the vulnerability of california amphibian species to

The Vulnerability of California Amphibian Species to Climate Change

A Thesis

Presented to

The Faculty of the Department of the Environmental Program

Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Rayna Ben-Zeev

May/2015

______________________

Miroslav Kummel

______________________

David Brown

2

Table of Contents

ABSTRACT .......................................................................................................................... 3

AMPHIBIANS AND CAUSES OF RECENT DECLINES ............................................................ 3

MODELING AND CLIMATE SCENARIOS ............................................................................. 8

MODEL METHODS ........................................................................................................... 11

RESULTS AND DISCUSSION .............................................................................................. 15

BD ..................................................................................................................................... 20

ASSUMPTIONS AND SOURCES OF ERROR ................... ERROR! BOOKMARK NOT DEFINED.1

FUTURE RESEARCH .................................................... ERROR! BOOKMARK NOT DEFINED.2

FIGURES ...................................................................... ERROR! BOOKMARK NOT DEFINED.4

WORKS CITED ............................................................ ERROR! BOOKMARK NOT DEFINED.8

APPENDICES ..................................................................................................................... 30

3

Abstract

Amphibians are declining on a global scale, faster than any other taxonomic

group. Although I am still unsure of the causes of many local declines, I have evidence

that on a larger scale temperature and precipitation changes caused by climate change

directly relate to broader amphibian disappearances. I aim to use past temperature and

precipitation trends and amphibian distributions in California to predict future amphibian

distributions. I use the Biomod2 package in R, along with CMIP5 climate layers and

emissions scenarios to correlate amphibian distributions to climate, and predict a range of

future possibilities for amphibians under different carbon emission scenarios. I hope to

identify which species are most in danger of extirpation to hopefully direct future

management initiatives to these species.

Amphibians and Causes of Recent Declines

Amphibian populations are decreasing throughout the world. Thousands of

species have declined, hundreds are on the brink of extinction, and some have already

vanished (Pounds et al. 2006). The 2004 global amphibian assessment found 32.5% of

amphibian species as globally threatened, and 43% experiencing population declines

(Rohr and Raffel 2010). Over 427 species are listed as “critically endangered” including

122 species that are already “possibly extinct” (Pounds et al. 2006). While there are

documented declines of amphibians worldwide, my analysis in this paper will be

restricted to understanding the dynamics of California amphibian populations.

Concern about amphibians is in large part due to their value as indicators of

environmental stress. However, amphibians are also important components of local

4

ecosystems and communities, and a worldwide decline could have devastating impacts on

other organisms (Blaustein and Kiesecker 2002). For example, in the Sierra Nevada

mountain area, loss of R. muscosa from high-elevation lakes and streams has been

correlated with significantly lower numbers of one of the frog’s predators, the garter

snake. However, populations of R. muscosa have also disappeared from over 75% of

study sites they formerly inhabited in the mountains of California. Amphibian declines of

all but two species in this region are occurring on a broad scale with loss of historic

populations but little or no colonization of new sites (Drost et al. 1996).

As a taxonomic group, amphibians are long-term survivors that have persisted

through the last four mass extinctions—over 360 million years. However, they are

currently at a greater risk of extinction than any other taxon worldwide (Wake and

Vredenburg 2008). This partially stems from their physiology. Amphibians have moist,

permeable, well-vascularized skin and unshelled eggs that are directly exposed to soil,

water, and sunlight. They are dependent on many types of food (because frog larvae are

herbivorous while adults are carnivorous) and they are also used as a food item by many

predators. Within a lifetime, a frog integrates toxins from both terrestrial and aquatic

plant and animal life. Being exposed to this vast range of environmental conditions and

being adapted to relatively narrow and stable temperature ranges causes amphibians to be

more vulnerable to stressors. These include heat, changes in moisture, changes in oxygen

concentration in water, and susceptibility to parasites (Rohr and Raffel 2010, Blaustein

and Kiesecker 2002).

Many amphibians have small geographic ranges, especially the terrestrial species.

This makes them especially vulnerable to habitat destruction or climate change, causing

5

large declines in species with restricted habitats or in widely distributed species at the

edges of their ranges (Wake and Vredenburg 2008). In the broader Sierra Nevada

mountain area, some species have already disappeared from the area entirely and other

formerly abundant species have dwindled to few small remnant populations. These

declines have occurred in a relatively undisturbed, pristine sites lacking obvious adverse

effects of human activities (Drost et al. 1996, Whitfield et al. 2007).

The causes of these declines still remain somewhat unclear, partially because they

are so complex. The situation is further complicated because susceptibility to stressors

depends on many factors such as life stage, species, population, geography, weather

parameters, water chemistry, and history of experiencing particular stressors. Thus,

different populations could react differently to the same combination of stressors

(Blaustein and Kiesecker 2002). The lack of long-term data on amphibian populations

severely limits our understanding of the distribution of amphibian declines and the causes

of these declines (Whitfield et al. 2007).

Possible causes of declines include habitat destruction and alteration, acid

precipitation, chemical pollution, introduced fish, drought, diseases, changes in the

climate regime, and interactions among these single factors (Blaustein and Kiesecker

2002, Drost et al. 1996). Different factors play different roles in varying study regions.

