic events. The consequences for terrestrial biota may be dramatic, but are yet to be elucidated. The well-established IUCN Red List does not, for example, include any explicit quantification of the current level of exposure to extreme climatic events in any species-based risk assessment. Using globally distributed data for cyclones and droughts as well as information on the distribution of 5,760 terrestrial mammals (species and subspecies) we: (i) define mammals with significant exposure as those with an overlap of at least 25% of their extant geographic range with areas that have been impacted by either cyclones or droughts; and (ii) pinpoint those with ≥75% overlap as being at the highest exposure.
Although a species’ risk of negative impacts from extreme climatic events depends not only on its exposure but also its intrinsic sensitivity and adaptive capacity, identifying taxa currently exposed can help to (i) reduce the uncertainty in identifying species least likely to be resilient to future impacts and (ii) complement extinction risk assessments and provide a more informed evaluation of current conservation status, to better guide management.
37 | P a g e
Introduction
Evidence is accumulating that the current increase in global temperatures will lead to changes in the frequency and intensity of extreme climatic events in the coming decades (IPCC 2012).
Such changes may have detrimental consequences for the Earth’s biota (Parmesan et al. 2000;
Jiguet et al. 2011). In terrestrial mammals, severe population declines following such phenomena have been reported for a variety of species (Caughley et al. 1985; Solberg et al.
2001; Dunham et al. 2003; Pavelka et al. 2003; Gordon et al. 2006; Scorolli et al. 2006;
Miller & Barry 2009; Worden et al. 2010). It is expected that exposed species whose biology makes them more susceptible and/or unable to adapt promptly to changes in the frequency and intensity of extreme climatic events will be those most vulnerable to this source of disturbance (Ameca y Juárez et al. 2012). Cyclones and droughts are examples of such natural forcing for which historical data and state-of-the-art climate change modelling suggest that some areas of the world have experienced a trend to more intense or frequent events, a trend which might continue in the future depending on the region and season
(Seneviratne et al. 2012 and references therein).
Despite a limited understanding of the mechanisms shaping extreme climatic events, there is consensus that the severity of their impacts strongly depends on the level of exposure to them (IPCC 2012). “Exposure” has been defined as “the nature and degree to which a system is exposed to significant climatic variations” (IPCC 2001). With this rationale, a species’ exposure to extreme climatic events can be described as the degree of contact (overlap) between the geographic area within which a species occurs and the spatial extent of extreme climatic events over a given time period. It follows that the greater such area of overlap, all else being equal, the greater the probability that a species will be affected.
Exposure by itself does not necessarily equal risk. The overall risk of a species experiencing negative impacts from climate change, including extreme events, is expected to
38 | P a g e depend not only on exposure but also the species’ intrinsic characteristics and adaptability to disturbance (Foden et al. 2008; Ameca y Juárez et al. 2012). Future changes in the location and intensity of extreme climatic events are complex to predict accurately (Bader et al. 2008;
Ghil et al. 2011; Seneviratne et al. 2012), but records of current frequency distributions for specific types of extreme climatic events are sufficient for the identification of areas where the background level of exposure is elevated. In this context, identifying taxa recently exposed can help to identify those species likely to possess less resilience to impacts in the near future and use this information to complement extinction risk assessments and guide management actions.
Currently, risk assessments for mammals in the IUCN Red List (IUCN 2008) are based on a categorisation that incorporates continuing, expected or anticipated threats, but does not reflect extreme climatic events in any systematic way (Mace et al. 2008). Hence in this study, we use geographic ranges of species and subspecies of volant and non-volant terrestrial mammals (hereafter, terrestrial mammals), risk status data from the IUCN Red List
Assessment (Version 3.1), and observed frequency distribution data of cyclones and droughts to: (i) determine terrestrial mammals at significant exposure to either cyclones or droughts, and identify the geographic patterns in exposure; and (ii) pinpoint terrestrial mammals at high exposure with a particular focus on those classified as “Threatened” or “Non-Threatened” by the IUCN. We defined “significant” exposure as an overlap of at least 25% between a species’ extant geographic range and areas impacted by cyclones or droughts. Similarly we defined “high” exposure when such a species’ range overlap with areas impacted by either cyclones or droughts was equal or greater than 75%. We focused on terrestrial mammals because all known species (and a large number of subspecies) have been assessed against the
IUCN Red List criteria and their geographic distribution delimited.
39 | P a g e
Methods
Data sources
We obtained from the 2008 IUCN Red List assessment (Accessed November 2011) (IUCN
2008) the distribution maps in shapefile format for species and subspecies of terrestrial mammals (n = 5,798). Species’ distributional ranges have been commonly used as indicators to detect symptoms of decline and extinction risk from multiple threats (Cardillo et al. 2008;
Hockey & Curtis 2009; Davidson et al. 2009; Collen et al. 2011). However, species are unlikely to be evenly distributed throughout their range. In the Red List assessment, a given species distribution map takes the form of range polygons linking known areas where each polygon is associated with a particular level of confidence. We only used range polygons for which presence was coded as “Extant”, as these reflect areas where occurrence is most likely
(IUCN 2008) By focusing on species’ extant distributional areas (rather than the entire species’ range) we aimed to avoid overestimating the degree of exposure. Extinction risk was assessed using the IUCN Red List threat categories version 3.1 (Accessed December 2011).
We refer species and subspecies currently recognized as Critically Endangered, Endangered, and Vulnerable as “Threatened”, while Least Concern and Near Threatened species and subspecies are referred to as “Non-Threatened”. Range polygons of Data Deficient mammals and/or without full Red List assessment were kept in the analyses if these have range polygons coded as “Extant”. The resulting dataset contained 5,760 terrestrial mammals comprising species and subspecies.
Frequency and geographic distribution of cyclones, available as shapefiles, were extracted from the joint database DEWA/GRID-Geneva of the United Nations Environment
Programme (Accessed June 2012) (UNEP 2005). This database includes geospatial data on cyclone tracks for the period 1980-2005. We restricted the analysis to the period 1992-2005 for which global coverage of cyclone occurrence is available. To generate the global
40 | P a g e distribution of areas currently prone to drought conditions, we used the Global Drought
Monitor database (Accessed December 2011) (Lloyd-Hughes & Saunders 2011). This database uses the Standardized Precipitation Index (SPI), which is a probability index based on the cumulative rainfall data for a given period of time, to identify drought occurrence
(McKee et al. 1993; Trnka et al. 2003). We used the global monthly average SPI data available on a 1° x 1° equally spaced longitude/latitude grid and used a running 9-month time window over the period 1980-2011 for each grid point. A mask was applied to the data to exclude grid points for the oceans as well as in locations/times of the year when it has not been possible to determine the SPI (e.g., Polar regions like Greenland or the Siberian Tundra and also deserts where totals are close to zero with very little variance). From the remaining grid points, drought areas were identified as those whose grid points had mean SPI scores below zero. This method is robust provided that computations are based on (i) a high-quality continuous precipitation record of at least 30 years and (ii) a reasonable SPI time scale to reflect impacts on water resources of interest (McKee et al. 1993; Lloyd-Hughes & Saunders
2002). Our SPI series spans a period of 31 years and different time windows were explored prior to determining the nine month scale at which the degree of dryness remained relatively consistent in agreement with general theory defining hydrological droughts (Seneviratne et al.
2012).
Quantifying exposure to cyclones and droughts
We defined exposure as the degree to which the spatial extent of a particular type of extreme climatic event overlays the geographic area within which a terrestrial mammal is most likely to occur. We therefore started by superimposing the extant geographic distribution of terrestrial mammals (using ArcGIS version 9.3, ESRI 2008) with the geographic distributions of the areas impacted by cyclones and drought conditions. In the second stage we identified
41 | P a g e mammals with “significant” and “high” exposure, defined as an overlap of at least 25% or
75%, respectively, of their extant geographic range with areas impacted by cyclones and droughts (see below). Cyclones are short-lived extreme climatic events (Landsea et al. 2010) and can have a high frequency of occurrence not always affecting the same extent of the environment (Lugo 2008). Taking this into account, we selected those mammal species in which at least 25% of their extant geographic ranges overlapped with the paths of cyclones.
We initially assumed that exposure to at least 2 cyclones or more over a relatively short period of time could prevent species not only from recovering the individual numbers lost in the first event but also erode the resilience of survivors and drive more serious population losses in the future should a phenomenon of similar magnitude takes place. For mathematical accuracy, however, we used 2.6 cyclones as this figure represents the weighted mean of the frequency of cyclones experienced by each species for the period 1992-2005, which equates to at least two cyclones every 10 years within a species extant range. Compared to cyclones, droughts take a long time to develop and have large return periods making start/end times difficult to delineate especially when there is insufficient observational data (McKee et al.
1993; Chung & Salas 2000; Breshears et al. 2005). This is why frequency counts of drought events and future projections are more challenging for some areas of the world than others
(Panu & Sharma 2002; Mishra et al. 2009; IPCC 2012). Recognizing the difficulties in quantifying the occurrence probabilities of individual events globally, we assessed exposure to droughts by focusing only on the spatial extent of those areas which, on average, have had drier than average conditions (SPI <0) over the period 1980-2011. Terrestrial mammals with significant exposure were identified as those with at least 25% overlap between their extant geographic range and the areas which have experienced such drought conditions (SPI <0).
Finally, we identified mammals having high exposure defined as those whose extant geographic range exhibited ≥75% overlap with either cyclones or droughts with particular
42 | P a g e emphasis on those collectively termed as “Threatened” and “Non-Threatened” for which we also identified relative location using the WWF terrestrial ecoregions’ classification (Olson et al. 2001). The 25% and 75% range overlaps used to characterize significant and high exposure for both types of extreme events are arbitrary cut-off levels because at present there is no objective basis on which to set the level. We use these values because they have been used previously in spatial ecology and conservation prioritization of mammals and other vertebrates at different scales (Argent et al. 2003; Orme et al. 2005; Morrison et al. 2007;
Pompa et al. 2012). We believe that these proportions can be easily interpretable and decision-makers more likely to be familiar with them.
Results
Terrestrial mammals at significant exposure
Of the world’s terrestrial mammals assessed (n = 5,760), 6.2% were determined at significant exposure to cyclones, 22.6% to droughts and 3.1% to both phenomena. Although the number of terrestrial mammals with significant exposure to droughts is over three times the number of those exposed to cyclones, the proportions of “Threatened”, “Non-Threatened” and “Data
Deficient” is similar (Fig. 3.1). Long-term observational data indicates that geographical hotspots of cyclones are different from those where droughts are common. As a result, there were few examples of mammals exposed to both phenomena over the time periods assessed.
Of the 6.2% (n = 357) of terrestrial mammals significantly exposed to cyclones, the greatest proportions of both “Threatened” and “Non-Threatened” mammals were located within the
Caribbean region (36.7% and 41.7%, respectively). Of the 22.6% (n = 1,301) terrestrial mammals significantly exposed to droughts, the greatest proportions of both “Threatened” and “Non-Threatened” mammals (51.9% and 65.7%, respectively) were predominantly distributed in Africa south of the Sahara.
43 | P a g e
Terrestrial mammals at highest exposure
From the 5,760 terrestrial mammals assessed, our analysis of the highest exposure for the
“Threatened” grouping yielded 100 (1.7%) species exposed exclusively to cyclones (Figure
3.2a and Table S3 in the online supplementary material) and 139 (2.4%) exposed exclusively to droughts (Figure 3.2b and Table S3) with 36 (0.6%) exposed to both phenomena. The greatest proportion of “Threatened” cyclone-exposed mammals (n = 56, 0.9%) was found in
Madagascar, including species across five terrestrial ecoregions (spiny thickets, succulent woodlands, subhumid forests, lowland forests and dry deciduous forests) (See Figure 3.2a).
The greatest proportion of “Threatened” drought-exposed mammals (n = 43, 0.7%) was found in West Africa, including species across the Sudanian savanna, Guinean and Congolian forest-savanna mosaic, Congolian coastal and swamp forests, and the Cameroonian
Highlands forests (Figure 3.2b). Globally, the taxonomic Order Primates comprised the greatest proportion of “Threatened” mammals at the highest exposure to either cyclones or droughts, followed by the Orders Rodentia and Chiroptera (Table 3.1).
In parallel, from the total 5,760 mammals assessed our analysis of highest exposure for
“Non-Threatened” mammals yielded 135 (2.3%) species exposed exclusively to cyclones
(Figure 3.2c and Table S3), 48 (0.8%) exposed exclusively to droughts (Figure 3.2d and
Table S3) and 30 (0.5%) exposed to both phenomena (Table 3.1). The greatest proportion of
“Non-Threatened” mammals exposed to cyclones (1.1%, n = 65,) and droughts (0.5%, n =
33) were located in Madagascar. Globally, the Order Rodentia comprised the greatest proportion of “Non-Threatened” mammals at highest exposure to cyclones, followed by the
Chiroptera and Afrosoricida (Table 3.1). In contrast, the Chiroptera comprised the greatest proportion of such taxa at high exposure to droughts, followed by the Rodentia and Primates
(Table 3.1).
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Discussion
Two decades of research and international collaboration have been devoted to compiling and assessing the available information to advance our understanding of climate change impacts on Earth. On the basis of this work, it is now widely recognised that we need to accelerate our efforts for managing the potential impacts of extreme climatic events on natural and human systems (CCSP 2008; ICSU 2008; UNISDR 2011; UNEP 2012). According to the recently released report on Managing the Risk of Extreme Events and Disasters to Advance Climate
Change Adaptation by the IPCC (IPCC 2012), there is solid evidence that the observed increments in global temperatures have been and will continue shaping activity patterns of extreme climatic events around the globe over the 21st century. Unfortunately, establishing the direction and magnitude of such patterns in terms of occurrence probabilities, frequency and intensity is challenging (Bader et al. 2008; Gutowski et al. 2008; Seneviratne et al. 2012).
Less contentious, however, is the proposition that the severity of impacts strongly depends on
(i) the level of exposure to extreme events, (i) the cumulative effects of similar such phenomena experienced in the past and (iii) the intrinsic vulnerability of the systems affected
(Ostertag et al. 2005; Adger 2006; Murray et al. 2012; Seneviratne et al. 2012).
From a conservation perspective, recent research defines mechanisms through which climate change and extreme climatic events are expected to increase biodiversity loss
(Dawson et al. 2011; Geyer 2011; Jiguet et al. 2011; Laurence & Useche 2012) and proposes frameworks to tackle negative impacts at both species and population levels (Williams et al.
2008; Ameca y Juárez et al. 2012). Studies identifying species’ susceptibility and adaptive capacity to a broad spectrum of impacts derived from climate change are also underway
(Foden et al. 2008; Scholss et al. 2012), yet exposure to extreme climatic events has not been explicitly addressed in any species’ risk assessment. The well-established IUCN Red List does not, for example, include any explicit consideration of extreme climatic events. The
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IUCN Red List does record threat types (IUCN Threats Classification Scheme, Version 3.0,
Accessed July 2012) and the category “climate change and severe weather” includes potential impacts derived from cyclones and droughts under two different sub-categories: “storms and floods” and “droughts”. Yet the Red List records only 69 terrestrial mammal species affected by storms/floods, and 81 affected by droughts.
Given that there is limited information on the consequences of extreme climatic events for species, and that in any case there is not a mechanism to use any such information systematically in the risk assessment, there is a gap in identifying species which could benefit from conservation actions to mitigate impacts from such extreme phenomena. For example, strategies enhancing an exposed population’s resilience to extreme climatic events might include the creation of a systematic network of waterholes where droughts are a high risk.
Admittedly, the impacts of cyclones will be more difficult to mitigate; in this case, the options could include translocations of individuals at imminent risk. As presented here, a workable indicator to assess the contribution of exposure to the overall species’ vulnerability can be formulated by quantifying the overlap between species occurrence and exposed areas to such extreme climatic events. Based on this, approximately 31.9% of the terrestrial mammals assessed under the IUCN Red List have experienced significant exposure to cyclones, droughts or both in combination, of which 4.7% faced extremely high exposure
(Table S3). This could represent a substantial increase in the number of terrestrial mammals classified as threatened by the IUCN under the category “climate change and severe weather”
(Table S4) provided that these species are found to possess high sensitivity and/or low adaptability to these phenomena (see below).
The historical exposure of a species to a given disturbance over evolutionary time is expected to shape its intrinsic adaptability to that disturbance, reducing its likelihood of extinction from this source (Lande et al. 2003). However, adaptations that have prevented
46 | P a g e species from becoming extinct due to recurrent exposure to extreme phenomena (in the order of thousands of years) might not be the same as those traits that prevent them from experiencing mass-mortality events. For example, populations of exposed species with early maturation, a large number of offspring and/or many reproductive events during a lifetime may bounce back from the brink of extinction caused by extreme climatic events; however these species might not have evolved the traits (dormancy, torpor, high dispersal capacity, diversified diet, etc.,) to avert immediate or near-term severe declines derived from exposure to disturbance. As a result, different taxa with similar levels of exposure might experience greater or lesser impacts due to species’ intrinsic susceptibility and/or adaptability and the local habitat conditions (See below) (Recher et al. 2009; Bezuijen et al. 2011; Ameca y
Juárez et al. 2012). From our analysis, the Order Primates exhibited the greatest proportion of taxa considered at highest exposure to cyclones and droughts, (Figure 3.3a-b). While it might be expected that some primate species will possess the behavioural and physiological flexibility to cope with conditions derived from these phenomena, it is also true that such flexibility will have its limitations: recent reports of cyclone and drought impacts on primate populations indicated losses far greater than the expected annual mortality rate. For example, a 46.8% loss in Semnopithecus entellus (Waite et al. 2007), a 50% loss in Eulemur fulvus
(Tarnaud & Simmen 2002), and a 42% loss in Alouatta pigra (Pavelka et al. 2003). In addition, local habitats might have already experienced anthropogenic degradation that could enhance species exposure to extreme climatic events and/or compromise any intrinsic coping strategies for population persistence in the longer term. Studies have revealed that the interaction between anthropogenic stressors and exposure to extreme climatic events can be expected to outstrip species’ adaptive capacity (if any), enhancing the risk of severe unanticipated impacts (Craig et al.1994; Munson et al. 2008). Yet, other studies report that these stressors can mitigate each other (Verboom et al. 2010; Blaum et al. 2011). Similarly,
47 | P a g e some extreme climatic events might be a ‘catastrophe’ for some species (Pavelka et al. 2003) but create a bonanza for others (Widmer et al. 2004). These principles will need to be taken into account in vulnerability and viability analyses to contribute in revealing within species’ symptoms of extinction risk and pinpointing overlooked taxa in need of conservation attention and/or reassure the efforts to those already of concern. The existing IUCN criteria have a wide range of mechanisms for calibrating threat levels across different life history and threat contexts, and this approach could be extended to deal with climate change impacts
(Foden et al. 2008) including extreme events. Our method is comparable to existing classification systems in its potential for consistency and flexibility, which are fundamental features in the Red Listing process (Mace et al. 2008; Vié et al. 2008). Consequently, it has the potential to be applicable to other taxonomic groups.
Although many details concerning extreme climatic events remain to be fully understood, earth-system modellers have made significant progress in the treatment of uncertainties. In this way, and following the uncertainty guidance of the IPCC fifth report (Mastrandrea et al.
2010), there is evidence of medium confidence suggesting that increases in both duration and intensity of droughts have been in place through southern Europe and West Africa; this is also expected to occur in the next 100 years in Central Europe, the Mediterranean, Central
North America and Mexico, northeast Brazil, and southern Africa. It is likely that the frequency of the most intense cyclones will increase substantially in some regions
(Seneviratne et al. 2012). These observed and projected changes suggest that identifying species that are or might soon be subject to extreme climatic events merit more attention from conservation scientists and policy-makers alike, if effective and proactive strategies are to be designed and implemented. The IUCN Red List is widely accepted as a critical conservation tool for species’ conservation against the escalating impacts of anthropogenic pressures. In this regard, assessing levels of exposure to extreme climatic events can complement existing
48 | P a g e guidelines and criteria for assessing species’ extinction risk and develop more robust assessments since these phenomena are not well addressed by the current criteria (Foden et al. 2008). In particular, pinpointing areas where species have been exposed to extreme climatic events can help target species that possess a combination of traits that makes them highly vulnerable to such events while being associated with a degree of exposure for which such traits may become critical in shaping survival. With this study we intend to stress that incorporating the quantification of exposure to extreme climatic events, combined with information pertaining to species’ intrinsic sensitivity and adaptability to such events, into existing risk assessments could contribute towards reducing the overall vulnerability of species to potential population losses and hence, ultimately, reducing their risk of extinction.
Supporting Information
Appendix Table S3. a, Threatened and b, Non-Threatened terrestrial mammals with highest exposure to cyclones and droughts.
Appendix Table S4. Terrestrial mammals categorized at threat of a, storm/floods (n = 69) and b, droughts (n = 81) on the IUCN Threats Classification Scheme Version 3.0.
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Chapter 4
Vulnerability assessment to cyclone-driven population declines: an implementation for
Mexican endangered mammals
Abstract
Most vulnerability assessments to climate change impacts focus on characterizing the degree to which species are likely to be affected by long-term changes in climatic variables. Yet, there has been little progress in the impact assessment of extreme climatic events on wildlife.
This is particularly worrying, as these phenomena can lead to drastic changes in population sizes and species unable to develop coping strategies soon enough may face an increased risk of extinction. We implemented a vulnerability assessment to cyclone-driven population declines for 19 Mexican non-volant terrestrial mammals listed in danger of extinction at country level. We based the assessment on indicatives of sensitivity (vagility & territoriality) and adaptive capacity (food/habitat flexibility) for each species to a given level of exposure and identified the most vulnerable species. Vulnerability as shaped by these indicators was not uniform across taxa: Two of the three species determined to be highly vulnerable to cyclones were Primates, three out of nine moderately-vulnerable species were Rodentia and four out of seven low-vulnerable species were Carnivora. Among the indicators evaluated, territoriality and a level of exposure ranging between 25% and 75% were the two indicators with the highest level of correlation, having the greatest contribution in shaping the observed vulnerability ranks for the assessed species. The integration of information on exposure to extreme climatic events and species biology as presented here represents a simple yet highly informative framework for assessing vulnerability and inform existing climate change action plans for species conservation.
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Introduction
A critical task for conservation biologists encompasses the identification of sources of threat to biodiversity and the development of comprehensive strategies to minimize losses detrimental to human wellbeing (Cardinale et al. 2012). Under the ongoing human appropriation of natural capital most conservation efforts focus on threats of direct anthropogenic origin such as urbanization, deforestation and wildlife utilization (McKinney
2002; Davies et al. 2006; Price & Gittleman 2007; Ceballos et al. 2010; Wearn et al. 2012) but give limited attention to threats of indirect anthropogenic origin, such as those associated with extreme climatic events. Recently, however, a growing body of evidence indicates that climate-related phenomena will become major triggers of biodiversity loss in the 21st century
(UNEP 2012). As a result, vulnerability assessments to climate change impacts initially developed to safeguard human lifes and assets (e.g., Füssel & Klein, 2006), are gaining attention in conservation planning and wildlife management of species and the systems which sustain them (Glick et al. 2011; Schlesinger et al. 2011; Crossman et al. 2012; Watson et al.
2012). In this regard, vulnerability to climate change impacts has been broadly defined as a function of three main components: exposure, sensitivity and adaptive capacity (Foden et al.
2008; Hole et al. 2011; Dawson et al. 2011). Exposure relates to the degree to which species or systems are exposed to significant climatic variations whereas sensitivity and adaptive capacity are expected to be shaped by species’ biological traits and the plasticity of such traits. Most vulnerability assessments focus on characterizing the degree to which species are likely to be affected by long-term changes in climatic variables (e.g., Berry et al. 2003;
Bezuijen et al. 2011; Byers & Norris 2011; Dubois et al. 2011). Yet, there has been little headway in the explicit assessment of exposure to extreme climatic events. For recently exposed and intrinsically sensitive species, pre-existent (or novel) environmental stressors can stimulate serious declines (Craig et al. 1994; Munson et al. 2008). Early identification of
51 | P a g e species’ vulnerability from recently occurred extreme climatic events can enable stakeholders to incorporate this information in existing action plans addressing short-term threats to populations’ survival; at a timeframe where conservation planning and mitigation strategies can be reasonably foreseen, financed and implemented.
Standing out as the fourth nation in terms of overall species richness and native mammalian fauna, Mexican biodiversity is among the richest on earth (Sarukhan et al. 2009).
At the same time, Mexico is recurrently impacted by extreme climatic events with tropical cyclones observed and expected to become more frequent and intense in coming decades
(Magaña-Rueda 2004; Manson et al 2009; Seneviratne et al. 2012). While it is assumed that species historically exposed to tropical cyclones (hereafter, cyclones) are well adapted to cope with disturbance, increased exposure might surpass their capacity to recover: in Mexico, species recently exposed to a high frequency of cyclone activity have indeed undergone serious population losses and currently struggle to bounce back to pre-disturbance population numbers (Cuarón et al. 2008a; Manson et al. 2009). Characterization of indicators shaping vulnerability to such cyclone-driven population declines is non-existent (e.g., see Rodríguez-
Luna et al. 2006). Thus, there is a gap in identifying the most vulnerable species which could benefit from pre-emptive management and/or immediate recovery actions in the cyclones’ aftermath. For species recently or expected to be exposed, such actions could be tailored to build resilience to existing and expected negative impacts (e.g., increasing population fitness) or developing management towards reduce probabilities of future exposure (e.g., habitat management or translocations).
In this study we implemented a vulnerability assessment to cyclone-driven population declines focusing on the Mexican’ non-volant terrestrial mammals considered in danger of extinction by Mexican laws. These species were chosen because at difference of marine or volant species they are expected to find it more difficult to avoid direct impacts of cyclones
52 | P a g e and already represent one of the most relevant focal groups in the context of biodiversity conservation in Mexico (Medellín et al. 2006). These species are flagged at high extinction risk because i) their population numbers within the country have fallen dramatically due to direct human utilization, predation or diseases and ii) the remaining natural habitat within species’ ranges is under heavy threatening processes such as destruction or drastic modifications (Norma Oficial Mexicana 059 [Official Norm for Mexican Ecology 059],
2001). Currently, the Mexican government is supporting the implementation of conservation programs with the aim of maintaining and/or enhancing resilience of the species and their habitats to impacts derived from current and expected climatic change (CONANP 2011).
Hence, information derived from this study could feed directly into management programs for these priority species: Among Mexican mammalian fauna they stand already at the first line to disappear due to human impacts and extreme climate-related phenomena can increase such risk. Therefore there is a critical need to identify those further jeopardized by climatic impacts and implement comprehensive management practices at once to protect them.
We assessed vulnerability as a function of past exposure, sensitivity and adaptive capacity to cyclones’ impacts to determine the most vulnerable species. First, available frequency distribution data pertaining to cyclones that affected the Mexican territory within the last three decades as well as distribution maps of the species and subspecies (for simplicity referred as taxa) of concern were used to outline a quantitative indicator of exposure (See
Table 4.1). Then from an extensive review on the life history and ecology of the species of concern, two biological characteristics were identified as indicative of sensitivity and two of limited adaptive capacity (hereafter non-adaptability) (Table 4.1). Each of these traits was scored (See assessment methodology section) in relation to its contribution to vulnerability following a recently introduced vulnerability framework for assessing population die-offs driven by extreme natural phenomena (Ameca y Juárez et al. 2012). Finally, high, moderate
53 | P a g e and low-vulnerable species are identified and the implications of the study for conservation planning and management discussed.
Methods
Data sources and indicators
Distributional ranges of the species of concern were obtained in shape file format from the
IUCN Red List assessment process (IUCN 2008). Available geo-spatial data on cyclones impacting Mexican territory (period 1980 - 2006) were extracted from the joint database
DEWA/GRID-Geneva of the United Nations Environment Programme (UNEP 2005,
Accessed June 2012). Focusing on species’ range polygons within the Mexican territory, we extracted those for which presence was coded as “extant”, as these reflect areas where species occurrence is most likely. Finally, superimposing the merged cyclones tracks with the species distribution data (using the ArcGIS software, Version 9.3, ESRI 2008) mammals with significant exposure were defined as those with an overlap of at least 25% of their extant geographic range with areas that have been impacted by ≥ 2 cyclones during the 27 year period. It is assumed that exposure to at least 2 cyclones or more over a relatively short period of time could prevent species not only from recovering the individual numbers lost in the first event but also erode the resilience of survivors and drive more serious population losses in the future due to conventional threatening processes or should a phenomenon of similar magnitude takes place. In this regard, 3 species out of the 23 were impacted by only 1 cyclone (Microtus californicus, Romerolagus diazi and Sylvilagus insonus) and 1 species exposed to more than one cyclone but equating an overlap < 25% (Alouatta palliata mexicana) and therefore were not assessed any further. As a result, 19 species were kept for the vulnerability analysis. Definition of traits indicative of sensitivity (low vagility and territoriality) and non-adaptability (habitat specialist and food specialist) for the taxa of
54 | P a g e concern were extracted from the Walker's encyclopaedia of living mammals (Nowak 1999) and the ecological assessments of the species assembled by relevant assessors available from the IUCN Red Listing process (IUCN 2008). These traits were screened against a check-list of species’ traits identified by species experts as indicatives of sensitivity and non- adaptability to climate change impacts across all taxonomic groups (Foden & Collen 2007).
We confirmed that our selected traits (see assessment methodology) were represented in such exercise as relevant in the assessment of vulnerability to extreme climatic impacts.
Assessment methodology
Based on a theoretical framework to assess species’ vulnerability to population die-offs driven by extreme natural events (Chapter 1) and a criterion for assessing exposure (Chapter
3), a vulnerability assessment from recent occurrence of cyclones was implemented (Table
4.1). Exposure to cyclones can vary between 0 and 100% depending on the overlap with the species’ extant geographic range. Species significantly exposed (hereafter exposed) defined as those having a range overlap of at least 25% with cyclones’ tracks are grouped in a vulnerability rank coded as follow: “0” Low vulnerability; “1” Moderate vulnerability; “2”
High vulnerability (Figure 4.1). Referring to Figure 4.1, a species possessing up to two traits indicative of sensitivity (S) and up to two traits of non-adaptability (NA) indicated as “Yes >
0” will be considered as being associated with a low level of vulnerability provided that exposure (E) is either non-significant or entirely absent (case i). For a species which is exposed indicated as “Yes ≥ 25%” and lacks the traits indicative of sensitivity and non- adaptability the level of vulnerability is also considered low. However, if an exposed species have up to two traits indicative of sensitivity (case ii) “or” non-adaptability (case iii) represented as “Yes > 0”, then it will be considered moderately vulnerable. When a species exhibits up to two traits indicative of sensitivity “and” up to two traits shaping non-
55 | P a g e adaptability then it will be considered as highly vulnerable (case iv). The position of a species within a given vulnerability rank (low, moderate, high) is shaped by degree of exposure. In this regard, an intrinsically high vulnerable species having its extant distribution most overlapped with cyclones’ tracks is expected to be the most vulnerable. As a result, it is possible to differentiate species sensitive and/or unable to adapt but least or not exposed
(scenarios similar to case i) and species similarly prone from intrinsic biology but exhibiting large levels of exposure (scenarios similar to case iv). Hierarchical agglomerative cluster analyses were carried out (using the R software, Version 2.15.1, R Core Development Team
2012) to visualize how associations between indicators were formed as well as to identity species most similar/dissimilar based on their respective scores of indicators of exposure, sensitivity and non-adaptability.
Results
Based on historical records of cyclone activity for the period 1980-2006, it is evident that on average, cyclone intensity as measured by mean wind speed has remained stable; however, the frequency of events has grown substantially particularly towards the last decade (Figure
4.2), a trend which might continue and increase risk of exposure (See Done et al. 2011).
Scores of indicators used to determine sensitivity, non-adaptability and exposure for the 19 species assessed are presented in Table 4.2. The level of vulnerability as shaped by these indicators was not homogenous across taxa: Primates was the Order with most highly- vulnerable species (n = 2) and Rodentia comprised the greatest number of moderately- vulnerable (n = 3) whereas Carnivora revealed the greatest number of least vulnerable species
(n = 4) (Figure 4.3). Overall, 3 species were determined to be highly-vulnerable (Ateles geoffroyi vellerosus, Ateles geoffroyi yucatanensis, Cyclopes didactylus) (Table 4.2). These species are predominantly distributed in the Yucatan’s Peninsula and also present in Chiapas,
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Tabasco, southern Tamaulipas, and Veracruz (Figure 4.4). On the other hand, 11 species were determined to be moderately-vulnerable and 6 species to be least vulnerable (Table 4.2).
Distribution of exposure over the extant geographic range of these species stretches along the
Atlantic coast (states of Sinaloa, Nayarit, Jalisco, Colima, Michoacán, Guerrero and Oaxaca) and the Yucatan’s Peninsula including the states of Veracruz and Tamaulipas in the Gulf of
Mexico (Figure 4.4).
Cluster analysis of indicators to assess exposure, sensitivity and non-adaptability, revealed that the indicator of “significant exposure” and the indicator of “territoriality” were most associated between them than with any other indicator (Figure 4.5a) and had the greatest contribution in shaping the observed vulnerability ranks for the assessed species (Table 4.2).
On the other hand, indicators of non-adaptability were closely associated with the indicator
“low vagility” and less so with the remaining indicator of exposure (Figure 4.5a). Cluster analysis focused on species (Figure 4.5b) revealed three major clusters of species sharing similar indicators shaping the determined vulnerability ranks (Table 4.2).
Discussion
In response to the sustained pressures of global climate change on biodiversity, vulnerability assessments with a range of levels of complexity have emerged to assist in identifying species at risk to undergo negative impacts, and in turn, guide management in designing precautionary measures and implementing reactive responses to secure their persistence
(Glick et al. 2011). While the quantification of the exposure component to climate change in vulnerability assessments is based on projections of changes in continuous long-term climatic conditions (e.g., precipitation, solar radiation, temperature) overlapping relevant areas with species’ ranges, an explicit account of short-duration extreme climatic events such as cyclones remains limited as the spatiotemporal scale within which they develop makes it
57 | P a g e difficult to represent in simulations (Seneviratne et al. IPCC 2012). Thus, projections of future exposure remain of partial reliability to be adopted in vulnerability assessments. For preventative conservation purposes, an alternative to the use of projections of future exposure to extreme climatic events are the observations of past exposure and specifically, of contemporary incidence as species and habitats recently exposed may be less resilient to current and/or near-future threats (Pavelka et al. 2007; Lugo 2008; Johnson & Winker 2010;
Lewis & Rakotondranaivo 2011; Ramirez-Barajas et al. 2012; Ameca y Juárez et al. 2013).
Admittedly, exposure to an extreme climatic event alone does not equals high vulnerability to population die-offs nor does it warrant a particular risk status designation, as exposed species may possess biological attributes acting as buffer against disturbance during such event, traits allowing fast recovery after the event, or a combination of both (Kanowski et al. 2008;
Wilson et al. 2008; Canale & Henry 2010; Johnson & Winker 2010). Therefore, conservationists assessing species’ vulnerability to climate change impacts will require the identification of the role(s) that life histories and ecologies may play when facing specific impacts derived from particular extreme climatic events. Here by assessing four biological traits in relation to exposure to cyclones for a group of mammals we shed light on how such identification process can be carried out for a particular extreme climatic event.
Vulnerability to cyclone-driven population declines for the 19 Mexican species assessed was not taxonomically homogenous. Yet different groups of species revealed to be similarly vulnerable as they exhibited biological attributes shaping similar sensitivity and/or non- adaptability to cyclones’ impacts: The species determined to be highly vulnerable share the commonality of been territorial and food specialists, and low-vulnerable species were not territorial, poor dispersers or specialists (neither food nor habitat specialists). These findings suggest in line with mammal extinction risk theory that similar biological attributes might cause certain species to be particularly prone to a given threat (Purvis et al. 2000; Isaac &
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Cowlishaw 2004; Cardillo et al. 2008). However, a note of caution is needed as species ranked under the same level of vulnerability due to intrinsic biology shaping sensitivity and/or non-adaptability are not equally prone; the level of exposure is expected to contribute vulnerability in parallel to that derived from biology (Ameca y Juárez et. al. 2013). The spider monkey species Ateles geoffroyi yucatanensis and Ateles geoffroyi vellerosus were determined to be highly vulnerable because both are territorial (sensitivity to direct physical exposure) and exhibit a diet heavily biased towards ripe fruits throughout the year (limited flexibility to sustain supplemental feeding from alternative resources). However, proportionally, the extent of occurrence of A. g. yucatanensis was found to be nearly two times more exposed than that of A. g. vellerosus. As a result, A. g. yucatanensis is positioned higher in the vulnerability rank. Cyclopes didactylus, the smallest living anteater species, was also determined to be highly vulnerable because is a slow moving and territorial mammal with narrow diet; however it is positioned below the two Primate species in the rank because its extent of occurrence is proportionally less exposed (See Table 2 and Appendix Table S5).
Alouatta pigra, Procyon pygmaeus and Eira barbara are ranked as moderately vulnerable yet the entire distribution range of P. pygmaeus (unlike that of A. pigra and E. barbara) is constrained to a small island, thus, having the least opportunities for dispersal and making it the most prone to direct physical exposure regardless its high vagility (Cuarón et al. 2008a).