For example, habitat destruction and alteration is unlikely as a cause in most California

sites because most habitat has been protected from habitat loss or evident habitat

degradation, with especially little degradation of the streams, ponds lakes and wet

meadows that provide breeding habitat for amphibians (Drost et al. 1996). Additionally,

we can disregard acid rain as a primary cause because field studies of lakes throughout

6

the Sierra Nevada have found no significant relationship between pH, water chemistry,

and amphibian distributions (Bradford et al. 1992).

Introduced predatory fish have influenced many amphibians in the Sierra Nevada

region, with frogs being essentially absent from some ponds containing introduced fish.

However, focusing on these unique location-specific causes does not adequately explain

the overall decline of frog species in the broader region. For example, most frog species

are capable of surviving and reproducing in waters containing fish as long as there is

emergent vegetation or other escape cover. Toads are also much less susceptible to the

effects of introduced fish because they breed in temporary bodies of water and secrete

toxins from their skin that cause fish to avoid them (Drost et al. 1996). Drought is also

unlikely to be a primary cause of amphibian declines. Even a prolonged drought should

not lead to widespread disappearance of species in an area such as California, where

extended droughts are a regular occurrence. Chemical contaminants from insecticides

remain a potential factor in the declines, but again, there is little available evidence that

these contaminants explain the overall decline of frog species in the region (Drost et al.

1996).

Hence, we are still unsure about the causes of the local amphibian declines in

California, the extent to which the declines are related, and the relationship between these

losses and the ones in other areas of the world (Drost et al. 1996). We do, however, know

that global environmental changes such as variations in temperature, precipitation, UV-B

radiation, and global spread of contaminants and diseases can affect amphibians on a

local scale (Blaustein and Kiesecker 2002). In fact, researchers determine with very high

confidence (>99%) that large-scale warming is a key factor in amphibian disappearances

7

(Pounds et al. 2006). The IPCC’s predictions of “amphibian extinctions increasing on

mountains” is now an empirical fact (Wake and Vredenburg 2008).

Many of the worldwide amphibian declines have been caused by possibly the

most deadly invasive species on the planet, the pathogenic chytrid fungus,

Batrachochytrium dendrobatidis (Bd) (Rohr et al. 2008). Bd is found only on amphibians,

lives on keratin, and is present in tadpoles on the external mouth and in adults on the

outer layer of the skin. Despite its aquatic life cycle, Bd has been found on fully

terrestrial species of amphibians that never enter water. As an invasive species, it now

appears to be globally distributed and is associated with serious declines and extinctions

of >200 species of amphibians (Wake and Vredenburg 2008).

In the Sierra Nevada region, the fungus has already had a devastating impact on

native species (Wake and Vredenburg 2008). Although Bd has tended to thrive in cooler

temperatures (unlike many other diseases), it is still likely that large pathogen outbreaks

will occur with climate change. It is possible that the outbreaks will be exacerbated

because increasing temperatures at many highland locations will shift originally colder

climates towards the growth optimum of Bd (Wake and Vredenburg 2008, Pounds et al.

2006). Temperature variability and changes in relative humidity caused by climate

change could also diminish the defenses of amphibian hosts, making them more

susceptible to the disease (Rohr and Raffel 2010).

Nonetheless, the role of climate change in the emergence of Bd is still

controversial, outside of a small number of cases such as in the harlequin frog and the

golden toad. For example, in Costa Rica 67% of the 110 species of Atelopus have

vanished because of Bd and climate warming (Pounds et al. 2006). In my study, I test a

8

hypothesis that unusually warm years will lead to declines of amphibians, however, I do

not assume that disease plays an important role. Since large-scale warming is a main

factor in amphibian extinctions, I will use projected climate changes to predict future

amphibian distributions. I construct a model that predicts changes in amphibian

populations based on the responses of populations to temperature and precipitation. In

order to forecast future distributions, I use four climate scenarios part of the IPCC 5th

assessment.

Modeling and Climate Scenarios

In the model, constructed using the Biomod2 package in R, I used past

temperature and precipitation trends as a means to predict future habitat alterations and

subsequent amphibian extinctions. I identified the six California frog and toad families,

Ranidae, Hylidae, Bufonidae, Ascaphidae, Scaphiopodidae, and Pipidae to determine

which is most at risk from climatic changes under a variety of different climate scenarios.

Hopefully this will allow policymakers to evaluate and implement management strategies

tailored to the threatened species.

Few studies have examined historical declines, with only a handful of analyses

conducted in the western United States. The same is true for introduced species. Many

regions lack the necessary museum records to document historical shifts. Fortunately, in

California there are enough reasonable, long-term collections to assess distributional

shifts in communities over a range of habitat types (Fisher and Shaffer 1996).

In order to incorporate predicted future changes in climate, I use simulations part

of phase 5 of the Coupled Model Intercomparison Project (CMIP5). CMIP5 provides a

9

common framework for comparing and assessing Earth system processes in the context

of climate simulations (Booth et al. 2013). These models are species distribution models

(SDMs), meaning they combine observations of species occurrences with environmental

estimates to predict species distributions (Elith and Leathwick 2009). SDMs are currently

one of the most commonly used techniques to predict the sites where we can expect

species to occur because of suitable environmental conditions for species persistence

(Hijmans 2012).