As a result P. pygmaeus is positioned higher among moderately vulnerable species. Species with a large proportion of their range exposed might not necessarily been highly vulnerable as is the case of Leopardus pardalis, Leopardus wiedii and Panthera onca: their extant geographical range within Mexican territory exhibited a degree of exposure > 70% yet these species occur at low densities and their ranges are large enough to dilute the risk of been exposed (e.g., Panthera onca 1-4 individuals per Km²; Caso et al. 2012).
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Practices already in place to reduce vulnerability or enhance resilience against conventional threats for multiple species sharing a common habitat could be adopted to also tackle the disturbance derived from cyclones’ impacts. Such management practices can be broadly classed as either reactive (aimed to deal with the disturbance during or right after it occurs) or preventative (aimed at build species and/or habitat resilience to disturbance before this is generated). Pertaining to reactive management, for example, translocation programs aimed to relocate permanently wildlife populations (e.g., Alouatta pigra, ranking in our assessment as highly-vulnerable) from areas where they are at high risk of human utilization and habitat loss (Canales-Espinosa et al. 2011) can also be implemented for species inhabiting areas where cyclones are a threat (See Carlile et al. 2012 and references therein for examples in seabirds). Subsequently, habitat management can be developed to restore pre- disturbance conditions. For species less readily to disperse and identified to possess limited flexibility to both endure cyclones’ impacts or go through emergency transfers, preventative management focusing on decrease probabilities of physical exposure may be a more suitable practice: For example, tree species with the highest wind resistance (Duryea & Kampf 2011) can be used to buffer habitats common to one or more vulnerable species. Based on pre- existing monitoring of habitat use and patterns of movement, vegetation corridors can also be adopted to systematically link up habitats exposed to cyclones (e.g., mangrove swamps) to suitable, less physically exposed habitats. In addition, a systematic assemblage of plant species chosen to shape these corridors could also provide provisional resources to individuals in transit (Luckett et al. 2004; Nasi et al. 2008).
In order to minimize risk to unanticipated population losses driven by extreme climatic events we need to foresee the species most vulnerable and ensure they benefit from appropriate interventions to secure their survival. The integration of information pertaining to exposure to extreme climatic events and species’ intrinsic biology as presented in our case
60 | P a g e study represents a simple yet highly informative framework to assess vulnerability to these types of phenomena and inform existing climate change action plans for species conservation.
Supporting Information
Appendix Table S5 Non-volant terrestrial mammals (n = 19) listed in danger of extinction by Mexican laws assessed for cyclone-driven population declines.
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Chapter 5
Quantifying the impact of cyclones on extinction probability of a primate population: insights into island extirpations
This chapter is in preparation for submission to the journal Endangered Species Research as: Ameca y Juárez, E.I. Quantifying the impact of cyclones on extinction probability of a primate population: insights into island extirpations.
Abstract
An important goal of population viability analysis is to identify the likely impacts that anthropogenic disturbances can exert on demographic parameters as a mean to project population trajectories and evaluate the performance of management interventions. Extreme climatic events are expected to intensify in the coming decades as a consequence of anthropogenic climate change with unknown consequences to the viability of wildlife populations. Unfortunately, long-term studies quantifying their impact on population growth and trajectory are rare. This type of information would be particularly relevant for populations inhabiting isolated and degraded habitats where extreme climatic events can be expected to substantially increase the risk of “island extirpations”. Using demographic data from a 14- year field study of an isolated primate population comprising 95 individuals (Alouatta palliata mexicana), estimates of quasi-extinction risk (N = < 5 individuals) as a function of different cyclone activity (frequency and intensity) are provided. Cyclone intensity was simulated affecting 30% the survival, 10% fecundity and 30% carrying capacity every year that a cyclone strikes with a frequency of occurrence of 0.1% per year. These estimates were used to derive alternative scenarios of low and high cyclone activity to obtain a range of potential effects on population survival while accounting for two conservative estimates of hunting and deforestation as sources of anthropogenic impacts. While risk of falling below the quasi-
62 | P a g e extinction threshold ranged between 9 to 23% by 20 years in sets of simulations with effects cyclone intensity ranging from low to high, while holding frequency at the baseline estimate, a model with overall low cyclone activity (low frequency and intensity) rendered < 5% probability of reaching the quasi-extinction threshold. A 5% increase in cyclone intensity from the baseline estimate while increasing proportionally cyclone frequency of occurrence revealed a quasi-extinction probability of 45% by 20 years but a 27% chance when cyclone intensity was low. These findings suggest that cyclone intensity becomes a more critical threat for population persistence with increased cyclone frequency. Quasi-extinction risk was found to range between 63 to 98% by 40 years for all the scenarios simulating different cyclone activity while accounting for human impacts but less so when cyclones were modelled singly.
In absence of cyclones, however, risk of quasi-extinction remained below 5% by 40 years but up to 42% by 50 years, reaching population extinction by around 60 years. This suggests that cyclone-mediated impacts on the population and the habitat have the potential to substantially increase the speed of the extinction vortex. Analysis of sensitivity for those values to each vital rate for the simulated scenarios shown that the adult survival generated the greatest change in the probability of reaching population quasi-extinction, followed by the survival of infants. This PVA help us to understand demographic consequences that could arise in isolated primate populations exposed to different degrees of cyclone disturbance. For those populations constrained to degraded habitats and experiencing heavy anthropogenic pressure, the resulting synergies will exacerbate risks of population extinction.
Introduction
Species are subject to great changes in the environment which have the potential to generate large mortality events rendering impacted populations more susceptible to extinction (Gilpin
& Soulé 1986; Schoener et al. 2001). The nature and processes by which human-mediated
63 | P a g e disturbances drive the extinction vortex has been reported by many (Vitousek 1997; Davies et al. 2006; Ceballos et al. 2010). However, less is known about the impacts of extreme climatic events on population growth and trajectory (Ameca y Juárez et al. 2012). Among terrestrial vertebrates, for example, evidence to date shows that the extant distribution of at least 50 species of primates has been exposed to cyclones within the past three decades (Ameca y
Juárez et al. 2013): many of these species are already endangered by human activities and such endangerment may be heightened by extreme climatic events becoming more frequent and/or more intense (Seneviratne et al. 2012).
Records of high mortality episodes (also known as die-offs) associated to the impacts of tropical cyclones (hereafter referred as cyclones) on primate populations harbouring in continuous, well-conserved forest habitats do exist (Dittus 1985; Tarnaud & Simmen 2002;
Pavelka et al. 2003). Populations constrained to degraded, small habitat patches (all else been equal) can therefore be expected to be heavily susceptible to cyclones. These die-offs can leave populations vulnerable to further threats such as Allee effects, increased demographic stochasticity and human exploitation which could cause the loss of nearly the entire population – a so called quasi-extinction (Ginzburg et al. 1982; Engen et al 2002; Lande et al.
2003). The risk of quasi-extinction is defined as the probability of reaching a population size at which the above factors (e.g., demographic stochasticity) become critical in determining the fate of the population (Boyce 1992; Akçakaya 2000). Quasi-extinction risk assessment is an important metric in the evaluation of species conservation status under the population viability analysis (PVA) framework (Breininger et al. 1999; Engen et al. 2002; Holmes et al.
2007; Mace et al. 2008).
Among Neotropical primates, populations of the critically endangered Mexican howler monkey (Alouatta palliata mexicana) distributed in the fragmented tropical forest of Los
Tuxtlas, Southern Mexico, are heavily affected by an array of anthropogenic processes with
64 | P a g e habitat destruction and fragmentation being the most persistent drivers of declines over the past three decades (Rodríguez-Luna et al. 1987; Solórzano-García et al. 2012). In addition to these anthropogenic processes, extreme natural events are not uncommon in the region
(Manson et al. 2009). Notably, climatic conditions in tropical environments in Mexico are influenced by cyclones (Guevara 2004; Portilla-Ochoa et al. 2006; Gutierrez-Garcia & Ricker
2011). These extreme phenomena pose an additional threat to primate populations harboured in forest remnants of Los Tuxtlas as available continuous habitat keeps shrinking and habitat fragments become smaller and more isolated (Solórzano-García et al. 2012). Although records of cyclone disturbance on any population are virtually non-existent, this information is crucial to help diagnose population responses as the region is experiencing an increased incidence of these phenomena (Magaña et al. 1998; Portilla-Ochoa et al. 2006; Manson et al. 2009). The effect of cyclone disturbance in populations could be simulated through a PVA modeling framework running a distribution of severities to get a reasonable range of potential effects on populations (Morris & Doak 2002; Akçakaya 2005). However, for A. p. mexicana populations inhabiting forest fragments, high-quality, and long-term demographic data demanded by PVA modeling is yet to be collected (Cristobal-Azkárate et al. 2011; Cristobal-Azkárate et al.
2012). Given this limitation, an analogous system possessing this type of data could help to gain insights into the potential impacts that cyclones could generate on population extinction probabilities. In this vein, the translocated population of A. p. mexicana harboured in
Agaltepec Island, Veracruz, Mexico, embodies such a system. The isolation and limited resources of Agaltepec Island are common features observed in isolated forest fragments inhabited by this subspecies of howler monkey and other primates across southern Mexico
(Ramos-Fernández & Ayala-Orozco 2002; Estrada et al. 2006; Rodríguez-Luna et al. 2007;
Cristóbal-Azkárate et al. 2011; Solórzano-García et al. 2012). However, the population of
Agaltepec Island possesses the unusual characteristics of being predator-free and non-exposed
65 | P a g e to human disturbance such as hunting or habitat destruction (Carrera-Sánchez et al. 2003).
These factors can be artificially incorporated in PVA modeling to represent more realistic conditions and better reflect potential changes in population dynamics and extinction risk
(Morris & Doak 2002; Akçakaya 2005).
This study quantifies the effect of cyclones on the quasi-extinction risk for the population of A. p. mexicana inhabiting Agaltepec Island, Veracruz, Mexico (also referred here as
Agaltepec population). It does so using a PVA, where risk of quasi-extinction (estimated through a stage-structured stochastic model) is modelled as a function of the effect of different frequencies and intensities of cyclone disturbance on vital rates (survival and fecundity) and carrying capacity simultaneously. A risk-based sensitivity analysis (Akçakaya
2000) was also conducted in order to identify the vital rate most affecting quasi-extinction risk and therefore requiring particular attention in pre-emptive and mitigation management. In light of the observed cyclone activity throughout primate habitats in tropical regions of the world (Ameca y Juárez et al. 2013) and the expected increment of these phenomena in the next decades (Seneviratne et al. 2102), study systems as the one presented here can help to understand demographic consequences that extreme agents of disturbance could trigger on isolated populations already endangered via human agency for which robust data is still not available.
Methods
Study system: Agaltepec Island & A. p. mexicana
Agaltepec Island is located in the Catemaco Lake (18°27′N, 95°02′W; elevation < 400 m asl) southern Veracruz, Mexico. This is an 8.3 ha reserve composed by two main vegetation types: semi-evergreen rain forest (6.2 ha) with several successional states and pasture (2.3 ha)
(Asensio et al. 2007). More than 2,000 trees (dbh > 30 cm) have been identified and mapped
66 | P a g e comprising 58 genera and 63 species, 30 of which are consumed by A. p. mexicana
(Rodríguez-Luna et al. 1993; Carrera-Sánchez et al. 2003). In October 1988, two mature males and eight females of this subspecies were translocated to Agaltepec Island from isolated fragments in Los Tuxtlas region. The individuals have been monitored continuously since release. By September 2002 the population comprised 95 individuals classed in three groups: adults (55), juveniles (27) and infants (13).
A. p. mexicana (classified as critically endangered in the IUCN Red List of threatened species, Cuarón et al. 2008b) is an arboreal folivorous primate currently distributed from southern Mexico (excluding the Yucatan Peninsula) to southern Guatemala (Rodríguez-Luna et al. 2006). This diurnal subspecies possesses a multi-male multi-female group composition and may breed at any time of the year giving birth to one offspring (Glander 1980). A. p. mexicana feeds mainly on young leafs (Estrada 1984), but have a great adaptability to supplement their nutritional requirements by feeding on resources normally uncommon in their diet such as shrubs, vines and lianas (Asensio et al. 2007). This is particularly evident during shortages of their preferred food items caused by seasonality or extreme environmental change (Estrada 1984).
Model overview
In this study quasi-extinction risk is estimated to hit when the population size falls below a threshold of 5 individuals within a 40-year time period. As the average social group size of A. p. mexicana ranges between 8 to 23 individuals (Kelaita et al. 2011), it was assumed that half the size of the minimum threshold group size could trigger endogamy, decrease reproductive success and therefore increased risk of extinction (Lande et al. 2003). The 40-year time window covers around four times the subspecies’ generation length (12 years) (Cuarón et al.
2008b). The population viability analysis software RAMAS Metapop (Akçakaya & Root
67 | P a g e
2007) has been proven robust in predicting real population trajectories provided that demographic parameters derived from empirical data and the biology of the species is well known (Brook et al. 2000). In this regard, the howler monkeys of Agaltepec Island comprise a well-studied population in terms of demography, ecology and behaviour (e.g., Cortés-Ortiz et al. 1994; Carrera-Sánchez et al. 2003; Días & Rodríguez-Luna 2005). RAMAS Metapop
(version 5.0) was thus used to assess quasi-extinction risk with demographic processes tracked on annual time-step for 40 years running 1,000 simulations, the latter being the minimum standard suggested to determine quasi-extinction declines risk curves with precision (Akçakaya & Root 2007). Density-dependence was simulated using a ceiling effect, based on the abundance of all stages, allowing survival rates to decrease as population size increases above carrying capacity (Akçakaya 2000). Estimates of two human impacts, deforestation and hunting, were incorporated in the PVA as the population of Agaltepec
Island is not exposed to these pressures common through Los Tuxtlas region. Accordingly, deforestation was modelled as a reduction in carrying capacity of 5% per year; this is the annual deforestation rate observed in forest fragments in Los Tuxtlas for the period 2000-
2007 (Solórzano-García et al. 2012). The removal of one individual of the infant stage class per-year was modelled as a conservative estimate of hunting pressure (Duarte-Quiroga &
Estrada 2003). Hunting activity is present in Los Tuxtlas region and it is of concern for this subspecies (Rodríguez-Luna et al. 2006). Table 5.1 summarizes the input parameters upon which the PVA was based.
Input parameters
During the 14-year period Agaltepec population exhibited an annual average population growth rate (λ) of 1.16 estimated by the equation where λt (population growth rate at a time t) was estimated by λt = Nt+1 / Nt, where Nt is the number of individuals
68 | P a g e in the population at a time t (Cortés-Ortiz et al. 1994; Carrera-Sánchez et al. 2003). The first years after the release of the ten founder individuals the average population growth rate was as high as 1.27 individuals per year (Cortés-Ortiz et al. 1994). However from 1992 to 2002 it stabilized with no signals of decline suggesting that the carrying capacity of the system could have been reached (Carrera-Sánchez et al. 2003). Carrying capacity (K) is defined as the upper limit to population size, above which the population growth tends to decline (Akçakaya
2000). An indirect measure of carrying capacity consists in determining the maximum number of individuals that an area could support (e.g., Mandujano & Escobedo-Morales 2008; Li et al. 2009; Amaral-Nascimento & Schmidlin 2011). In a study on A. p. mexicana harboured in forest fragments, Mandujano & Escobedo-Morales (2008) defined carrying capacity as the maximum density found among the fragments inhabited by this subspecies. In such scenario, however, bias could exist due to individuals’ movement between habitat patches, low detectability of observers with the techniques employed, or loss of individuals due to hunting between censuses. The estimation of carrying capacity following the above definition is more robust when it is based on sampling populations inhabiting areas undisturbed by anthropogenic factors (Mandujano 2007). Contrary to the scenario in forest fragments, the combination of natural isolation of Agaltepec Island allowing total population counts with the absence of human impacts makes it possible to achieve a more accurate estimate. Agaltepec
Island has an area of 8.3 ha and the maximum population size of A. p. mexicana for the 14- year period was 95 individuals (Carrera-Sánchez et al. 2003). Accordingly, density for this time period is 11.4 individuals/ha. A 30% reduction of carrying capacity was used as a baseline estimate to simulate the amount habitat being lost in years that a cyclone strikes
(Table 5.2). This estimate was reached by consensus in an earlier assessment for the subspecies (Cortés-Ortíz & Rodríguez-Luna 1996, see below) and it was used in a viability analysis in other locations (Mandujano & Escobedo-Morales 2008). Based on the stable level
69 | P a g e of growth rate documented for Agaltepec population at the end of the 14-year period, in the present study this population is considered to be at its carrying capacity.
Fecundity rate (the proportion of offspring alive in the next time step t+1 per the total number of potential parents, individual adults of both sexes, in the previous time step) and survival rates are used as input parameters to model the dynamics of the population from time t to t + 1 in a 5 by 5 stage-structured matrix presented in Figure 5.1. The effects of stochasticity on fecundity, survival rates, and carrying capacity were assumed to be correlated. Environmental stochasticity (the effect of environmental fluctuations in population growth rate) was modelled from year to year using a standard deviation matrix of the vital rates (fecundity and survival rates) which were specified as elements of the stage projection matrix. Demographic stochasticity was included by sampling the number of survivors from a binomial distribution, and the number of offspring from a Poisson distribution (Akçakaya
2005).
Cyclones are considered a threat for A. p. mexicana populations in the Conservation
Assessment and Management Plan for Mexican Primates (CAMP, Rodriguez-Luna et al.
2006), and it has been recommended that viability analysis should take them into account when assessing population extinction risk. However, the short-lived and stochastic nature of cyclones makes it difficult to use them in PVA modeling frameworks because impacts on populations have not been documented. In absence of actual severities observed in populations an alternative in PVA modeling to infer impacts of catastrophes consists of running multiple models ranging across reasonable values for both frequency and intensity
(Morris & Doak 2002). In this regard, the Population and Habitat Viability Assessment for A. p. mexicana (PHVA, Cortés-Ortíz & Rodríguez-Luna 1996) facilitated by the IUCN/SSC
Conservation Breeding Specialist Group (CBSG) provided estimates of the impacts of cyclones on vital rates. Specifically, it was estimated a 30% reduction in survival rates and
70 | P a g e
10% in fecundity rates every year that a cyclone strikes, with the frequency of cyclone occurrence estimated at 0.1% per year (see Table 5.2) These estimates were reached by consensus among 38 participants of 17 institutions with expertise on the subspecies’ life history, ecology and the natural history of its habitat in Mexico. Furthermore the estimate of cyclone frequency of occurrence is in agreement with the observed record between 1980-2006 impacting the subspecies’ extant distribution in Mexico (0.1% per year, UNEP 2005,
Accessed June 2012). RAMAS Metapop allows simulations of the impact of catastrophic events on population parameters based on user-defined multipliers (Akçakaya 2005). Using the baseline estimates of the PHVA, 3 alternative scenarios of cyclone frequency each with 3 different effects of cyclone intensity, 2 scenarios with no cyclone impacts, and 1 scenario with only cyclone impacts were developed (see Table 5.2). By using this approach the expectation is to get a better sense of how different severities could influence quasi-extinction risk and how different these risks could be from scenarios where the effect of cyclones is zero but human impacts are present and vice-versa. Finally, in a risk-based sensitivity analysis for all the scenarios, each vital rate was decreased by 10% while holding cyclone intensity and all other parameters constant. This was with the aim to identify the element affecting the most population quasi-extinction risk (Akçakaya 2000).