I am considering climate uncertainties in this model to attempt to predict a range

of possibilities for future amphibian populations. Much of the existing information on

climate uncertainties is based on general circulation models (GCMs), driven by future

changes in atmospheric greenhouse gas concentrations. However, GCMs ignore

uncertainties in carbon cycle processes, which relate emissions to global concentrations

(Booth et al. 2013). Instead, I run scenarios from Earth System Models (ESMs), which

incorporate processes controlling the exchange of carbon around the climate system.

ESMs use both terrestrial and ocean carbon cycle processes to represent the interactions

between the carbon cycle and the physical climate system (Booth et al. 2013). In these

models, the carbon cycle and the physical climate system are connected using carbon

concentrations and carbon feedbacks (Arora et al. 2013).

In the model I consider a broad spread of projected temperature change scenarios

to find the range of possibilities for different California amphibian species. The four

scenarios I chose to evaluate came from the following simulated datasets, MIROC-ESM,

Had-GEM2-ES, IPSL-CM5A-LR, and MPI-ESM-LR (WorldClim). Other studies have

found, by running as many as 57 Earth system simulations comparing future atmospheric

10

CO2 concentrations that the MRI-ESM-LR scenario falls on the lower bound of the

simulations and the MIROC-ESM scenario falls on the upper bound (Booth et al. 2013). I

include both of these scenarios to hopefully obtain a plausible range of amphibian

distribution predictions.

The varying climate scenarios contain relatively small differences when run over

the next 30-50 years, but vary greatly by the end of the century because each scenario

incorporates different climate uncertainties. This poses challenges for long-scale

predictions, however, investigating a number of varying scenarios allows us to

understand the consequences of the uncertainties (Booth et al. 2013).

We do not know which of the climate projections are most likely to occur, which

is why we run a few different simulations using four different climate scenarios in the

model. However, the least severe scenario in terms of predicted CO2 emissions (MRI-

ESM) already underestimates observed trends of CO2 emissions (Booth et al. 2013).

Thus, the MRI-ESM scenario may not be as relevant as our other three climate scenarios.

Each emission scenario includes a range of radiative forcing values of

representative concentration pathways (RCP). The four options for each scenario are 2.6,

4.5, 6.0, and 8.5 W/m^2. I chose to run each emission scenario with rcp85 reflecting 8.5

W/m^2. I only run one rcp value for each emission scenario because other studies have

demonstrated that the greatest changes in global temperatures arise from emission-driven

models. In fact, RCP8.5 model projections produce a large enough range of possibilities

to encompass all responses of the CMIP5 emission-driven simulations (Booth et al.

2013). Thus, I do not find it necessary to run varying rcp scenarios.

11

Model Methods

To begin the model, I matched records with climate data to predict future

amphibian declines with climate change. To locate all available California amphibian site

records, I reviewed herpetological data from natural history museums on HerpNET.

These records allowed us to evaluate changes in amphibian distribution and abundance

over a large region encompassing different habitats and a variety of amphibian species.

This included geographic data for all 29 frog and toad species found in California

belonging to the six broader frog and toad families. I removed repeat coordinates by

deleting entries less than 1 km away from other data points. I also renamed outdated

species records to maintain the most recent taxonomic names for all species. I was able to

access a range of museum records for each amphibian family with as many as 1838 and

1796 records for Ranidae and Hylidae species respectively, and only 65 records for both

Pipidae and Ascaphidae species.

I used climate layers from the IPCC fifth assessment on WorldClim to compile

current daily maximum temperatures and precipitation at 2.5 arc-minutes resolution

(approximately a 5 km grid size) (Hijmans et al. 2005). I considered breeding season

temperatures and winter precipitation, with breeding season months as March, April, and

May, and winter precipitation as the average of November-March. I chose to use

breeding season temperatures because of other studies that have found the most

significant contributor to variation in vagility and subsequent successes to be breeding

season temperatures (Hillman 2014). Additionally, past studies have found that winter

precipitation along the west coast largely determines water levels in lakes and ponds the

12

following spring, meaning dry conditions associated with climate trends could be causing

amphibian declines (Pounds 2001).

My goal was to predict the colonizations and extirpations of amphibian species

from now until the year 2070. To do this, I assessed the interaction between temperature

and precipitation using presence data of species from the museum records. I used the

Biomod2 package on R to spatially display the current presences and absences of varying

amphibian families. I used what is called “pseudo-absence” data, meaning I do not have

confirmed records of absences, just positive confirmation of presences. These presence-

only models are more frequently used than presence/absence models because of the wide

availability of occurrence data from museum records as opposed to presence absence data

from surveys (Hijmans 2012). I used this pseudo-absence data to categorize the habitat

around each record as presences and classify the habitat around a specified radius of each

record as current suitable amphibian habitat. The model also used the temperature and

precipitation patterns of areas classified as suitable habitat to categorize other regions

with similar climate patterns as additional suitable habitat. All areas outside of these

regions of suitable habitat were classified as species absences. Using this data, I

developed a spatial map of amphibian distributions in the present day. I had access to

many museum records for each amphibian family with as many as 1838 and 1796 records

for Ranidae and Hylidae species respectively. With this many records, I can be

reasonably confident with the predicted areas of amphibian presences.

Next I created response curves that displayed the thresholds in breeding season

temperature and winter precipitation according to current distribution values. This way, I

evaluated the ranges of breeding season of temperatures and precipitations that each

13

amphibian family was able to tolerate, with temperature and precipitation as separate

independent variables.