Results
Quasi-extinction risk & sensitivity analysis
Focusing on the scenario with no cyclone or human impacts (VI), population size fluctuated below and above the carrying capacity (Table 5.3) without reaching quasi-extinction.
However, models considering a low cyclone frequency (Scenario I) revealed a quasi- extinction risk above 60% at 40 years for the three simulated levels of intensity to affect vital rates and carrying capacity (Table 5.4). The effects of cyclone frequency on vital rates and
71 | P a g e carrying capacity proposed in the PHVA for the subspecies (Scenario II) generated a quasi- extinction risk ranging between 9% and 23% for the three simulated levels of cyclone intensity by 20 years (population size of around 16 individuals), and up to 93% by 40 years
(Table 5.4). A similar increment in quasi-extinction risk was revealed in the models of high cyclone frequency (Scenario III): probability of reaching the quasi-extinction threshold was above 95% by 40 years. For each of the scenarios where cyclone impacts are present (I-III and V), the highest quasi-extinction risk was found for the models with high cyclone intensity. For the scenario with only human impacts (IV), population size revealed a steady decline but risk of quasi-extinction remained below 5% by 40 years (Table 5.4). Additional simulations over 60 years revealed that quasi-extinction was reached at approximately 58 years (See panel j in Appendix Figure S3). In comparison, for the scenario with only cyclone impacts (scenario V), probability of reaching quasi-extinction revealed to be approximately three times higher by 40 years considering the baseline values for frequency and intensity
(panel k in Appendix Figure S3).
The analysis of sensitivity for those values to each vital rate (survival rates and fecundity rates) for the scenarios of different cyclone frequency (scenarios I-III and V) and no cyclone impacts (scenario IV and VI) while keeping cyclone intensity constant are reported in Table
5.5. It is clear that the adult survival generated the greatest change in the probability of reaching population quasi-extinction, followed by the survival of infants: for the scenario with low cyclone intensity the probability of reaching the quasi-extinction threshold by 40 years shifted from 63.1% to 74.2% and from 64% up to 67%, respectively (Table 5.4). The greatest change in quasi-extinction time was produced in the scenario with only cyclone impacts (Scenario V). Quasi-extinction risk was less sensitive to other survival rates, and fecundity.
72 | P a g e
Discussion
Population modeling
For population viability analysis to provide relatively robust estimates of extinction risk, long-term demographic data must be available and the life-history of the species of concern must be well-known (Brook et al. 2000; Akçakaya et al. 2004; Akçakaya et al. 2005). These conditions are rarely available for populations of globally threatened species. Furthermore, in order to provide reliable estimates of the effect of catastrophes on extinction probabilities, modeling assumptions must be realistic pertaining to the likely impacts of extreme disturbance on both the population and its habitat (Akçakaya et al. 2000; Morris & Doak
2002). The PVA for A. p. mexicana population in Agaltepec Island benefits from robust knowledge of the biology and demography derived from long-term field research (Cortés-
Ortiz et al. 1994; Carrera-Sánchez et al. 2003) as well as from knowledge on the natural history of the subspecies’ habitat in Mexico (Guevara 2004; Días & Rodríguez-Luna 2005;
Asensio et al. 2007). Admittedly, no studies so far have documented impacts of cyclones on this or any population of A. p. mexicana. The effect of cyclone impacts, as modelled here, can be simulated running a distribution of severities to get a defensible range of potential effects on population survival (Morris & Doak 2002; Akçakaya 2005). The rest of input parameters used in the models, other than human impacts, were derived from the actual population and should reflect robust population trajectories. A. p. mexicana population in Agaltepec Island is not affected by human impacts and therefore estimates were extrapolated from known conditions in forest fragments throughout Los Tuxtlas region, to represent more naturalistic conditions. Parasitic diseases (Cristóbal-Azkarate et al. 2010) and insect infestations
(Cristóbal-Azkarate et al. 2012b) were recently documented in some populations in this region but their effects on population survival are unknown and were therefore not modelled.
73 | P a g e
A. p. mexicana, cyclone impacts & population quasi-extinction risk
Howler monkeys of Agaltepec Island, share ecological (activity patterns, diet composition) and demographic (sociometric composition, average group size) features within the range of variation reported for howler monkey populations which are harboured in forest fragments in
Los Tuxtlas, Mexico (Gómez-Marín & García-Orduña, 1996; Cristóbal-Azkarate et al. 2005;
Asensio et al. 2007; Diaz & Rodríguez-Luna 2005). Forest fragments in Los Tuxtlas are characterized by rain forest vegetation in several successional states whose attributes and structure can have important effects upon individuals’ fitness and survival (Rodríguez-Luna et al. 2007; Solórzano-Garcia et al. 2012). When these habitats are both saturated and migration is limited, depletion of resources could come quickly. As observed by Asensio et al. (2007), in both Agaltepec Island and forest fragments in Los Tuxtlas region, A. p. mexicana copes with these circumstances by extending the time travelling in search of food (sometimes on the ground), reducing resting times and displaying group partitioning. In addition, A. p. mexicana exhibits a diversified folivorous diet (Cristóbal-Azkarate et al. 2005; Cristóbal-Azkarate &
Arroyo-Rodríguez 2007) positively correlated with secondary vegetation as the one reported for Agaltepec and that typically found in forest fragments (López-Galindo & Acosta-Pérez
1988; Guevara 2004). As a result, availability of high quality foliage may allow howlers in both habitats sufficient resources to persist for an extended period of time (all else been equal) without significant competition (Asensio et al. 2007).
A. p. mexicana is a highly arboreal primate, and this makes it highly sensitive to strong winds and heavy rainfall generated by cyclones. When return periods of cyclones are long and intensity is low, affected systems can have more chances to bounce back, albeit slowly, towards pre-cyclone conditions than when cyclone frequency and intensity is elevated (Lugo
2008). With this rationale, more intense cyclones may increase risk of mortality of individuals after being hit by large tree branches or after falling from trees whereas high frequency of
74 | P a g e occurrence, apart from causing direct mortality, may decrease habitat recovery times and production of key food resources. In this context, the effect of low frequency and intensity of cyclones on A. p. mexicana population as modelled in this study (incorporating the effect of human impacts, see Table 5.1) revealed < 5% probability of reaching quasi-extinction by 20 years. However, 0.05% increase in cyclone frequency of occurrence from the 0.1 baseline estimate per year revealed a quasi-extinction probability of 40%. Thus, cyclone frequency and intensity are likely to play an important role in shaping population survival for exposed populations.
For A. p mexicana reduction in habitat and food amounts can increase antagonistic interactions, physiological stress and suppression of normal functioning of the immune system (Bicca-Marques 2003; Martinez-Mota et al. 2007; Trejo-Macias & Estrada 2012).
Coping strategies under these circumstances range from adaptability to survive on very low- quality diets to adjustment of activity patterns, group size, and cohesion to minimize competition for available resources. These coping strategies have been documented in fragmented landscapes and/or continuous habitat exposed to extreme disturbance (Rodríguez-
Luna et al. 1996; Serio-Silva & Rico-Gray 2000; Arroyo-Rodríguez & Mandujano 2006;
Aristizabal-Borja et al. 2011; Behie & Pavelka 2012). Certainly, the recurrent exposure of a species to a given disturbance is expected to shape its intrinsic adaptability to that disturbance, reducing its likelihood of extinction from this source (Lande et al. 2003). For instance, Dittus
(1985) described how recurrent cyclones affected much of the food supply for two folivorous monkeys inhabiting the dry evergreen forest of Sri Lanka, and documented how survivors fulfilled their nutritional requirements by shifting their diets towards a greater proportion of leaves. While it might be expected that species recurrently exposed will possess the behavioural and physiological flexibility to cope with conditions of extreme disturbance, such flexibility may have its limitations. Pavelka et al. (2007) documented a large decline 3.5 years
75 | P a g e after a cyclone hit the forest riverine habitat of Alouatta pigra (a closely-related species of A. p. mexicana) in Belize: over 80% of the individuals disappeared linked to the cumulative effect of stress factors such as food scarcity, social disorganization and habitat degradation.
For A. p. mexicana, population growth in fragmented landscapes is related to the effect of food scarcity and habitat quality on fecundity and the survival of infants and reproductive adults (Bicca-Marques 2003; Arroyo-Rodríguez & Mandujano et al. 2006; Mandujano &
Escobedo-Morales 2008; Pave et al. 2012). In view of the above, the effect of extreme events may be biased towards particular vital stages and knowing which are most affected could inform management to improve rates of recovery. For A. pigra in Belize, Pavelka et al. (2007) reported low survival of infants the next 2-3 years after the cyclone. In this study for A. p. mexicana, the sensitivity analysis of vital rates revealed that increased mortality of individual infants seconded the greatest changes in quasi-extinction probabilities, with adult survival been the most influential factor. The PVA presented here suggests that future action plans for
A. p. mexicana populations should pay particular attention on these life stages.
The population of Agaltepec Island can help to understand the consequences that cyclones would have on the viability of populations in fragments for which this quality of data is yet to be produced. However the effects of cyclone impacts should be modelled within PVA taking into account the impacts of anthropogenic disturbances. In this study, accounting for two conservative estimates of the effect of human impacts impacting vital rates and carrying capacity in absence of cyclones, the probability of reaching population quasi-extinction remained below 5% by 40 years (slightly over three A. p. mexicana generations). Conversely, a scenario modeling low cyclone frequency and intensity with human impacts together revealed three times more such a risk by 30 years. Increment in cyclone frequency could lead to lowered habitat resilience and increased susceptibility of local populations to subsequent
76 | P a g e cyclones and pre-existent anthropogenic disturbance. As a result recurrently exposed populations could face an elevated risk of extinction.
This PVA help us to understand demographic consequences that could arise in isolated primate populations exposed to different degrees of cyclone disturbance. Current exposure to cyclones for many primate species and subspecies is alarmingly high (Ameca y Juárez et al.
2013) and frequency of occurrence of these phenomena is expected to increase in the coming decades (Seneviratne et al. 2012). For those populations constrained to degraded habitats and experiencing heavy anthropogenic pressure, the resulting synergies will exacerbate risks of population extinction.
Supporting Information
Appendix Figure S3. Plots of quasi-extinction risk for A. p. mexicana population in Agaltepec Island.
77 | P a g e
Conclusions
Natural population die-offs, extreme climatic events and lessons for conservation
Most conservation efforts are committed to tackle human-mediated impacts (Davies et al.
2006; Ceballos et al. 2010; Wearn et al. 2012) giving limited attention to threats of indirect anthropogenic origin, such as those associated with extreme natural events (ENEs), including extreme climatic events. These phenomena have caused high mortalities in wildlife populations coined as natural population die-offs (NPDOs) (Young 1994). There is a growing number of studies documenting NPDOs and may be not surprisingly, a consensus on how to define them remained to be presented until recently (Ameca y Juárez et al. 2012). This was not an easy task because such a definition has to be broad to be applicable to a majority of organisms and at the same time, been consistent with the specific organisms’ biologies and life histories. It is also of concern that there is very limited work in assessing the consequences of NPDOs on the long term viability of the affected populations. As evident from the first section of this thesis, these issues were tackled by i) providing a definition of die-off taking into account the species-specific mortality rates in absence of exposure to extreme phenomena and ii) integrating a framework to compile species traits known to shape sensitivity and/or adaptability for a given type of disturbance. This synthesis improves our ability to identify populations most at risk.
Across a range of mammalian groups, previous studies have found certain biological traits are significant indicators of extinction risk (e.g., Purvis et al. 2000; Isaac & Cowlishaw 2004;
Cardillo et al. 2008). It is therefore plausible that intrinsic biology might also be associated with lower resilience to ENEs. The aim to confirm this hypothesis was addressed by examining the effects of the above traits on the degree of population loss caused by ENEs.
The main finding in this regard for a subset of terrestrial mammals (Chapter 2) showed that that susceptibility to large NPDOs decreases with increased home range size for a given body
78 | P a g e mass wide ranging mammals are less susceptible to large NPDOs than narrow ranging mammals. Our results suggest that the mechanisms underlying population losses from extreme natural events and direct anthropogenic forces are fundamentally different.
Although the explanatory power of predictors can certainly be improved, the results provide insights towards the identification of species most vulnerable to extreme natural events.
Global climate change is expected to increase the intensity and frequency of extreme climatic events with potentially severe consequences for the terrestrial biota (UNEP 2012;
IPCC 2012). In this regard, the research presented in Chapters 1 and 2 has clarified the mechanisms by which extreme climatic events are likely to increase species’ extinction risk
(based on the sensitivity and adaptability of species). The IUCN Red List broadly classifies species as been impacted by a variety of climate change phenomena. Surprisingly, the assessment of level of species’ exposure to these phenomena remained pending. In chapter 3 an analysis aiming to improve the existing approach taken by IUCN is introduced. This assessment focus on terrestrial mammals recently exposed to cyclones and droughts and revealed that significant numbers of species at risk from both phenomena are not currently listed in the IUCN Red List of Threatened Species under the category “Climate Change and
Severe Weather”. Exposure by itself does not necessarily equals risk; this will also depend on the species’ intrinsic characteristics and adaptability to climate change and extreme climatic events.
With extreme natural events being expected to occur more frequently and with greater intensity in the coming decades, there is increasing awareness that such events represent a growing threat to wildlife populations and their habitats. This is why vulnerability assessments to climate change impacts are gaining attention in conservation biology (Glick et al. 2011; Crossman et al. 2012; Watson et al. 2012). Building upon the knowledge generated in chapters 1 to 3 a vulnerability assessment to cyclone-driven population declines was
79 | P a g e implemented for Mexican non-volant terrestrial mammals. The integration of information pertaining to exposure to extreme climatic events and species’ intrinsic biology as presented in our case study represents a simple yet highly informative framework to assess vulnerability to these types of phenomena and inform existing climate change action plans for species conservation. Extreme natural events, including climate extremes, represent a growing threat to biodiversity, and this might be particularly true for populations already under pressure due to habitat degradation or overexploitation. In this regard, population viability analysis (PVA) can help identifying the likely impacts that such disturbances can exert on the demography of populations. Unfortunately, long-term studies quantifying their impact on population growth and trajectory are rare. This type of information would be particularly relevant for populations inhabiting fragmented and degraded landscapes where extreme climatic events can be expected to substantially increase the risk of “island extirpation”. In the final chapter of the thesis a PVA was performed to understand demographic consequences that could arise from different cyclone severities (frequency and intensity) on isolated populations. This can allow us to prioritise and adapt current management to guarantee the survival of those vulnerable populations. For those constrained to degraded habitats and experiencing heavy anthropogenic pressure, the resulting synergies will exacerbate risks of population extinction.
Future research plan
The research developed through this thesis evidences that although nature and processes by which anthropogenic disturbance drives the extinction vortex has been reported by many
(Vitousek et al. 1997; Ceballos et al. 2010) less is known about the impacts of extreme climatic events (ECEs) on the fate of biodiversity (Parmesan et al. 2000; Jentsch et al. 2006).
Recent frameworks and my own exposed in this thesis help clarify the mechanisms by which
ECEs are likely to increase extinction risk now and in the future (Foden et al. 2008; Dawson
80 | P a g e et al. 2012). Species being unable to develop coping strategies soon enough under an increased exposure to acute disturbance within their present habitats (e.g. being unable to persist in situ or track promptly suitable environmental refugia) may face an increased risk of population extirpations and, in consequence, increased risk of species extinction. Thus, there is a critical urgency to identify, within areas at risk of ECEs’ occurrence, the species which could benefit from tailored management strategies prior, during or in the aftermath of episodes of acute disturbance, as well as the species that may still persist with minimum intervention. This will be a particular element of my further research with focus on amphibians. Amphibians have undergone substantial declines and extinctions due to anthropogenic stressors on many fronts (Stuart et al. 2004). This trend is already being exacerbated by climate change impacts including ECEs (McMenamin et al. 2008; Alford et al. 2011; Scheele et al. 2012). Amphibians’ populations distributed in regions displaying limited permeability (natural landscape heterogeneity and/or human encroachment) to allow movement towards danger-free refugia are likely to be the most vulnerable (Shoo et al 2011;
Bickford et al. 2010). In line with the key priority research gaps of the global Amphibian
Conservation Action Plan pertaining to climate change and population declines (Gascon et al.
2007), my future research plan will center on i) the characterization of amphibians’ vulnerability to population extirpations from droughts and floods, ii) the identification of areas where species may be able move to and persist in under changing environmental conditions, and iii) how this information can be used to ensure the viability of populations. A species highly predisposed from its biology (e.g. limited recovery potential due small clutch size, late age at first reproduction or short oviposition season) and having its extant distribution most exposed, all else been equal, is expected to be the most vulnerable to population extirpations. Relevant climate data pertaining to amphibian biology (e.g. maximum temperature of the warmest month, minimum temperature of the coldest month,
81 | P a g e temperature/precipitation seasonality) defining present conditions from their known distributions will be extracted from the WorldClim database to generate future decadal scenarios by 2050 using the MAGICC/SCENGEN software (Hijmans et al. 2005). For selected species, habitat suitability maps (time series of maps) will be built using a maximum entropy approach (Phillips et al. 2006) to identify areas in which highly vulnerable species could find suitable refugia from particular ECEs. For exposed species, projected future conditions will be used to map danger-free areas with potential to become refugia. For these set of exposed species a vulnerability assessment to ECEs-driven population losses will be then produced.
82 | P a g e
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1 List of Tables 2 Table 1.1 Examples of extreme natural events reported in the literature as related to recent natural population die-offs.
Type of natural hazard a Extreme natural events b Related die-offs
Hydrometeorological Cold wave (Pavelka & Behie 2005; Process or phenomenon of atmospheric, hydrological or oceanographic Extreme weather, extreme temperature, cold temperatures. Scorolli et al. 2006) nature that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic Cyclone (Taylor et al. 2006; Miller disruption, or environmental damage. Hurricane, tropical storm, tropical depression, typhoon. & Barry 2009)
Drought (Foley et al. 2008) Deficiency of precipitation, desertification, pronounced absence of rainfall.
Flood (Prins & Douglas- Inundation; includes: flash floods. Hamilton 1990)
Heat wave (Gordon et al. 2006) Extreme weather, extreme temperature, high temperatures.
Wild fire (Woolley et al. 2008; Bush fire, forest fire, uncontrolled fire, wildland fire. Berenstain 1986)
Biological Epidemic (Bermejo et al. 2006) Process or phenomenon of organic origin or conveyed by biological A disease affecting or tending to affect a disproportionately large vectors, including exposure to pathogenic micro-organisms, toxins and number of individuals within a population, community, or region at bioactive substances that may cause loss of life, injury, illness or other the same time. health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage. Insect infestation (Elkan et al. 2009) Spreading or swarming in of various kinds of insects over or in a troublesome manner.
Geological Volcanic activity (Hilton et al. 2003) Geological process or phenomenon that may cause loss of life, injury Crater, lava, magma, molten materials, pyroclastic flows, volcanic or other health impacts, property damage, loss of livelihoods and rock, volcanic ash. services, social and economic disruption, or environmental damage. 3 4 a Categories and definitions following the terminology of the United Nations International Strategy for Disaster Risk Reduction (UNISDR 2009). 5 b Basic definitions of extreme natural events are collated from the web-based project of the UNISDR (http://www.preventionweb.net).
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6 Table 2.1 Summary of species’ biological traits predicted to shape NPDO severitya
Biological traitb Hypothesis Rationale Citation Adult body mass Small-sized species should show Small-sized species tend to exist at higher population densities than Davies et al. 2006 Price Mass in kilograms of an individual larger individual losses than large- large species. This could lead to higher individual losses during & Gittleman 2007 adult of the species. sized species. episodes of unusually high resource scarcity (e.g., increased competition for scarce food or water) caused by extreme phenomena. Small-sized species could also possess less physical robustness to direct impacts from environmental disturbance, i.e., be more easily injured or weakened, and thus easy target of predators or diseases.
Foraging strategy Grazers and mixed feeders should Vegetation can be affected by acute environmental changes. Cooper et al. 1988 Preferred dietary selectivity for experience larger individual losses Species with a browsing dietary selectivity (at difference of grazers Shipley 1999 plants: mainly grazing, mainly than browsers. and mixed feeders) might be less likely to exhibit die-offs due to Worden et al. 2010 browsing or a combination of both their physiological efficiency which confers high adaptability to without marked preference (mixed exploit alternative food items which can persist during resource- feeding). limiting periods.
Home range size Species with small home range Assuming no constraints in habitat availability species with large Russell et al. 1998 Size of the area within which should experience larger individual home range can have a greater mobility which might minimize Cardillo et al. 2005 everyday activities of individuals losses than species with larger exposure to disturbance. In addition, such species might find easier Davidson et al. 2009 or groups (of any type) are home ranges. to move to areas where limited resources are still available despite typically restricted. harsh changes in the environment.
Territoriality Territorial species should Animals that exhibit territoriality might have a high exposure to Lande 1987 Ability of defending particular experience larger individual losses extreme events both because individuals might be less predisposed Vaughan et al. 2010 areas within the home range for than non-territorial species. to abandon defended areas, and because the existence of territorial exclusive use of shelters, foraging neighbours makes it difficult to move to new areas. or reproduction grounds.
7 aNPDO severity is defined as the degree of population loss experienced by a population due to extreme natural events. 8 bUnits and codes of biological traits used in PGLS analysis as follows: Adult body mass (kg), foraging strategy (1 = mixed feeding & grazing, 2 = browsing), home range size (square 9 kilometres), and territoriality (1 = present, 2 = absent). 10
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Table 2.2 PGLS model of Home range size and Adult body mass as predictors of NPDO severitya. Model based on 72 observations (NPDOs); coefficient estimate (Slope) and standard error (SE) are given. Adjusted R square (R2) informs on the amount of variation explained by the model.
Parameterb Slope SE t p
Intercept 0.46 0.23 2.00 0.05
Home range size 0.34 0.12 2.81 0.006
Adult body mass 0.24 0.11 2.12 0.04
Home range size * Adult body mass -0.18 0.06 -3.13 0.003
R2 = 0.12 aNPDO severity is defined as the degree of population loss experienced by a population due to extreme natural events. bHome range size (Km2) and Adult body mass (Kg) were log +1 transformed. Asterisk indicates interaction.
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Table 2.3 PGLS model of Home range size and Foraging strategy as predictors of NPDO severitya. Model based on 72 observations (NPDOs); coefficient estimate (Slope) and standard error (SE) are given. Adjusted R square (R2) informs on the amount of variation explained by the model.
Parameterb Slope SE t p
Intercept 0.99 0.12 8.28 <0.05
Home range size -0.14 0.058 -2.42 0.02
Foraging strategy (Grazing & Mixed feeding) -0.07 0.13 -0.56 0.58
Home range size * Foraging strategy (Grazing & Mixed 0.14 0.07 2.20 0.03
feeding) R2 = 0.10 aNPDO severity is defined as the degree of population loss experienced by a population due to extreme natural events. bHome range size was (Km2) +1 log transformed. Foraging strategy is a categorical variable (Browsing versus Mixed feeding and grazing). Asterisk indicates interaction.
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Table 2.4 PGLS models of NPDO severitya as ranked by the Akaike information criterion corrected for small samples (AICc). NPDO Severity is defined as the degree of population loss experienced by a population due to extreme natural events. Branch-length transformation using the parameter lambda was performed to improve the fit of the data to the phylogeny using maximum likelihood.
b Model AICc
Home range size * Adult body mass 14.92
Home range size * Foraging strategy 16.10
Foraging strategy 17.91
Adult body mass * Foraging strategy 18.04
Foraging strategy * Territoriality 20.40
Home range size 20.94
Adult body mass 21.08
Territoriality 22.38
Adult body mass * Territoriality 23.07
Home range size * Territoriality 23.89 aNPDO severity is defined as the degree of population loss experienced by a population due to extreme natural events. bBiological traits used in the models are coded as follows: Adult body mass (Kg), Foraging strategy (1 = mixed feeding & grazing, 2 = browsing), Home range size (Km2), and Territoriality (1 = present, 2 = absent). Asterisk indicates interaction.
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Table 3.1 “Threatened" and “Non-Threatened” Terrestrial mammals at high exposure to cyclones and droughts by taxonomic Order.
Highly exposed to cyclones Highly exposed to droughts Threatened Non-threatened Threatened Non-threatened Afrosoricida 6 23 5 8 Carnivora 6 5 5 2 Cetartiodactyla 7 3 5 4 Chiroptera 29 44 31 22 Dasyuromorpha 1 5 0 0 Diprotodontia 2 1 6 0 Eulipotyphla 6 18 25 7 Lagomorpha 3 4 0 0 Peramelemorphia 1 0 0 0 Primates 40 9 55 15 Rodentia 35 53 43 20
Totals 136 165 175 78
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Table 4.1 Indicators of exposure, sensitivity and non-adaptability used to assess vulnerability to cyclone-driven population declines for non-volant mammals classed in danger of extinction by Mexican law (Nom-059-Ecol-2001).
Category Indicator Description
Overlap of cyclones’ Cyclones overlapping at least 25% of a species’ range might impact a tracks with species’ significant number of individuals particularly for territorial species extant geographic with poor dispersal and/or poor adaptability to persist in-situ. range
Vulnerability to population die-offs may increase in presence of anthropogenic disturbance preventing dispersal (e.g., settlements,
roads, fences) or generating additional sources of mortality (e.g., due to hunting or transmission of diseases). As the overlap of cyclones
Exposure with a species’ range increases, the demographic stability of extant populations could be seriously affected assuming limited availability of non-disturbed areas for dispersal within its range: vulnerability to population die-offs might increase with high sensitivity and poor adaptive capacity.
Low vagility Cyclone-induced mortality is expected to depend of the relative vagility of individuals in escaping from strong winds, heavy rain and/or flooded areas, particularly in presence of anthropogenic
disturbance. Highly vagile species could move through its range swiftly which may decrease chances of direct exposure. For low-
mobility species (versus highly vagile species) vulnerability to population die-offs may be enhanced if the level of exposure affects
Sensitivity the most of its range but lessened if the species possess adaptive capacity to persist in situ.
Territoriality Territorial individuals might be less predisposed to abandon preferred areas (e.g., sleeping sites, latrines, feeding sites, breeding sites) and/or the existence of territorial neighbours may makes it difficult to move to occupied areas. Territorial species might be highly vulnerable if they are slow moving and exhibit a high level of exposure.
Non-ability to use Cyclones can drastically reduce key elements used by species as alternative habitats shelter from adverse weather conditions or protection from predators (Habitat specialist) (e.g., thermal, screening/escape cover). Therefore individuals heavily reliant on specific habitat elements may find it difficult to survive in
alternative areas deprived from such elements. Vulnerability to
population die-offs is expected to be high if, in addition, the overlap of cyclones with the species’ range increases.
Non-ability to use For some species the bulk of their diet may consist mostly of specific adaptability - alternative food items which could be diminished during cyclones. These species may resources (Food Non find it difficult to feed for a sustained time interval from alternative specialist) resources, and consequently become more susceptible to starvation and/or less resistant to diseases leaning towards mortality events. Vulnerability to population die-offs in such diet-specialists is expected to increase, for example, with increased exposure and habitat specificity.
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Table 4.2 Non-volant mammals (n = 19) listed in danger of extinction by Mexican law (Nom-059-Ecol-2001) assessed for cyclone-driven population declines. Criterion for assessing vulnerability in Figure 4.1. Detailed indicators of vulnerability as in Table 4.1
ID Species Order Exposure Sensitivity Sensitivity Non-adaptability Non-adaptability Total scores & Vulnerability Species’ extent range impacted by cyclones % Low vagility Territoriality Habitat specialist Food specialist rank 2 Ateles geoffroyi yucatanensis Primates 98 0 1 0 1 2,98 High 3 Ateles geoffroyi vellerosus Primates 56 0 1 0 1 2,56 High 1 Cyclopes didactylus Pilosa 50 1 1 0 1 2,50 High 4 Procyon pygmaeus Carnivora 100 0 1 0 0 2,00 Moderate 6 Alouatta pigra Primates 95 0 1 0 0 1,95 Moderate 8 Eira barbara Carnivora 76 0 1 0 0 1,76 Moderate 7 Tamandua mexicana Pilosa 73 1 1 0 0 1,73 Moderate 10 Zygogeomys trichopus Rodentia 68 0 1 0 0 1,68 Moderate 14 Tapirus bairdii Perissodactyla 66 0 1 0 0 1,66 Moderate 13 Ursus americanus Carnivora 58 0 1 0 0 1,58 Moderate 12 Castor canadensis Rodentia 51 0 1 0 0 1,51 Moderate 9 Lepus flavigularis Lagomorpha 47 0 0 1 0 1,47 Moderate 11 Antilocapra americana Cetartiodactyla 46 0 1 0 0 1,46 Moderate 5 Erethizon dorsatum Rodentia 30 1 1 0 0 1,30 Moderate 17 Cabassous centralis Cingulata 96 0 0 0 0 0,96 Low 15 Leopardus pardalis Carnivora 86 0 0 0 0 0,86 Low 16 Leopardus wiedii Carnivora 77 0 0 0 0 0,77 Low 18 Panthera onca Carnivora 74 0 0 0 0 0,74 Low 19 Chironectes minimus Didelphimorphia 74 0 0 0 0 0,74 Low
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Table 5.1 Parameters used in the PVA analysis of A. p. mexicana population in Agaltepec Island. (See Methods, Chapter 5).
Parameters Description
Population size (N = 95) 13 infants (i)
8 first year juvenile (J1)
8 second year juvenile (J2) 11 sub-adults (SA) 55 adults (A) Growth rate (λ) 1.16 Fecundity rate (Fec) 0.50
Survival rates (Sx) Infants Si = 0.76
First year juvenile Sj1 = 1.00
Second year juvenile Sj2 = 1.00
Sub-adult SSA = 1.00
Adult SA = 0.84 Carrying capacity 11.4 individuals / ha Density dependence Ceiling effect based on the abundance of all stages
Stochasticity Effects on Fec, Sx, and K, assumed to be correlated Catastrophe (Cyclone) Frequency of cyclone occurrence 0.1% per year Intensity of cyclone: Reduction of 30% survival rates, 10% fecundity and 30% carrying capacity when a cyclone strikes. These estimates of frequency and intensity are used as baseline values to derive alternative scenarios (See Table 5.2). Human impact Hunting (removal of 1 individual infant every year) and, Habitat deforestation (carrying capacity is reduced 5% every year) Simulations 1,000 Time steps Annual (for 40 years)
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Table 5.2 Scenarios used in the PVA of A. p. mexicana population in Agaltepec Island. For scenarios I- III and V, two different levels of cyclone intensity were simulated by changing ±5% the baseline values of 30% reduction in survival rates, 10% reduction in fecundity and 30% reduction in carrying capacity expected to take place when a cyclone strikes (PHVA, Cortés-Ortíz & Rodríguez-Luna 1996). In scenarios IV and VI cyclone impacts are absent. Effects of human impacts in scenarios I to IV are described in Table 5.1 (Chapter 5).
Scenarios Probability of cyclone frequency per year
I Low frequency 0.05% II Baseline frequency 0.1% III High frequency 0.15%
IV Only human impact - V Only cyclone impact 0.1% VI No human or cyclone impact -
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Table 5.3 Average population abundance of A. p. mexicana population for the scenarios used to estimate quasi-extinction risk. For scenarios I, II, III, and V, different levels of cyclone intensity (low, baseline, high) are shown.
Average population abundance (±SD)
Scenarios by 10 years by 20 years by 30 years by 40 years
I Low frequency Low intensity 47(36-58) 23(15-32) 11(5-17) 5(1-8) Baseline intensity 47(36-58) 23(15-32) 11(5-16) 4(1-8) High intensity 46(34-58) 23(14-32) 19(5-16) 4(1-8) II Baseline frequency Low intensity 39(26-53) 16(8-25) 6(1-10) 1(0-4) Baseline intensity 38(24-52) 16(7-24) 6(1-21) 1(0-4) High intensity 38(24-53) 16(6-25) 5(0-11) 1(0-4)
III High frequency Low intensity 34(20-48) 11(3-19) 3(0-7) 0.6(0-2) Baseline intensity 32(18-47) 11(3-19) 3(0-6) 0.6(0-2) High intensity 32(16-47) 10(2-18) 2(0-6) 0.5(0-1)
IV Only human impact 55(54-56) 33(32-34) 19(18-20) 10(9-12)
V Only cyclone impact Low-Low 76(54-99) 65(40-89) 55(29-80) 47(22-71) Baseline 64(38-90) 45(20-70) 33(10-55) 23(4-42) High-high 50(23-78) 29(6-51) 18(0-36) 10(0-24)
VI No human or cyclone impact 95(84-105) 94(84-104) 94(83-104) 94(83-105)
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Table 5.4 Probability of quasi-extinction risk of A. p. mexicana population in Agaltepec Island. For scenarios I, II, III, and V, different levels of cyclone intensity (low, baseline, high) are shown. Probability of quasi-extinction is provided as a percentage every ten years with a 95% confidence interval. The highest probability of quasi-extinction was found for the models with high cyclone intensity (highlighted in bolds at the end of the 40-year time-window). (Chapter 5).
Probability of quasi-extinction (95% CI)
Scenarios by 10 years by 20 years by 30 years by 40years
I Low cyclone frequency Low intensity 0(0-4) 2(0.4-6) 18(15-21) 63(60-65) Baseline intensity 0.01(0-2) 3(1-7) 18(15-21) 65(63-68) High intensity 0.02(0-3) 4(2-7) 20(17-23) 68(65-71)
II Baseline cyclone frequency Low intensity 0.04(0-3) 9(6-12) 53(50-55) 93(90-96) Baseline intensity 0.06(0-3) 18(15-20) 54(51-57) 91(88-94) High intensity 01(0-4) 23(20-26) 57(54-59) 93(90-96)
III High cyclone frequency 1(0-4) 27(24-30) 79(76-82) 96(94-99) Low intensity 2(0-4) 32(29-35) 81(78-84) 97(94-100) Baseline intensity 3(0.06-6) 45(42-48) 83(80-85) 98(96-100) High intensity
IV Only human impact* 0(0-2) 0(0-2) 0(0-2) 3(0-3) V Only cyclone impact Low frequency & intensity 0.1(0-2) 0.7(0-3) 2(0-5) 4(1-6) Baseline frequency & intensity 0.3(0-3) 2(0-5) 10(7-12) 19(16-21) High frequency & intensity 2(0-5) 16(13-19) 37(34-39) 57(54-60) VI No human or cyclone impact 0 0 0 0
* Probability of quasi-extinction by 50 years was about 42% and 100% by 60 years (See Appendix Figure S3, panel j).
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Table 5.5 Sensitivity analysis for the scenarios of different cyclone frequency, while holding intensity constant, and no cyclone impacts affecting quasi-extinction risk. Each vital rate was decreased by 10% while holding cyclone intensity and all other parameters at their baseline values: Corresponding probabilities of quasi-extinction risk at 40 years (annual time-step for 40 years running 1,000 simulations) are shown outside brackets. For comparison, the probabilities of quasi-extinction holding all variables constant are given within brackets. The greatest change in quasi-extinction risk for all the scenarios of different cyclone frequency was generated by the adult survival.
Scenarios
I II III IV V VI Low Baseline High Only human Only cyclone No human or
cyclone cyclone cyclone impact impact* cyclone Vital rates frequency frequency frequency impact
Infant 67.0 (64.0) 93.4 (94.5) 98.1 (99.0) 0.009 (0) 14.5(14.2) 0 (0) Juvenile 1 68.8 (65.1) 93.2 (94.0) 98.4 (98.9) 0.006 (0) 17.7(13.0) 0 (0) Juvenile 2 68.8 (64.7) 93.8 (92.3) 97.0 (98.2) 0.006 (0) 17.9(14.2) 0 (0) Sub-adult 67.3 (65.6) 94.1 (93.6) 99.5 (97.2) 0.009 (0) 17.1(12.7) 0 (0) Adult 74.2 (63.1) 95.7 (92.7) 99.7 (98.9) 0.040 (0) 30.1(16.1) 0 (0) Fecundity 66.9 (66.1) 92.2 (91.2) 99.3 (98.9) 0.010 (0) 17.0(16.0) 0 (0)
* For scenario V, quasi-extinction probabilities derived from the baseline estimates of frequency (See Table 5.2).