Then, I created a three-dimensional surface to display the interaction term

between temperature and precipitation using the formula: a + b*temperature +

c*precipitation + d*temperature*precipitation. This allowed me to evaluate the ways the

interaction between temperature and precipitation influenced amphibian distribution

dynamics. Using this interaction, I created maps that estimated all current amphibian

distributions across California based on current temperature and precipitation trends.

In order to create a legend that can be understood as a probability, I transformed

this distribution map using binaries to fit the model onto a scale from 0 to 1. This allowed

me to calculate an index that can be interpreted as a probability that the species would

occur in a certain region, with 1 being flourishing populations, .5 being diminishing

populations, and 0 as absence populations, with different colors representing these

different values on the spatial map.

I predicted future populations by downloading WorldClim future prediction

scenarios for California. I chose sample climate scenarios MIROC-ESM, Had-GEM2-ES,

IPSL-CM5A-LR, and MPI-ESM-LR for the year 2070. These models originated from

Model for Interdisciplinary Research on Climate, Earth System Model; Hadley Centre

Global Environmental Model, version 2 (Earth System); Institut Pierre-Simon Laplace

Coupled Model, version 5A, coupled with Nucleus for European Modelling of the Ocean

(NEMO), low resolution; and Max Planck Institute Earth System Model, low resolution.

To assess which amphibian families were most vulnerable to extinction in comparison to

the other families, I chose MIROC-ESM as the baseline scenario. I used a binary scale to

14

predict future population distributions based on future temperature and precipitation

predictions. I chose these scenarios to produce a range of possibilities for projected future

climates.

I compared current and future distributions by calculating the change in habitat

area. I defined change as current distribution + 2*future distributions. This allowed me to

plot predicted future changes on a scale of 0 to 3, 3 being stable populations (dark green),

2 being potential colonizations (light green), 1 being extirpations (orange), and 0 as

absences (white). I mapped this change for each California amphibian family.

I calculated the predicted area of change between current suitable habitat and

future suitable habitat by dividing future/current grid cell areas. Subsequently, I

calculated the probability of extinction as 1- area change ^.25 (Thomas et al. 2004). I

plotted proportion of remaining habitat and probability of extinction for each amphibian

family to determine the families that were most rapidly and most likely to go extinct by

2070. I tested the level of robustness of these predictions by randomly eliminating 10%

of data points for each amphibian family and plotting the standard deviations of the

former and latter models.

I attempted to obtain the varying future outcomes of each amphibian family by

running the model under different climate scenarios. I ran the four climate scenarios for

each amphibian family and calculate the means and standard deviations for each one to

find a range of future predictions for each family.

15

Results and Discussion

I found future mass extinctions in 5 out of 6 California amphibian families.

Pipidae species were the only species able to successfully recolonize, and

Scaphiopodidae species were projected to face absolute extinctions by the year 2070. I

found Scaphiopodidae to be most vulnerable to extinctions, followed by Ascaphidae,

Hylidae, Bufonidae, Ranidae, and Pipidae. The only species predicted to be successful in

the future happens to be Pipidae species, the species that are invasive to California.

These species (fully comprised of X. Laevis in our model) are adaptable, exclusively

aquatic, and voracious predators as they often eat the young of native amphibian

populations. They may even be the initial source of Bd in California, as well as an

ongoing vector of the disease (McCoid and Fritts 1980).

By looking at the response curves of each amphibian family, I found, with the

exception of Pipidae species, that species successes are most strongly limited by breeding

season temperatures, not winter precipitation. In fact, in Pipidae species, temperatures did

not have any influence on successes. Pipidae also had smaller ranges in their winter

precipitation response curves than the other amphibian families, meaning that they were

even more sensitive to changes in precipitation than the other species. However, the

future precipitation predictions must fall within the range of the Pipidae response curves

because Pipidae are expected to be most successful. All other species were more strongly

influenced by changes in breeding season temperatures because these species had larger

ranges of possibilities for precipitation, but smaller ranges for temperatures. Thus, in the

two-dimensional model, it seems that Pipidae were successful because they were limited

by precipitation rather than the breeding season temperatures that limit the native species.

16

Precipitation influenced amphibian dynamics more strongly in the three-

dimensional surfaces displaying the interactions between temperature and precipitation

(Figures 4, 5). The least successful species (Scaphiopodidae) had a very small range of

possibilities for precipitation and temperatures, slightly more successful species

(Ascaphidae, Hylidae) had larger ranges of possible breeding season temperatures,

moderately more successful species (Bufonidae, Ranidae) had an even larger range of

breeding season temperatures and a larger range of winter precipitation trends, and the

most successful species (Pipidae) had resilience to all breeding season temperatures.

When examining these spatial distribution maps, I found that current species range

distributions did not necessarily indicate the trends of future distributions. For example,

the species with large distributions now are not the ones predicted to have the largest

distributions in the future. The spatial maps indicate that most species are predicted to

lose most of their habitat range, with populations restricted to the northwestern edges of

the current ranges.

On these spatial maps, I found that Ranidae and Bufonidae species were predicted

to occupy a similar range in the California sierras and along the northwestern coast. This

new range contained few to no recolonizations, with species restricted to the northern

section of their current range. I found Ascaphidae and Hylidae species to occupy a very

small range in the northwest corner of California, with the southern and eastern sections

of their current habitat range completely eliminated. Lastly, I found that future

Scaphiopodidae species are expected to cease existing in all regions of California.