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List of figures
Figure 1.1 Theoretical example of vulnerability to NPDOs of black howler monkey (Alouatta pigra) in the Yucatán Peninsula exposed to cyclones. Vulnerability is assessed for populations inhabiting three contrasting habitat conditions. Overall vulnerability (grey triangle) can be heightened or lessened depending of the interplay between sensitivity [S], non- adaptability [NA] and exposure [E].
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Fig. 3.1 Global pattern of terrestrial mammals with significant exposure to cyclones (panel a) and droughts (panel e) classified by risk status following the IUCN Red List criteria. Mammals with significant exposure to cyclones, shown in panels b, Threatened (Critically Endangered, Endangered, Vulnerable) c, Non-Threatened (Least Concern, Near Threatened), and d, Data Deficient, are those with at least 25% overlap between their ‘extant’ geographic range and areas experiencing a high cyclone frequency in the period 1992-2005. Mammals with significant exposure to droughts are those with at least 25% overlap between the ‘extant’ geographic range (panels, f, = Threatened, g, = Non-Threatened, and h, = Data Deficient) and areas experiencing drought conditions in the period 1980-2011.
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Fig. 3.2 Global pattern of “Threatened” and “Non-Threatened” terrestrial mammals at high exposure to cyclones and droughts. For panel a, Threatened (Critically Endangered, Endangered, Vulnerable) and panel b, Non-Threatened (Least Concern, Near Threatened) mammals, dots represent the centroid area within each species’ “extant” geographic range having a >25% and ≥75% overlap with a high cyclone occurrence, respectively: areas with the lowest frequency of cyclones are indicated in light brown whereas dark blue areas represent locations with the greatest cyclone frequency (period 1992-2005). Dots in panels c, (Threatened) and d, (Non- Threatened) represent the centroid area within each species’ “extant” geographic range with >25% and ≥75% overlap (respectively) with areas affected by drought conditions, (light-brown shaded areas) for the period 1980-2011.
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Exposure (E) Yes ≥ 25% No < 25% Sensitivity (S) Yes > 0 1 No 0 0 0 Non-adaptability (NA) Yes > 0 1 No 0 0 0
Total scores “1” or “2” “0”
Key to scores Vulnerability rank 0 = ≥ 25% exposed but neither sensitive nor non-adaptable Low 1 = ≥ 25% exposed and either sensitive or non-adaptable Moderate 2 = ≥ 25% exposed and both sensitive and non-adaptable High
Case i Case ii Case iii Case iv
Figure 4.1 Criterion for assessing vulnerability to cyclone-driven population declines for non-volant mammals listed in danger of extinction by Mexican law (Nom-059-Ecol-2001). Emphasis is made on whether species are exposed to cyclones or not as determined by the overlap between species’ ranges and cyclones’ tracks (see Table 4.1). Accordingly, a species highly predisposed from intrinsic biology having its extant distribution most overlapped with cyclones’ tracks is expected to be the most vulnerable as in case “iv” (See results in Table 4.2). Species non-exposed (when the overlap of cyclones’ paths with a species extant range is < 25%) but either sensitive and/or non- adaptable are considered low vulnerable as in case “i” (See rationale in Methods, Chapter 4).
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Figure 4.2 Cyclone activity in Mexico for the period 1980-2006. Average speed wind is the average speed equated from the total number of cyclones recorded for every year. Data extracted and processed from: DEWA/GRID-Geneva Project, United Nations Environment Programme (UNEP 2005, Accessed June 2012).
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Figure 4.3 Non-volant mammals categorized in danger of extinction by Mexican law (Nom-059-Ecol-2001) (n = 19) grouped by taxonomic Order assessed for vulnerability (Low, Moderate, High) to cyclone-driven population declines. See in Table 2 the list of indicators and corresponding scores used to quantify the sensitivity, non-adaptability and exposure components used to assess vulnerability.
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Figure 4.4 Spatial pattern of non-volant mammals categorized in danger of extinction by Mexican law (Nom-059-Ecol-2001) assessed for vulnerability to cyclone-driven population declines. Panels represent the merged extant distribution of species determined to be at high (red), moderate (blue) and low (green) vulnerability. In grey scale, areas with the lowest intensity of cyclones are indicated in light grey whereas dark grey areas represent locations with the greatest intensity (period 1980-2006). Number of cyclones overlapping each species extant range as well as percentage of the species’ range impacted is provided in Appendix Table S5.
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Exposure≥ 75%
Territoriality Low vagility Low
specialist Food
Exposure≥ 25% < 75% Habitat specialist Habitat
Figure 4.5a Hierarchical agglomerative cluster analysis of the indicators used to assess vulnerability to cyclone-driven population declines in non-volant Mexican mammals (n = 19) listed in danger of extinction by Mexican law (Nom-059-Ecol- 2001). The clustergram above is used to visualize the pattern of association between indicators. The height of each node represents the distance of the two clusters that the node joins. The vertical axis represents the level of dissimilarity between clusters as equated via Euclidean distances (See details in R documentation, hierarchical clustering, “hclust” function).
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sdidactylus
flavigularis Lepus
Procyonpigmaeus
Cyclope Eira Eira barbara
Alouattapigra
Tapirus bairdii Tapirus
Atelesgeoffroyi vellerosus
Panthera onca
Atelesgeoffroyi yucatanensis
Leoparduswiedii
Ursusamericanus
Castorcanadensis
Erethizon dorsatum
Leoparduspardalis
Cabassouscentralis
Tamanduamexicana
Chironectesminimus Antilocapraamericana Zygogeomystrichopus
Figure 4.5b Hierarchical agglomerative cluster analysis for non-volant mammals listed in danger of extinction by Mexican Law (Nom-059-Ecol-2001). Clustergram displays three major aggregations of species (the number for each species indicates the coding in Table 2) sharing similar components shaping vulnerability to cyclone-driven population declines. Thus, each tip branch represents a species assessed via each of the factors indicative of exposure, sensitivity and non-adaptability. Height of each node and vertical axis as described in Figure 4.5a.
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Figure 5.1 Demographic projection matrix for A. p. mexicana population in Agaltepec Island. The matrix is divided into 5 stage classes based on demographic data for the period 1988-2002. Vital rates are coded as follow: Si infant survival; Sj1 first year juvenile survival; Sj2 second year juvenile survival; SSA sub-adult survival; SA adult survival; Fec fecundity.
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List of Boxes
Box 1.1 Conceptual model for assessing species’ vulnerability to Natural Population Die-Offs (NPDOs). Vulnerability is assessed by evaluating the exposure of a species’ population to a given ENE [E], the species’ intrinsic biological sensitivity to this event [S], and its inability to adapt, or non- adaptability, to the event [NA]. Based on this framework, vulnerability to NPDOs can be low (example II and IV), moderate (example I) or high (example III) depending on the relationship between the level of exposure, sensitivity, and non adaptability (displayed as individual arrows).
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Appendices
Table S1. Data set with the 72 NPDOs (31 species) used in PGLS analysis. Key to column codes is given below (Chapter 1). I II III IV V VI VII VIII IX X XI XII XIII XIV Aepyceros_melampus_a 18.0 a 71.8 1 1 1 1 2 1.011 1.729 0.473 1 1 Aepyceros_melampus_b 18.0 a 35.5 1 1 1 1 2 0.638 1.729 0.473 1 1 Alcelaphus_buselaphus_a 5.0 b 81.0 2 1 2 1 2 1.120 2.209 0.572 1 1 Alcelaphus_buselaphus_b 5.0 b 77.1 3 1 3 1 2 1.072 2.209 0.572 1 1 Antilocapra_americana 20.0 c 62.0 4 2 4 1 1 0.907 1.685 0.957 1 1 Cervus_elaphus 5.0 d 35.4 5 1 5 1 3 0.637 2.384 1.774 1 1 Connochaetes_taurinus_a 6.0 e 32.0 2 1 2 1 2 0.601 2.300 0.301 1 1 Connochaetes_taurinus_b 6.0 e 90.0 1 1 6 1 2 1.249 2.300 0.301 1 1 Connochaetes_taurinus_c 6.0 e 83.0 1 1 1 1 2 1.146 2.300 0.301 1 1 Connochaetes_taurinus_d 6.0 e 92.5 3 1 3 1 2 1.293 2.300 0.301 1 1 Connochaetes_taurinus_e 6.0 e 100.0 6 3 7 1 2 1.571 2.300 0.301 1 1 Equus_burchellii_a 5.0 e 50.0 2 1 2 1 4 0.785 2.447 2.111 1 2 Equus_burchellii_b 5.0 e 86.4 3 1 3 1 4 1.193 2.447 2.111 1 2 Equus_caballus_a 11.0 f 51.0 7 2 8 1 4 0.795 2.607 1.221 1 2 Equus_caballus_b 11.0 f 27.5 8 2 9 1 4 0.552 2.607 1.221 1 2 Equus_caballus_c 11.0 f 50.0 9 2 10 1 4 0.785 2.607 1.221 1 2 Equus_zebra_a 4.5 e 90.0 1 1 6 1 4 1.249 2.452 0.924 1 2 Equus_zebra_b 4.5 e 78.0 1 1 1 1 4 1.083 2.452 0.924 1 2 Giraffa_camelopardalis_a 9.0 e 61.7 3 1 3 1 6 0.903 2.985 1.917 2 2 Giraffa_camelopardalis_b 9.0 e 48.0 6 3 7 1 6 0.765 2.985 1.917 2 2 Hippopotamus_amphibius 0.5 g 53.2 3 1 3 1 7 0.817 3.187 0.623 2 1 Kobus_ellipsiprymnus_a 5.0 h 80.0 1 1 1 1 2 1.107 2.313 0.358 1 1 Kobus_ellipsiprymnus_b 5.0 h 30.0 1 1 11 1 2 0.580 2.313 0.358 1 1 Kobus_kob 5.0 b 40.0 10 1 12 1 2 0.685 1.909 2.002 1 1 Loxodonta_africana_a 1.0 i 19.7 11 1 13 2 5 0.460 3.583 2.770 2 2 Loxodonta_africana_b 1.0 i 87.9 3 1 3 2 5 1.216 3.583 1.750 2 2 Loxodonta_africana_c 1.0 i 17.6 12 4 14 2 5 0.433 3.583 2.770 2 2 Loxodonta_africana_d 1.0 i 22.6 13 1 15 2 5 0.495 3.583 2.770 2 2 Loxodonta_africana_e 1.0 i 7.0 14 1 15 2 5 0.268 3.583 2.770 2 2 Macropus_giganteus_a 34.0 j 55.0 15 1 16 3 8 0.835 1.537 0.149 2 2 Macropus_giganteus_b 34.0 j 67.0 16 1 17 3 8 0.959 1.537 0.149 2 2 Macropus_giganteus_c 34.0 j 44.0 15 1 18 3 8 0.725 1.537 0.149 2 2 Macropus_rufus_a 21.0 j 59.0 15 1 19 3 8 0.876 1.602 0.706 1 2 Macropus_rufus_b 21.0 j 42.7 17 1 20 3 8 0.712 1.602 0.706 1 2 Macropus_rufus_c 21.0 j 30.0 18 1 17 3 8 0.580 1.602 0.706 1 2 Macropus_rufus_d 21.0 j 38.0 15 1 18 3 8 0.664 1.602 0.706 1 2 Nanger_granti 10.0 k 52.0 19 1 3 1 2 0.805 1.752 0.525 2 1 Odocoileus_hemionus_a 15.0 l 42.0 20 2 21 1 3 0.705 1.932 0.479 1 1 Odocoileus_hemionus_b 15.0 l 25.0 20 2 22 1 3 0.524 1.932 0.479 1 1 Ovibos_moschatus 5.0 m 34.6 21 2 23 1 2 0.629 2.496 1.300 1 2 Ovis_aries_a 13.0 n 51.0 22 1 24 1 2 0.795 1.879 1.300 1 1
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Ovis_aries_b 13.0 n 53.1 23 2 25 1 2 0.815 1.879 1.300 1 1 Ovis_aries_c 13.0 n 44.0 23 2 25 1 2 0.725 1.879 1.300 1 1 Ovis_aries_d 13.0 n 43.9 23 2 25 1 2 0.715 1.879 1.300 1 1 Ovis_aries_e 13.0 n 55.7 23 2 25 1 2 0.835 1.879 1.300 1 1 Ovis_canadensis_a 5.0 n 52.0 24 2 26 1 2 0.805 1.879 1.300 1 1 Ovis_canadensis_b 5.0 n 85.0 24 2 27 1 2 1.173 1.879 1.300 1 1 Ovis_canadensis_c 5.0 n 85.0 24 2 28 1 2 1.173 1.879 1.300 1 1 Ovis_canadensis_d 5.0 n 75.0 24 2 29 1 2 1.047 1.879 1.300 1 1 Ovis_canadensis_e 5.0 n 88.0 25 2 30 1 2 1.217 1.879 1.300 1 1 Pelea_capreolus 10.0 o 27.0 26 2 31 1 2 0.546 1.375 0.431 1 1 Phacochoerus_aethiopicus_a 25.0 p 88.0 19 1 32 1 10 1.217 1.884 0.371 1 2 Phacochoerus_aethiopicus_b 25.0 p 92.3 1 1 1 1 10 1.290 1.884 0.371 1 2 Phacochoerus_aethiopicus_c 25.0 p 40.0 1 1 11 1 10 0.685 1.884 0.371 1 2 Phascolarcto_scinereus 9.2 q 63.0 27 1 33 3 9 0.917 0.877 0.004 1 1 Rangifer_tarandus_a 16.0 r 100.0 28 2 34 1 3 1.571 2.042 3.443 1 2 Rangifer_tarandus_b 16.0 r 48.9 21 2 23 1 3 0.774 2.042 3.443 1 2 Rangifer_tarandus_c 16.0 r 77.0 21 2 23 1 3 1.071 2.042 3.443 1 2 Rangifer_tarandus_d 16.0 r 67.0 21 2 35 1 3 0.959 2.042 3.443 1 2 Rangifer_tarandus_e 16.0 r 33.0 21 2 35 1 3 0.612 2.042 3.443 1 2 Rangifer_tarandus_f 16.0 r 78.0 21 2 35 1 3 1.083 2.042 3.443 1 2 Rangifer_tarandus_g 16.0 r 83.0 21 2 35 1 3 1.146 2.042 3.443 1 2 Rangifer_tarandus_h 16.0 r 54.4 29 2 36 1 3 0.829 2.042 3.443 1 2 Redunca_fulvorufula 30.0 s 51.0 26 2 31 1 2 0.795 1.482 3.443 1 1 Redunca_redunca* 30.0 s 90.0 6 3 7 1 2 1.249 1.646 0.029 1 1 Syncerus_caffer_a 3.0 t 86.5 1 1 1 1 2 1.195 2.774 1.811 1 2 Syncerus_caffer_b 3.0 t 32.0 1 1 11 1 2 0.601 2.774 1.811 1 2 Taurotragus_oryx 32.0 u 87.9 3 1 3 1 2 1.216 2.751 2.293 1 2 Tragelaphus_eurycerus 9.0 e 26.9 30 1 37 1 2 0.545 2.435 2.922 2 2 Tragelaphus_imberbis 20.0 v 92.0 3 1 3 1 2 1.284 1.979 0.477 2 1 Tragelaphus_strepsiceros_a 7.0 r 50.3 1 1 1 1 2 0.788 2.316 2.922 2 1 Tragelaphus_strepsiceros_b 7.0 r 40.0 1 1 11 1 2 0.685 2.316 2.922 2 1 *Annual Mortality rate for R. redunca was not available and therefore we used the data from R. fulvorufula
Key to codes used in Table S1 I Species populations used in PGLS analysis II Adult annual mortality rate reported for the species (percentage) III Key to references of adult annual mortality rate for species. See references in the code to column III below IV Reported NPDO severity per species' population assessed (percentage) V Key to reference of NPDO severity for each population. See references in the code to column V below VI Type of extreme natural event. Key: 1= Drought, 2= Cold wave, 3= Flood, 4= Wildfire VII Location where the NPDO was recorded. See corresponding location in the code to column VII below VIII Order. Artiodactyla=1, Proboscidea=2, Diprotodontia=3 I Family. Antilocapridae=1, Bovidae=2, Cervidae=3, Equidae=4, Elephantidae=5, Giraffidae=6 Hippopotamidae=7, Macropodidae=8, Phascolarctidae=9, Suidae=10 Arcsince square root transformation of NPDO Severity. I Log Adult body mass + 1 (Kg) II Log Home range +1 (Km²)
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III Foraging strategy. Key: Mixed feeders & Grazers=1, Browsers=2 IV Territoriality. Key: Present=1, Abscent=2 Code to column III - References of adult annual mortality rate for the species assessed a - Owen-Smith, N., and D.R. Mason. 2005. Journal of Animal Ecology 74:762-773 b - Fryxell, J.M. 1987. Oecologia 72:83-91 c - Grogan, R., and Lindzey, F. 2007. Cooperative Fish and Wildlife Research Unit, University of Wyoming, Larainie. d - Gaillard, J.M., M. Festa-Bianchet, and N.G. Yoccoz.1998. Trends in Ecology & Evolution 13:58-63 e - Owen-Smith, N.O., D.R. Mason, and J.O. Ogutu. 2005. Journal of Animal Ecology 74:774-788 f - Scorolli, A.L., and A.C. Lopez Cazorla. 2010. Wildlife Research 37:207-214 g - Lewison, R. 2007. African Journal of Ecology 45:407-415 h - Melton, D.A. 1987. African Journal of Ecology 25:133-145 i - Foley, Ch.A.H., and L.J. Faust. 2010. Oryx 44:205-212 j - Wilson, G.R. 1975. Australian Wildlife Research 2:1-9 k - Fink, H., and R. Baptist. 1992. World Animal Review 73:2-8 l - Unsworth, J.W., Pac, D.F., White, G.C., and R.M. Bartmann. 1999. The Journal of Wildlife Management 63:315-326 m - Sittler, B. 1988. Polarforschung 58:1-12 n - Gaillard, J.M., M. Festa-Bianchet, and N.G. Yoccoz.1998. Trends in Ecology & Evolution 13:58-63 o - Taylor, W.A., J.D. Skinner, and R.C. Krecek. 2005. South African Journal of Animal Science 35:19-29 p - Rodgers, W.A. 1984. Mammalia 3:329-351 q - Lunney, D., L. O’Neil, A. Matthews, and W.B. Sherwin. 2002. Biological Conservation 106:101-113 r - Gaillard, J.M., M. Festa-Bianchet, and N.G. Yoccoz.1998. Trends in Ecology & Evolution 13:58-63 s - Taylor, W.A., J.D. Skinner, and R.C. Krecek. 2005. South African Journal of Animal Science 35:19-29 t - Jolles, A.E. 2007. African Journal of Ecology 45:398-406 u - Roth, H.H., Osterberg, R. 1971. Rhodesian Journal of Agricultural Research 9, 45-51 v - Besselmann, D., et al. 2008. Journal of Zoo and Wildlife Medicine 39:86-91