In the spatial distribution maps it also seemed that the native amphibian families

ceased to persist in regions inhabited by Pipidae species. The model predicted these

17

species to recolonize just north of their current distributions, maintaining varying habitats

scattered across many regions of California. This corresponds with other studies in which

native amphibians and introduced fish and bullfrogs did not co-occur, with introduced

exotics occupying low-elevation sites, and native species persisting primarily at higher

elevations (Fisher and Shaffer 1996). It thus seems plausible that habitat modifications or

low levels of topographic have allowed exotic species to invade low-elevation sites, and

this has contributed to the native amphibian declines. This supports our predictions that in

the future the exotic species will further invade low-elevation sites and eliminate native

species in these regions. Introduced exotics also tend to thrive in highly modified

habitats, further supporting our predictions for colonization of invasive species with

further California habitat modification and degradation (Fisher and Shaffer 1996).

In addition to being spatially separated from the invasive species, it is also

possible that the native species will be more heavily influenced by temperature variability

or temperature-dependent immunity to disease than the invasive species. In this way,

changes in temperature could also cause a shift from optimal levels of amphibian

immunity to disease, increasing susceptibility of native amphibians to infection (Raffel et

al. 2006). Once we further understand these disease dynamics, we can incorporate the

effects of temperature-dependent immunity into future models.

There is also much evidence that El Niño climatic events drive widespread

amphibian losses through increased regional temperature variability (Rohr and Raffel

2010). In the Cascades, for example, El Niño events have been connected to increased

mortality of Bufonidae and Ranidae eggs in years with reduced precipitation (Corn et al.

2003). Given that global climate change seems to increase temperature variability,

18

extreme climate events and the strength of El Niño episodes could exacerbate worldwide

amphibian declines. These events reduce amphibian defenses against pathogens and

could increase susceptibility to disease (Rohr and Raffel 2010). Thus, in years with

increased climate variability, the effects on amphibians could be even more dramatic than

those predicted in our model.

Additional environmental harm from climate changes could emerge from habitat

fragmentation. This is important for many species because frogs and toads may travel

kilometers over land to reach other ponds and foraging areas. Future habitats may not be

physically fragmented, but from an amphibian perspective they could be “thermally

fragmented”. In this way, microenvironmental conditions could be altered enough to

increase the cost of activities such as foraging (Bartelt et al. 2010). This habitat

fragmentation could further increase population declines caused by the predicted habitat

losses in the model.

We also found the varying climate scenarios to influence the amphibian families

differently. Although MIROC-ESM is predicted to be the most severe climate scenario

for future CO2 concentrations, and MPI-ESM-LR is supposed to be the lower bound for

CO2 concentration, this does not translate into these scenarios having the most severe

influence on amphibian populations. The predictions for each family of Ranidae,

Bufonidae, Hylidae, and Ascaphidae display a different one of the four scenarios as most

severe for their particular species. Thus, CO2 concentrations do not necessarily translate

into direct habitat losses and species extinctions. It seems that the microclimates are more

important in predicting future amphibian habitat. This holds implications for future

legislation practices. Policymakers need to be aware that management based on levels of

19

CO2 in the atmosphere and emissions scenarios doesn’t actually translate to lower levels

of extinction.

When calculating the means and standard deviations for each climate scenario, I

found the highest uncertainty (standard deviations) for proportion of habitat remaining in

Pipidae, followed by Ascaphidae, Ranidae, Bufonidae, Hylidae, then Scaphiopodidae.

However, these uncertainties were not the same for probability of extinction, with highest

uncertainties found in Hylidae, followed by Ascaphidae, Pipidae, Bufonidae, Ranidae,

and then Scaphiopodidae. Scaphiopodidae species had standard deviations of zero, as all

scenarios predicted complete extinctions. Although the uncertainties varied family to

family, the general succession of species most likely to go extinct remained the same

even when considering the uncertainties.

When testing the level of robustness of the model by randomly eliminating 10%

of data, I found all families as fairly robust, with the exception of Pipidae. These species

had a standard deviation of almost 1.5 for proportion of remaining habitat, and also

contained the highest standard deviation for probability of extinction. It is interesting that

the model is fairly robust for all families with the exception of Pipidae species. This

suggests that we need to conduct more research to determine Pipidae’s future

distributions since the model indicates they are very sensitive to deletion of data.

In conclusion, we predict that Pipidae, the invasive species, will be most

successful in future colonizations, while all other species will experience severe

extinctions. It seems that the native species, Ranidae, Bufonidae, Ascaphidae, Hylidae,

and Scaphiopodidae, are more strongly influenced by breeding season temperatures than

precipitation. In fact, the reason why the invasive Pipidae species are able to thrive seems

20

to be their complete resilience to changes in breeding season temperatures. We are still

unsure of the interactions between climate and Bd-related amphibian declines, however,

we expect Bd infections as well as climate variability to further exacerbate the

predictions formed in our model. In the future, we hope to further improve our climate

models in order to determine the most accurate climate scenarios based on current

emission and climate trends.

Bd

Another factor that is likely to be a large future threat to amphibians is the

pathogenic chytrid fungus, Bd. Since the complex interactions between Bd and

temperature are still unclear, our model does not take this into account. However, since

Bd is one of the factors causing many amphibian losses, it should be incorporated into

future models.