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Key to column V - References of NPDO severity for each species' population 1 - Walker, B.H., Emslie, R.H., Owen-Smith, R.N., Scholes, R.J. 1987. Journal of Applied Ecology 24:381-401 2 - Hillman C.J., and A.K.K. Hillman. 1977. African Journal of Ecology 15:1-18 3 - Worden, J., Mose, V., Western, D. 2010. Technical Report. Kenya Wildlife Service. 4 - Martinka, C. 1967. Journal of Wildlife Management 31:159-164 5 - Singer, F.J., A. Harting, K.K. Symonds, and M.B. Coughenour. 1997. The Journal of Wildlife Management 61:12-25 6 - Prins, H.H.T., and I. Douglas-Hamilton. 1990. Oecologia 83:392-400 7 - Garrot, R.A. & Taylor, L. 1990. Journal of Wildlife Management. 55:641-648 8 - Scorolli, A.L., A.C. Lopez Cazorla, and L.A. Tejera. 2006. Journal of Neotropical Mammalogy 13:255-258 9 - Berger, J. 1983. Nature 303:59-61 10 - Fryxell, J.M. 1985 Resource limitation & population ecology of white-eared kob. PhD Thesis. University British Columbia 11 - Foley, CH., Pettorelli, N., Foley, L. 2008. Severe drought and calf survival in elephants. Biology Letters 4:541-544 12 - Woolley, L.A. et al. 2008 . PLoS ONE 3(9): e3233. doi:10.1371/journal.pone.0003233 13 - Conybeare, A., & Haynes, G. 1984. Quaternary Research 22:189-200 14 - Dudley, J.P., Criag, G.C., Gibson, D.St.C., Haynes, G., and Klimowicz, J. 2001. African Journal of Ecology 39:187-194 15 - Caughley, G., Grigg, G.G., and Smith, L. 1985. Journal of Wildlife Management 49:679-685 16 - Robertson, G. 1986. Australian Wildlife Research 13:349-354 17 - Newsome, A.E., D.R. Stephens, and A.K. Shipway. 1967. Wildlife Research 12:1-8 18 - Robertson, G. 1986. Australian Wildlife Research 13:349-354 19 - Ottichilo, W.K., J. De Leeuw, A.K. Skidmore, H.H.T. Prins, and M.Y. Said. 2000. African Journal of Ecology 38:202-216 20 - Robbinette, H.C., C.H. Bear, R.E. Pillmore, and C.E. Knittle. 1952. Journal of Wildlife Management 37:312-326 21 - Miller, F.L., & Barry S.J. 2009. Artic 2:175-189 22 - Garel, M., Loison, A., Gaillard, J.M., Cugnasse, J.M., Maillard, D. 2004. Proc. R. Soc. Biol. 271:471-473 23 - Clutton-Brock, T.H., Price, O.F., Albon, S.D., Jewell, P.A. 1991. Journal of Animal Ecology 60:593-608 24 - Stelfox, J. G. 1971. Bighorn sheep in the Canadian Rockies. A history 1800-1970. Canadian Field Naturalist 85:101-122 25 - Uhazy, L.S., J.C. Holmes, and J.G. Stelfox. 1973. Canadian Journal of Zoology 51:817-824 26 - Taylor, W.A., J.D. Skinner, M.C. Williams, and R.C. Krecek. 2006. Journal of Zoology 268:369-379 27 - Gordon, G., A.S. Brown, and T. Pulsford. 2006. Australian Journal of Ecology 13:451-461 28 - Shaw, G.E., and K.A Neiland. 1973. Journal of Wildlife Diseases 9:311-313 29 - Solberg, E.J., P. Jordhøy, O. Strand, R. Aanes, A. Loison, B.E. Sæther, and J.D.C. Linell. 2001. Ecography 24:441-451 30 - Elkan, P.W., Parnell, R., Smith, J.L.D. 2009. African Journal of Ecology 47:528-536
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Key to column VII - Location where the NPDO was recorded as described in corresponding reference (column V) 1 - Klaserie Private Nature Reserve South Africa 2 - Nairobi National Park Kenya 3 - Amboseli Kenya 4 - Milk river Montana USA 5 - Yellowstone National Park USA 6 - North eastern Tuli region Bostwana 7 - Lake Manyara National Park Tanzania 8 - Montana USA 9 - Parque Provincial Argentina 10 - Buffalo Hills USA 11 - Kruger National Park South Africa 12 - Boma National Park Sudan 13 - Tsavo National Park Tanzania 14 - Pilansberg National Park South Africa 15 - Hwange National Park Zimbabwe 16 - Southern Queensland Australia 17 - Kinchega National Park New South Wales Australia 18 - South Australia 19 - Southern Queensland Australia 20 - Alice Spring Area Northern Territory Australia 21 - Heaston Utah USA 22 - Meadow Utah USA 23 - Western Queen Elizabeth Islands 24 - Southwestern border of the massif central, France 25 - St Kilda Island Scotland 26 - Banff Canada 27 - Kootenay Canada 28 - Provincial lands east of the park 29 - Waterton Canada 30 - British Columbia USA 31 - Sterkfontein Dam Nature Reserve South Africa 32- Masai Mara National Park 33 - Mungalalla Creek South Western Queensland Australia 34 - Mount Motiff Alaska 35 - South Central Queen Elizabeth Islands 36 - Nordenskjold Land Peninsula 37 - Nouabale-Ndoki National Park Congo
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Appendix Table S2. Selection of evolutionary models using the Akaike Information Criterion (AIC) prior PGLS modeling of NPDO severity. Lowest AIC confirmed the most suitable model. PGLS use Brownian model by default but the tree was transformed using the Lambda parameter (Orme et al. 2012) (Chapter 2).
Evolutionary models AIC
Lambda model 24.71
Ornstein-Uhlenbeck model 32.30
Kappa model 42.50
Delta model 4427.10
Brownian motion 13054.83
Early burst model 13057.19
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Appendix Table S3. a,“Threatened” and b, “Non-Threatened” terrestrial mammals with high exposure to cyclones and droughts. Details of data extraction and quantification of exposure are provided in Methods (Chapter 3).
a, “Threatened” terrestrial mammals with high exposure to cyclones (n=100). Mammals also exposed to droughts (n = 36) are starred. Species and subspecies Overlap area (%) Red List status Alouatta pigra 93.61 EN Archboldomys luzonensis 100.00 VU Ateles geoffroyi yucatanensis 96.99 EN Avahi cleesei* 100.00 EN Avahi occidentalis* 100.00 EN Axis calamianensis 100.00 EN Batomys russatus 100.00 EN Brachytarsomys villosa* 100.00 EN Bubalus mindorensis 100.00 CR Canis rufus 100.00 CR Chaetodipus dalquesti 99.62 VU Chiroderma improvisum 100.00 VU Crateromys australis 100.00 CR Crateromys heaneyi 100.00 EN Crateromys schadenbergi 100.00 EN Crocidura orii 100.00 EN Cryptoprocta ferox 97.97 VU Cryptotis phillipsii 100.00 VU Dasyprocta ruatanica 100.00 EN Dasyurus hallucatus 78.88 EN Diplothrix legata 100.00 EN Dipodomys insularis 100.00 CR Dipodomys margaritae 100.00 CR Eidolon dupreanum 98.30 VU Eliurus penicillatus 100.00 EN Eliurus petteri 100.00 VU Eptesicus guadeloupensis 100.00 VU Eptesicus japonensis 100.00 EN Eptesicus malagasyensis* 100.00 EN Eulemur albifrons 100.00 VU Eulemur coronatus 100.00 VU Eulemur macaco flavifrons* 100.00 EN Eulemur macaco macaco 100.00 VU Eulemur mongoz 80.24 VU Eulemur rubriventer 100.00 VU Eulemur sanfordi* 100.00 EN Eumops floridanus 100.00 CR Galidictis grandidieri* 100.00 EN Geocapromys ingrahami 100.00 VU Hapalemur alaotrensis 100.00 CR Hapalemur aureus* 100.00 EN Hapalemur griseus 100.00 VU Hapalemur occidentalis 100.00 VU Hydropotes inermis 78.47 VU Hypogeomys antimena* 100.00 EN Indri indri 100.00 EN Isoodon auratus 99.07 VU Lagorchestes hirsutus 100.00 VU Lagostrophus fasciatus 100.00 EN Lasiurus insularis 100.00 VU
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Lasiurus minor 100.00 VU Lepilemur ankaranensis* 100.00 EN Lepilemur edwardsi 100.00 VU Lepilemur septentrionalis 100.00 CR Lepus hainanus 96.65 VU Limnogale mergulus 100.00 VU Macrotarsomys ingens* 100.00 EN Mazama pandora 100.00 VU Mesocapromys angelcabrerai 100.00 EN Mesocapromys auritus 100.00 EN Mesocapromys nanus 100.00 CR Microcebus berthae* 100.00 EN Microcebus ravelobensis* 100.00 EN Microcebus sambiranensis* 100.00 EN Microcebus tavaratra* 100.00 EN Microgale dryas* 100.00 VU Microgale jenkinsae* 100.00 EN Microgale jobihely* 100.00 EN Microgale monticola* 100.00 VU Microgale nasoloi* 100.00 VU Microtus breweri 100.00 VU Miniopterus fuscus 100.00 EN Mogera etigo 100.00 EN Mormopterus acetabulosus 88.29 VU Mormopterus minutus 100.00 VU Muntiacus crinifrons 87.96 VU Murina ryukyuana 100.00 EN Murina tenebrosa 100.00 CR Myotis dominicensis 100.00 VU Myotis findleyi 100.00 EN Myotis peninsularis 100.00 EN Myotis pruinosus 100.00 EN Myotis yanbarensis 100.00 CR Mysateles gundlachi 100.00 EN Mysateles meridionalis 100.00 CR Natalus primus 100.00 CR Neohylomys hainanensis 100.00 EN Nesomys lambertoni* 100.00 EN Nomascus hainanus 100.00 CR Nyctalus furvus 100.00 VU Pentalagus furnessi 100.00 EN Peromyscus dickey 100.00 CR Peromyscus madrensis 100.00 EN Phaner electromontis* 100.00 VU Phaner parienti* 100.00 VU Phloeomys cumingi 100.00 VU Pipistrellus endoi 100.00 EN Plagiodontia aedium 100.00 EN Podogymnura aureospinula 100.00 EN Podomys floridanus 100.00 VU Procyon pygmaeus 100.00 CR Prolemur simus* 100.00 CR Propithecus candidus* 100.00 CR Propithecus coquereli* 100.00 EN Propithecus coronatus* 100.00 EN Propithecus deckenii* 100.00 VU Propithecus edwardsi* 100.00 EN Propithecus perrieri* 100.00 CR
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Propithecus tattersalli* 100.00 EN Propithecus verreauxi* 91.27 VU Pseudomys fieldi 100.00 VU Pteropus mariannus 100.00 EN Pteropus niger 100.00 EN Pteropus pselaphon 100.00 CR Pteropus rodricensis* 100.00 CR Pteropus rufus 100.00 VU Pteropus yapensis 100.00 VU Reithrodontomys spectabilis 100.00 CR Rhynchomys isarogensis 100.00 VU Salanoia concolor 100.00 VU Solenodon paradoxus 100.00 EN Stenoderma rufum 100.00 VU Sturnira thomasi 100.00 VU Sus cebifrons 89.57 CR Sus oliveri 100.00 EN Sylvilagus graysoni 100.00 EN Tokudaia muenninki 100.00 CR Tokudaia osimensis 100.00 EN Tokudaia tokunoshimensis 100.00 EN Trachypithecus delacouri 81.40 CR Triaenops auritus* 100.00 VU Varecia rubra* 100.00 EN Varecia variegata editorum 100.00 CR Varecia variegata subcincta* 100.00 CR Varecia variegata variegata 100.00 CR Voalavo antsahabensis 100.00 EN a, “Threatened” terrestrial mammals with high exposure to droughts (n=139). Mammals also exposed to cyclones (n = 36) are starred. Species and subspecies Overlap area (%) Red List status Avahi cleesei* 100.00 EN Avahi occidentalis* 100.00 EN Brachytarsomys villosa* 100.00 EN Callicebus oenanthe 99.32 EN Cercopithecus mitis kandti 100.00 EN Cercopithecus mitis schoutedeni 100.00 CR Cercopithecus preussi insularis 100.00 EN Cercopithecus preussi preussi 100.00 EN Colobus angolensis prigoginei 100.00 EN Colobus satanas satanas 99.42 EN Crocidura andamanensis 94.27 CR Crocidura ansellorum 85.35 EN Crocidura jenkinsi 96.30 CR Crocidura lanosa 100.00 EN Crocidura miya 100.00 EN Crocidura picea 100.00 EN Crocidura stenocephala 100.00 EN Crocidura tarella 92.85 EN Crocidura thomensis 98.01 EN Crocidura wimmeri 89.00 CR Cryptotis nelsoni 100.00 CR Dactylopsila tatei 100.00 EN Dendromus kahuziensis 100.00 CR Desmomys yaldeni 100.00 EN Dipodomys stephensi 100.00 EN Dorcopsis atrata 100.00 CR Eptesicus malagasyensis* 100.00 EN
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Eulemur cinereiceps 100.00 EN Eulemur macaco flavifrons* 97.31 EN Eulemur sanfordi* 95.62 EN Euoticus pallidus pallidus 95.21 EN Feroculus feroculus 85.74 EN Galidictis grandidieri* 100.00 EN Giraffa camelopardalis peralta 89.34 EN Gorilla beringei beringei 100.00 CR Gorilla gorilla diehli 100.00 CR Hapalemur aureus* 100.00 EN Hybomys badius 100.00 EN Hybomys basilii 100.00 EN Hylomyscus baeri 89.09 EN Hylomyscus grandis 100.00 CR Hypogeomys antimena* 100.00 EN Kobus leche anselli 78.66 CR Lamottemys okuensis 100.00 EN Lepilemur ankaranensis* 100.00 EN Liomys spectabilis 76.59 EN Lophuromys dieterleni 100.00 EN Lophuromys eisentrauti 100.00 EN Lophuromys rahmi 100.00 EN Loris lydekkerianus grandis 100.00 EN Loris lydekkerianus nordicus 99.81 EN Loris tardigradus nycticeboides 100.00 EN Macaca sinica opisthomelas 100.00 EN Macaca sinica sinica 97.96 EN Macaca sylvanus 81.14 EN Macrotarsomys ingens* 100.00 EN Mandrillus leucophaeus leucophaeus 89.60 EN Mandrillus leucophaeus poensis 99.18 EN Microcebus berthae* 100.00 EN Microcebus ravelobensis* 100.00 EN Microcebus sambiranensis* 97.71 EN Microcebus tavaratra* 100.00 EN Microgale dryas* 82.84 VU Microgale jenkinsae* 99.97 EN Microgale jobihely* 100.00 EN Microgale monticola* 100.00 VU Microgale nasoloi* 100.00 VU Mus fernandoni 88.53 EN Mus mayori 81.21 VU Myonycteris brachycephala 99.74 EN Myosorex eisentrauti 100.00 CR Myosorex okuensis 100.00 EN Myosorex rumpii 100.00 EN Neonycteris pusilla 100.00 VU Neotoma angustapalata 96.30 EN Nesomys lambertoni* 100.00 EN Nomascus concolor lu 100.00 CR Notiosorex villai 100.00 VU Notopteris macdonaldi 93.34 VU Nyctalus azoreum 94.38 EN Oreonax flavicauda 77.58 CR Oreotragus oreotragus porteousi 77.65 EN Otomys barbouri 100.00 EN Otomys burtoni 100.00 EN Otomys occidentalis 95.80 VU
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Pan troglodytes verus 77.32 EN Panthera pardus kotiya 96.73 EN Pappogeomys alcorni 100.00 CR Paradoxurus zeylonensis 96.10 VU Pearsonomys annectens 85.19 VU Peromyscus ochraventer 80.67 EN Phaner electromontis* 97.88 VU Phaner parienti* 98.89 VU Pipistrellus maderensis 75.20 EN Plecotus teneriffae 93.77 EN Pogonomys fergussoniensis 93.47 EN Potorous longipes 82.19 EN Praomys degraaffi 100.00 VU Praomys hartwigi 100.00 EN Praomys morio 100.00 EN Praomys obscurus 100.00 EN Procolobus badius temminckii 88.59 EN Procolobus pennantii bouvieri 100.00 CR Procolobus pennantii pennantii 100.00 EN Procolobus preussi 100.00 CR Procolobus rufomitratus tephrosceles 78.36 EN Prolemur simus* 99.20 CR Propithecus candidus* 100.00 CR Propithecus coquereli* 100.00 EN Propithecus coronatus* 100.00 EN Propithecus deckenii* 100.00 VU Propithecus edwardsi* 78.66 EN Propithecus perrieri* 100.00 CR Propithecus tattersalli* 100.00 EN Propithecus verreauxi* 100.00 VU Pseudalopex fulvipes 97.84 CR Pseudochirops coronatus 97.26 VU Pseudochirulus schlegeli 97.13 VU Pteralopex anceps 100.00 EN Pteralopex atrata 100.00 EN Pteralopex flanneryi 100.00 CR Pteralopex taki 100.00 EN Pteropus aldabrensis 100.00 VU Pteropus anetianus 98.57 VU Pteropus cognatus 100.00 EN Pteropus fundatus 100.00 EN Pteropus livingstonii 100.00 EN Pteropus mahaganus 100.00 VU Pteropus nitendiensis 100.00 EN Pteropus rennelli 100.00 VU Pteropus rodricensis* 100.00 CR Pteropus ualanus 100.00 VU Pteropus woodfordi 100.00 VU Punomys kofordi 85.85 VU Rattus montanus 100.00 EN Rattus stoicus 93.73 VU Redunca fulvorufula adamauae 76.61 EN Reithrodontomys hirsutus 100.00 VU Rhinolophus cognatus 86.19 EN Rhinolophus guineensis 86.90 VU Rhinolophus hilli 100.00 CR Rhinolophus hillorum 88.34 VU Rhinolophus maclaudi 95.44 EN