Amphibian susceptibility to Bd may be due to multiple factors, including host

physiology, host life history, environmental conditions, and community structure (Searle

et al. 2011). Stream-breeding amphibians are more likely to be infected with Bd than

pond-breeders and almost all infected animals occur in permanent water bodies (Kriger

and Hero 2007). High-elevation species with restricted ranges are most likely to

experience Bd-related declines (Bielby et al. 2008). Larger species such as toads could

potentially be more susceptible to Bd because they have more space for a pathogen to

colonize (Searle et al. 2011).

Studies have found Bufonidae and Ranidae, to have the highest rates of Bd-

related mortality, with Hylidae having the lowest mortality rates (Searle et al. 2011).

21

Some Bd-exposed Hylidae were not even infected with Bd despite that fact that they were

inoculated with Bd four separate times. This suggests that individuals of this family may

be resistant to acquiring Bd or can quickly recover from a Bd infection. Results of other

studies also suggest that Bufonidae are more susceptible to Bd than other amphibian

species and families.

Thus if I were to incorporate the effects of Bd into my model, I would expect the

declines to be most severe in Bufonidae species since Bufonidae species are expected to

be most susceptible to Bd. I would not expect much of a change in Hylidae species from

my current model since Hylidae aren’t expected to experience severe Bd-related losses.

When incorporating Bd into a future model, perhaps the predictions for Hylidae and

Bufonidae species would end up having similar levels of declines. Once we are clearer on

the mechanisms behind how temperature influences Bd, we can add in these subsequent

declines into a model that would incorporate mortality based on temperature change.

Assumptions and Sources of Error

In the formation of the model, I relied on the quality of the museum records for

baseline historical data, which did not take into account every population of amphibian

species. However, museum records should provide a reasonable basis for documenting

historical patterns at the large scale over which we are working. Even with the relatively

incomplete samples found within museum collections, the large size of the records made

it possible to find historical patterns of distribution and patterns of decline (Fisher and

Shaffer 1996).

22

Additionally, I correlated WorldClim, “current” climate data (defined as the time

period, 1950s-2000s) with California museum records that span the last century, some of

which date as early as the 19th

century. It is likely that temperature and precipitation

patterns varied during the time of these earlier records. However, this should not be too

significant because many of the climatic changes did not begin to occur until the 80’s and

90’s. Also, many amphibian declines have already occurred, which I did not take into

account in the model of current distributions. In this way, the spatial map of current

distributions more accurately displays temperature and precipitation trends that provide

suitable amphibian habitat rather than an accurate representation of the current spatial

populations of amphibians.

Within the climate scenarios, there are many uncertainties in the carbon emissions

produced by land-use change. This significantly contributes to carbon emissions and

needs to be accounted for to produce the most accurate models. Furthermore, sampling

uncertainties arising from atmospheric physics, land carbon cycle, ocean physics, and

sulfur cycle can lead to a broad range of future atmospheric CO2 and temperature

responses, and should be incorporated in future models (Booth et al. 2013).

Future Research

We are already initiating research to validate our predictions, conducting a 5-year

field study deploying agar frog models across California. We aim to empirically test the

model to determine the extent to which amphibian extinctions are caused by temperature

and precipitation changes. At each site we deploy agar models in varying microhabitats to

understand the relationship between microhabitat variations and water loss rates in

23

amphibians. We vary wet, dry, sun, shade, day, and night conditions. By evaluating water

loss rates at sites where each species is extinct and extant, we determine the difference in

habitat properties between these two types of sites and hopefully identify the key driving

factors in extinctions. Additionally, we are testing the leaf water potentials and

photosynthetic rates of native California plants using a pressure chamber and a leaf

porometer. This way, we will be able to predict future habitat change and future

amphibian declines caused by these habitat alterations. When we lose local California

habitat, we also lose the local fauna with the disappearance amphibian refuges.

24

Figures

Figure 1: Hylidae’s two-dimensional response curves

Figure 2: Pipidae’s two-dimensional response curves

25

Figure 3: Hylidae’s three-dimensional responses

Figure 4: Hylidae’s predicted change in distrubutions. Orange: extirpations, dark green:

stable populations, light green: potential colonizations, gray: absences.

26

Figure 5: Pipidae’s predicted change in distrubutions. Orange: extirpations, dark green:

stable populations, light green: potential colonizations, gray: absences.

Figure 6

27

Figure 7

Figure 8

28

Works Cited

Arora, Vivek K. et al. 2013. “Carbon-Concentration and Carbon-Climate Feedbacks in CMIP5 Earth

System Models.” Journal of Climate 26: 5289–5314.

Bartelt, Paul E., Robert W. Klaver, and Warren P. Porter. 2010. “Modeling Amphibian Energetics, Habitat

Suitability, and Movements of Western Toads, Anaxyrus (=Bufo) Boreas, across Present and Future

Landscapes.” Ecological Modelling 221: 2675–86.

Bielby, Jon et al. 2008. “Predicting Susceptibility to Future Declines in the World’s Frogs.” Conservation

Letters 1: 82–90. http://dx.doi.org/10.1111/j.1755-263X.2008.00015.x.

Blaustein, Andrew R., and Joseph M. Kiesecker. 2002. “Complexity in Conservation: Lessons from the

Global Decline of Amphibian Populations.” Ecology Letters 5: 597–608.

Booth, B. B B et al. 2013. “Scenario and Modelling Uncertainty in Global Mean Temperature Change

Derived from Emission-Driven Global Climate Models.” Earth System Dynamics 4: 95–108.