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Rhinolophus ziama 100.00 EN Rhinopithecus brelichi 100.00 EN Ruwenzorisorex suncoides 78.44 VU Sciurocheirus alleni alleni 95.51 EN Semnopithecus priam thersites 78.60 EN Solisorex pearsoni 100.00 EN Solomys ponceleti 94.49 CR Solomys salebrosus 94.49 EN Solomys sapientis 91.68 EN Sorex milleri 86.11 VU Spilocuscus papuensis 94.05 VU Srilankamys ohiensis 100.00 VU Suncus fellowesgordoni 100.00 EN Suncus zeylanicus 95.73 EN Sylvisorex camerunensis 94.34 VU Sylvisorex isabellae 100.00 EN Sylvisorex lunaris 88.44 VU Sylvisorex morio 100.00 EN Tadarida bregullae 91.42 EN Tadarida tomensis 97.38 EN Thamnomys kempi 98.53 VU Thomasomys apeco 100.00 VU Thomasomys macrotis 100.00 VU Trachypithecus vetulus monticola 100.00 EN Trachypithecus vetulus philbricki 99.94 EN Tragelaphus derbianus derbianus 86.67 CR Triaenops auritus* 99.13 VU Urocyon littoralis 93.21 CR Uromys rex 96.39 EN Vandeleuria nolthenii 100.00 EN Varecia rubra* 98.54 EN Varecia variegata subcincta* 99.88 CR b, “Non-Threatened” terrestrial mammals with high exposure to cyclones (n=135) Mammals also exposed to droughts are starred (n = 30). Species and subspecies Overlap area (%) Red List status Anourosorex yamashinai 100.00 LC Apodemus argenteus 100.00 LC Apodemus semotus 100.00 LC Apodemus speciosus 100.00 LC Apomys datae 100.00 LC Apomys microdon 100.00 LC Apomys musculus 100.00 LC Archboldomys kalinga 100.00 LC Archboldomys musseri 100.00 LC Ardops nichollsi 93.30 LC Arielulus torquatus 100.00 LC Blarina carolinensis 78.47 LC Brachyphylla cavernarum 97.89 LC Brachyphylla nana 100.00 LC Brachytarsomys albicauda 100.00 LC Brachyuromys betsileoensis 100.00 LC Brachyuromys ramirohitra 100.00 LC Bullimus luzonicus 100.00 LC Capricornis crispus 100.00 LC Capricornis swinhoei 100.00 LC Capromys pilorides 100.00 LC Carpomys phaeurus 100.00 LC Cervus Nippon 95.92 LC
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Cheirogaleus major 99.05 LC Cheirogaleus medius* 95.20 LC Chimarrogale platycephalus 100.00 LC Chrotomys silaceus 100.00 LC Chrotomys whiteheadi 100.00 LC Crocidura dsinezumi 100.00 LC Crocidura grayi 100.00 LC Crocidura tanakae 100.00 LC Crocidura watasei 100.00 LC Cryptotis mayensis 99.98 LC Dasykaluta rosamondae 83.43 LC Dymecodon pilirostris 100.00 LC Echinops telfairi* 93.56 LC Eliurus grandidieri 100.00 LC Eliurus majori 99.20 LC Eliurus minor 98.50 LC Eliurus myoxinus* 96.94 LC Eliurus tanala 98.30 LC Eliurus webbi 98.58 LC Emballonura atrata 97.42 LC Emballonura tiavato* 100.00 LC Episoriculus fumidus 100.00 LC Eptesicus matroka 100.00 LC Erophylla bombifrons 100.00 LC Erophylla sezekorni 100.00 LC Euroscaptor mizura 100.00 LC Galidia elegans 97.73 LC Geogale aurita* 88.96 LC Geomys pinetis 99.41 LC Glirulus japonicus 100.00 LC Gymnuromys roberti 98.85 LC Hemicentetes nigriceps 100.00 LC Hemicentetes semispinosus 100.00 LC Herpestes urva 98.42 LC Heteromys gaumeri 99.07 LC Hipposideros obscures 80.67 LC Hipposideros pygmaeus 79.91 LC Lasiurus intermedius 79.60 LC Lepus brachyurus 100.00 LC Lepus coreanus 78.07 LC Lepus sinensis 76.78 LC Lophostoma evotis 78.37 LC Macaca cyclopis 100.00 LC Macaca fuscata 100.00 LC Macrotarsomys bastardi* 94.34 LC Martes melampus 100.00 LC Meles anakuma 100.00 LC Mesembriomys macrurus 100.00 LC Microcebus griseorufus* 81.31 LC Microcebus murinus* 96.71 LC Microcebus rufus 97.99 LC Microgale brevicaudata* 100.00 LC Microgale cowani 99.17 LC Microgale dobsoni 98.76 LC Microgale drouhardi 99.38 LC Microgale fotsifotsy 99.36 LC Microgale gracilis 99.17 LC Microgale gymnorhyncha 99.14 LC
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Microgale longicaudata* 100.00 LC Microgale majori 97.97 LC Microgale parvula 99.17 LC Microgale principula 97.54 LC Microgale pusilla 96.41 LC Microgale soricoides 97.84 LC Microgale taiva* 96.22 LC Microgale talazaci 99.16 LC Microgale thomasi 98.15 LC Microtus montebelli 100.00 LC Miniopterus gleni 100.00 LC Miniopterus majori* 99.00 LC Miniopterus manavi* 99.26 LC Miniopterus sororculus* 97.70 LC Mogera imaizumii 100.00 LC Mogera wogura 100.00 LC Monophyllus plethodon 94.12 LC Monophyllus redmani 100.00 LC Monticolomys koopmani* 99.32 LC Mormoops blainvillei 100.00 LC Mormopterus jugularis* 96.23 LC Murina ussuriensis 79.46 LC Mustela itatsi 100.00 LC Myodes andersoni 100.00 LC Myodes regulus 82.94 LC Myodes rex 100.00 LC Myodes smithii 100.00 LC Myotis goudoti* 99.30 LC Myotis macrodactylus 86.27 LC Myzopoda aurita 98.68 LC Myzopoda schliemanni* 100.00 LC Natalus stramineus 93.07 LC Neofiber alleni 100.00 LC Nesomys audeberti 98.00 LC Nesomys rufus 98.29 LC Ningaui timealeyi 97.43 LC Niviventer coninga 100.00 LC Nyctiellus Lepidus 100.00 LC Oryzorictes hova 98.22 LC Otomops madagascariensis* 100.00 LC Otonyctomys hatti 100.00 LC Otopteropus cartilagonodus 100.00 LC Peromyscus eva 84.67 LC Peromyscus polionotus 98.26 LC Peromyscus yucatanicus 100.00 LC Petaurista leucogenys 100.00 LC Petrogale rothschildi 100.00 LC Phaner furcifer 100.00 LC Phaner pallescens* 100.00 LC Phloeomys pallidus 100.00 LC Phyllonycteris poeyi 100.00 LC Phyllops falcatus 100.00 LC Pipistrellus westralis 87.23 LC Plecotus sacrimontis 100.00 LC Pseudantechinus roryi 77.49 LC Pseudantechinus woolleyae 86.20 LC Pseudomys chapmani 95.31 LC Pteromys momonga 100.00 LC
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Pteropus leucopterus 100.00 LC Rhinolophus inops 90.43 LC Rhogeessa aeneus 99.92 LC Sciurus aureogaster 100.00 LC Sciurus lis 100.00 LC Sciurus yucatanensis 95.86 LC Scotophilus marovaza* 100.00 LC Scotophilus robustus 99.48 LC Setifer setosus* 98.48 LC Sminthopsis longicaudata 75.59 LC Sorex hosonoi 100.00 LC Sorex maritimensis 93.37 LC Sorex shinto* 100.00 LC Suncus madagascariensis* 99.11 LC Sundasciurus hoogstraali 100.00 LC Sundasciurus samarensis 100.00 LC Sylvilagus palustris 99.61 LC Tadarida jobimena* 100.00 LC Tadarida leucostigma* 99.71 LC Tenrec ecaudatus* 98.48 LC Triaenops furculus* 99.87 LC Triaenops rufus* 98.86 LC Urotrichus talpoides 100.00 LC Vespadelus douglasorum 95.65 LC Voalavo gymnocaudus* 100.00 LC Zyzomys woodwardi 100.00 LC b, “Non-Threatened” terrestrial mammals with high exposure to droughts (n=48). Mammals also exposed to cyclones are starred (n=30). Species and subspecies Overlap area (%) Red List status Aethomys nyikae 77.44 LC Arvicanthis ansorgei 88.71 LC Calcochloris obtusirostris 76.13 LC Cercocebus atys atys 84.96 NT Cercopithecus campbelli campbelli 92.92 LC Cercopithecus mitis doggetti 85.24 LC Cercopithecus mitis erythrarchus 80.32 LC Cercopithecus mitis moloneyi 80.95 LC Cercopithecus mitis opisthostictus 83.43 LC Cercopithecus petaurista buettikoferi 91.74 LC Cheirogaleus medius* 99.52 LC Crocidura niobe 85.48 NT Crocidura somalica 79.71 LC Cryptomys darlingi 83.36 LC Damaliscus lunatus superstes 100.00 LC Damaliscus lunatus tiang 78.65 LC Dendromus insignis 87.27 LC Dobsonia inermis 92.29 LC Echinops telfairi* 99.48 LC Eliurus myoxinus* 98.97 LC Emballonura tiavato* 99.07 LC Eulemur rufifrons 97.65 NT Genetta bourloni 83.29 NT Geogale aurita* 99.98 LC Gerbillus maghrebi 99.63 LC Gerbillus perpallidus 88.51 LC Gerbillus pulvinatus 88.35 LC Grammomys dryas 76.89 NT Hipposideros commersoni 77.10 NT
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Hipposideros thomensis 97.99 LC Hybomys planifrons 83.44 LC Kobus vardonii 82.78 NT Lemniscomys linulus 88.87 LC Lemur catta 99.53 NT Lophuromys roseveari 100.00 LC Lophuromys woosnami 76.68 LC Macrotarsomys bastardi* 99.46 LC Melonycteris fardoulisi 92.33 LC Mico saterei 99.47 LC Microcebus griseorufus* 98.81 LC Microcebus murinus* 99.64 LC Microgale brevicaudata* 99.21 LC Microgale longicaudata* 85.23 LC Microgale taiva* 83.90 LC Microtus abbreviatus 81.45 LC Millardia kathleenae 95.41 LC Miniopterus majori* 81.79 LC Miniopterus manavi* 77.56 LC Miniopterus sororculus* 81.00 LC Mirza coquereli 99.46 NT Monticolomys koopmani* 83.11 LC Mormopterus jugularis* 76.35 LC Moschiola meminna 91.47 LC Mus callewaerti 100.00 LC Mustela subpalmata 95.31 LC Myosorex babaulti 95.33 NT Myotis goudoti* 76.48 LC Myzopoda schliemanni* 99.20 LC Otomops madagascariensis* 100.00 LC Paracrocidura maxima 93.54 NT Phaner pallescens* 99.58 LC Pteropus rayneri 100.00 NT Pteropus samoensis 79.00 NT Pteropus tonganus 75.00 LC Rhinolophus rex 95.39 LC Sciurus alleni 85.35 LC Scotophilus marovaza* 99.45 LC Setifer setosus* 77.10 LC Sorex shinto* 100.00 LC Suncus madagascariensis* 82.93 LC Sylvisorex vulcanorum 86.02 NT Tadarida jobimena* 98.82 LC Tadarida leucostigma* 84.56 LC Tadarida solomonis 89.30 LC Tenrec ecaudatus* 77.10 LC Triaenops furculus* 99.14 LC Triaenops rufus* 97.05 LC Voalavo gymnocaudus* 100.00 LC
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Appendix Table S4. Terrestrial mammals categorized at threat of a, storm/floods (n = 69) and b, droughts (n = 81) on the IUCN Threats Classification Scheme Version 3.0. (Data accessed July 2012). Mammals reported for both sub-categories (n = 9) are starred. These threats could be in the past, ongoing or in the future, using a time frame of three generations or ten years, whichever is the longer (not exceeding 100 years in the future) as required by the Red List Criteria.(Chapter 3).
a, species and subspecies b, species and subspecies Alouatta pigra Addax nasomaculatus Ardops nichollsi Alcelaphus buselaphus Ariteus flavescens Allocricetulus curtatus Axis porcinus Ammodorcas clarkei Bison bison Ammotragus lervia Biswamoyopterus biswasi Arabitragus jayakari Blastocerus dichotomus* Arvicola sapidus Caprolagus hispidus Beatragus hunteri Cardiocranius paradoxus* Bettongia lesueur Chilonatalus tumidifrons Blastocerus dichotomus* Chiroderma improvisum Brachylagus idahoensis Crocidura andamanensis Capra nubiana Crocidura hispida Cardiocranius paradoxus* Crocidura jenkinsi Cricetulus sokolovi Crocidura nicobarica Crocidura tarfayensis Dama mesopotamica* Cynomys parvidens Desmana moschata* Dama dama Emballonura semicaudata Dama mesopotamica* Enhydra lutris Damaliscus lunatus Eptesicus guadeloupensis Desmana moschata* Equus ferus* Dorcatragus megalotis Equus ferus przewalskii* Ellobius fuscocapillus Eumops floridanus Ellobius tancrei Geocapromys ingrahami Eptesicus gobiensis Geocapromys thoracatus Equus africanus Heteromys nelsoni Equus ferus* Hipposideros demissus Equus ferus przewalskii* Hydropotes inermis Equus hemionus Lama guanicoe* Eudorcas rufifrons Lasiurus degelidus Felis margarita Lasiurus minor Galemys pyrenaicus Lasiurus pfeifferi Gazella dorcas Lepus flavigularis Gazella spekei Melomys rubicola Geomys arenarius Meriones hurrianae* Hippocamelus antisensis Myotis dominicensis Hippopotamus amphibius Myotis martiniquensis Kobus leche Natalus stramineus Lagostrophus fasciatus Neophocaena phocaenoides Lama guanicoe* Ornithorhyncus anatinus Lasiorhinus krefftii Procyon pygmaeus Lasiorhinus latifrons Pteropus aldabrensis Leopardus geoffroyi Pteropus anetianus Leporillus conditor Pteropus howensis Lepus flavigularis Pteropus livingstonii Lepus nigricollis Pteropus mariannus Loxodonta africana Pteropus melanotus Lynx pardinus Pteropus niger Mastacomys fuscus Pteropus nitendiensis Mazama gouazoubira Pteropus pelewensis Mephitis mephitis
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Pteropus rayneri Meriones hurrianae* Pteropus renneli Microtus fortis Pteropus rodricensis* Microtus paradoxus Pteropus rufus Neophocaena asiaeorientalis Pteropus samoensis Notoryctes caurinus Pteropus tonganus Notoryctes typhiops Pteropus ualanus Odocoileus virginianus Pteropus yapensis Omithorhynchus anatinus Rattus burrus Onychogalea fraenata Reithrodontomys raviventris Oryx leucoryx Reithrodontomys spectabilis Otocolobus manui Rhinoceros unicornis Otocyon megalotis Rhogeessa genowaysi Ovis ammon Sorex maritimensis Ovis orientalis Stenoderma rufum Perameles bougainville Sturnira thomasi Peronyscus mayensis Sylvilagus aquaticus Plecotus austriacus Sylvilagus bachmani Procapra gutturosa Zaedyus pichiy Pteropus rodricensis* Redunca redunca Saiga tatarica Salpingotus crassicauda Sorex roboratus Spalax uralensis Spermophilus mohavensis Spermphilus erythrogenys Stylodipus sungorus Sylvilagus robustus Syncerus caffer Vicugna vicugna Zyzomys pedunculatus
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Appendix Table S5. Non-volant terrestrial mammals (n = 19) listed in danger of extinction by Mexican laws assessed for cyclone-driven population declines. Criterion for assessing vulnerability in Figure 4.1 and indicators as in Table 4.1 Species with an overlap < 25% of their extant geographic range with areas that have been exposed to < 2 cyclones (starred) were excluded from the assessment (Chapter 4).
Exposure Exposure Species’ extant Species’ Cyclones Species & Subspecies range in extant range Overlapping ≥25%<75%overlap ≥75% overlap Mexico Km² impacted % Species’ ranges Cyclopes didactylus 1 0 107832.22 50.70 9 Ateles geoffroyi yucatanensis 1 1 154652.52 98.85 23 Ateles geoffroyi vellerosus 1 0 263089.23 56.69 24 Procyon pygmaeus 1 1 492.99 100.00 9 Erethizon dorsatum 1 0 423697.79 30.34 11 Alouatta pigra 1 1 162662.64 95.10 23 Tamandua mexicana 1 0 532130.46 73.02 41 Eira barbara 1 1 847087.88 76.73 49 Lepus flavigularis 1 0 102.88 47.91 2 Zygogeomys trichopus 1 0 653.85 68.00 2 Antilocapra americana 1 0 116453.73 46.87 9 Castor canadensis 1 0 305856.95 51.81 12 Ursus americanus 1 0 118247.55 58.35 13 Tapirus bairdii 1 0 72586.17 66.98 22 Leopardus pardalis 1 1 336418.91 86.19 36 Leopardus wiedii 1 1 732101.48 77.93 52 Cabassous centralis 1 1 7465.70 96.60 2 Panthera onca 1 0 295212.54 74.11 55 Chironectes minimus 1 0 212966.78 74.29 21 Romerolagus diazi * 0 0 482.03 1.69 1 Microtus californicus * 0 0 25999.31 2.08 1 Alouatta palliata Mexicana* 0 0 38432.26 19.92 3 Syvilagus insonus * 1 1 1236.03 100.00 1
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Appendix Figure S1 Spatial correlogram Moran’s I plotted against distance classes (spatial lags) in the residuals. Horizontal line in the graph indicates the expectation of the statistic under null hypohtesis that no significant spatial autocorrelation is present. Moran’s I was computed using a permutation-based test using the moran.cp function running 99 permutations at 5% significance level in the “spdep” package in R. Values range from -1 (indicating perfect dispersion) to +1 (perfect correlation). We did not found spatial autocorrelation in the residuals (Moran’s I = -0.74) (Chapter 2).
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Appendix Figure S2. Moran scatter plot for evaluation of spatial autocorrelation in the residuals. Model residuals explicitly reveals lack of systematic patterns of spatial autocorrelation. Plot was built using the moran.plot function in the “spdep” package in R
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Appendix Figure S3. Quasi-extinction probabilities for A. p. mexicana population in Agaltepec Island. Panels show the effect of cyclones on vital rates (survival rates and fecundity rates) and carrying capacity for different scenarios. For all the panels, continuous blue curve is the cumulative probability distribution, and it shows the probability of falling below, at or before a specific year with a 95% confidence interval while bars in the same x axes indicate such probability for a specific year. Vertical line indicates the median time to reach quasi-extinction (< 5 individuals). Details of modelling in Methods (Chapter 5).
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j) Humans impacts only (no effect of cyclones)
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