Bradford, David F, Christina Swanson, and Malcolm S Gordon. 1992. “Effects of Low pH and Aluminum

on Two Declining Species of Amphibians in the Sierra Nevada, California.” Journal of Herpetology

26: 369–77. http://www.jstor.org/stable/1565113.

Buckley, Lauren B, and Walter Jetz. 2007. “Environmental and Historical Constraints on Global Patterns of

Amphibian Richness.” Proceedings. Biological sciences / The Royal Society 274: 1167–73.

Corn, Paul Stephen et al. 2003. “Amphibian Breeding and Climate Change: Importance of Snow in the

Mountains.” Conservation Biology 17: 622–27.

Drost, Charles A et al. 1996. “Collapse of a Regional Frog Fauna in the Yosemite Area of the California

Sierra Nevada , USA.” 10(2): 414–25.

Elith, Jane, and John R Leathwick. 2009. “Species Distribution Models : Ecological Explanation and

Prediction Across Space and Time.” Systematics 40: 677–97.

Fisher, Robert N, and H Bradley Shaffer. 1996. “The Decline of Amphibians in California’s Great Central

Valley.” Conservation Biology 10: 1387–97.

Hijmans, Robert J. et al. 2005. “Very High Resolution Interpolated Climate Surfaces for Global Land

Areas.” International Journal of Climatology 25(15): 1965–78.

http://doi.wiley.com/10.1002/joc.1276 (May 25, 2014).

———. 2012. “Cross-Validation of Species Distribution Models: Removing Spatial Sorting Bias and

Calibration with a Null Model.” Ecology 93: 679–88.

Kriger, Kerry M., and Jean Marc Hero. 2007. “The Chytrid Fungus Batrachochytrium Dendrobatidis Is

Non-Randomly Distributed across Amphibian Breeding Habitats.” Diversity and Distributions 13:

781–88.

Mccoid, M. J., & Fritts, T. H. (1980). Notes on the Diet of a Feral Population of Xenopus laevis (Pipidae)

in California. The Southwestern Naturalist, 25(2), 272.

29

Pounds, J A. 2001. “Climate and Amphibian Declines.” Nature 410: 639–40.

Pounds, J Alan et al. 2006. “Widespread Amphibian Extinctions from Epidemic Disease Driven by Global

Warming.” Nature 439(7073): 161–67. http://www.ncbi.nlm.nih.gov/pubmed/16407945 (May 25,

2014).

Raffel, T. R., J. R. Rohr, J. M. Kiesecker, and P. J. Hudson. 2006. “Negative Effects of Changing

Temperature on Amphibian Immunity under Field Conditions.” Functional Ecology 20: 819–28.

Rohr, Jason R et al. 2008. “Evaluating the Links between Climate, Disease Spread, and Amphibian

Declines.” Proceedings of the National Academy of Sciences of the United States of America 105(45):

17436–41.

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2582253&tool=pmcentrez&rendertype=a

bstract (June 4, 2014).

Rohr, Jason R, and Thomas R Raffel. 2010. “Linking Global Climate and Temperature Variability to

Widespread Amphibian Declines Putatively Caused by Disease.” Proceedings of the National

Academy of Sciences of the United States of America 107(18): 8269–74.


bstract (May 25, 2014).

Searle, C. L. et al. 2011. “Differential Host Susceptibility toBatrachochytrium Dendrobatidis, an Emerging

Amphibian Pathogen.” Conservation Biology 25: 965–74.

Thomas, Chris D et al. 2004. “Extinction Risk from Climate Change.” Nature 427(6970): 145–48.

http://www.ncbi.nlm.nih.gov/pubmed/14712274.

Wake, David B, and Vance T Vredenburg. 2008. “Colloquium Paper: Are We in the Midst of the Sixth

Mass Extinction? A View from the World of Amphibians.” Proceedings of the National Academy of

Sciences of the United States of America 105 Suppl : 11466–73.



Whitfield, Steven M et al. 2007. “Amphibian and Reptile Declines over 35 Years at La Selva, Costa Rica.”

Proceedings of the National Academy of Sciences of the United States of America 104(20): 8352–56.



30

Appendix

37

Sample R Code for Ranidae species under the MIROC-ESM climate scenario:

require(dismo) require(tcltk) require(maps) require(maptools) library(gtools) library(fitdistrplus) require(rgeos) require(biomod2) data(stateMapEnv) rm(list=ls()) rana<-read.csv("~/Documents/AmphibianData/Ranidae_cleandata.csv",stringsAsFactors=F) coordinates(rana) = ~ Lon + Lat class(rana) #plot(anaxyrusboreas$Lon,anaxyrusboreas$Lat,pch=19,col="red",cex=.5) #map("world",add=T);map("state",add=T);map("county",add=T) ca=readShapeSpatial("~/Documents/GISstuff/California_Boundary/CA_Bound.shp") plot(ca) amjjTmax = crop(stack(paste0("~/Documents/GISstuff/tmax_2-5m_bil/tmax",c(4:7),".bil")),extend(extent(rana),3)) amjjTmax = mean (amjjTmax) winPPT = crop(stack(paste0("~/Documents/GISstuff/prec_2-5m_bil/prec",c(10:12,1:4),".bil")),extend(extent(rana),3)) winPPT = sum(winPPT) clim = stack(amjjTmax,winPPT) names(clim) = c("Breeding Season Temperature","Winter

38

Precip") DataSpecies <- data.frame(as.data.frame(rana),pres=1) attach(DataSpecies) head(DataSpecies) myRespXY <- DataSpecies[,c("x","y")] myResp <- DataSpecies$pres myExpl <- clim myRespName <- 'rana' myBiomodData <-BIOMOD_FormatingData(resp.var = myResp,expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName,PA.nb.rep = 1, PA.nb.absences = 10000, PA.strategy = 'disk',PA.dist.min = 20000, PA.dist.max = NULL, PA.sre.quant = 0.025, PA.table = NULL,na.rm = TRUE) # run the model with GLM myBiomodOptions<- BIOMOD_ModelingOptions(GLM = list( type = 'quadratic',interaction.level = 1,myFormula = NULL,test = 'BIC',family = 'binomial',control = glm.control(epsilon = 1e-08,maxit = 1000,trace = FALSE) )) myBiomodModelOut <- BIOMOD_Modeling(myBiomodData, models = c('GLM'), models.options = myBiomodOptions, NbRunEval=1, DataSplit=80, models.eval.meth = c('ROC',"TSS"), do.full.models=FALSE,modeling.id="test") # look at the response curves myGLMs <- BIOMOD_LoadModels(myBiomodModelOut, models=c('GLM')) myRespPlot2D <- response.plot2(models = myGLMs,Data = get_formal_data(myBiomodModelOut,'expl.var'),show.variables= get_formal_data(myBiomodModelOut,'expl.var.names'),do.bivariate = FALSE,fixed.var.metric = 'median',col = c("blue", "red"),legend = TRUE,data_species =

39

get_formal_data(myBiomodModelOut,'resp.var')) ######### 3d response plots! ########## myRespPlot3D <- response.plot2(models = myGLMs[1], Data = get_formal_data(myBiomodModelOut,'expl.var'), show.variables= get_formal_data(myBiomodModelOut,'expl.var.names'), do.bivariate = TRUE, fixed.var.metric = 'median', data_species = get_formal_data(myBiomodModelOut,'resp.var')) myGLMs <- BIOMOD_LoadModels(myBiomodModelOut, models=c('GLM')) get_formal_model(get(myGLMs[1])) get_variables_importance(myBiomodModelOut) myBiomodProj <- BIOMOD_Projection( modeling.output = myBiomodModelOut, new.env = myExpl, proj.name = 'current', selected.models = 'all', binary.meth = 'ROC', compress = 'xz', clamping.mask = F, output.format = '.grd') plot(myBiomodProj) getFormalModel(myBiomodProj)

40

##predicted distribution map par(mfrow=c(1,1)) plot(get_predictions(myBiomodProj)[[1]],main="Predicted Distribution") points(rana,pch=".",cex=2) #plot(Gwis_rm,add=T) #plot(Gsila_rm,add=T) #plot(Gcop_rm,add=T) plot(ca,add=T) plot(BinaryTransformation(get_predictions(myBiomodProj)[[1]],500)) r = BinaryTransformation(get_predictions(myBiomodProj)[[1]],500) points(rana,pch=".",cex=2) plot(ca,add=T) ASM_r = get_predictions(myBiomodProj)[[1]]/1000 # should be prob_of_occ plot(ASM_r, main = "Probability of Occurence") points(rana,pch=".") plot(ca,add=T) pred_values = extract(ASM_r , get_formal_data(myBiomodModelOut)@ coord ) pa = get_formal_data(myBiomodModelOut)@ data.species pa[is.na(pa)] = 0 p = pred_values[pa==1] a = pred_values[pa==0] library(dismo) eval = dismo::evaluate(p,a) t = eval@t[which.min(abs(eval@TNR-eval@TPR))]#set threshold such that TPR=TNR predicted_distribution = reclassify(ASM_r, c(0,t,0,t,1,1)) plot(predicted_distribution ) points(rana,pch=".")

41

################## R0 ############# R0 = ASM_r *.5 # thresholded occurence, e.g presence absence ############### Future Projection ############## winPPT_fut = sum(crop(stack(paste0("~/Documents/GISstuff/mr85pr70/mr85pr70",c(10:12,1:4),".tif")),extent(ca))) #adult active months/ breeding season temperature amjjTmax_fut = mean(crop(stack(paste0("~/Documents/GISstuff/mr85pr70/mr85pr70",c(4:7),".tif")),extent(ca)))/10 ca_r = rasterize(ca,winPPT_fut) plot(ca_r) winPPT_fut = winPPT_fut*ca_r plot(winPPT_fut) amjjTmax_fut = amjjTmax_fut*ca_r plot(amjjTmax_fut) # Adult Survival myExpl = stack(amjjTmax_fut,winPPT_fut) names(myExpl) = c("Breeding Season Temperature","Winter Precip") myBiomodProj_fut <- BIOMOD_Projection( modeling.output = myBiomodModelOut, new.env = myExpl, proj.name = '2070 MIROC', selected.models = 'all',

42

binary.meth = 'ROC', compress = 'xz', clamping.mask = F, output.format = '.grd') surv_adult_fut = get_predictions(myBiomodProj_fut)[[1]]/1000 plot(surv_adult_fut, main = "Adult Survival Future") points(rana,pch=".") ASM_r_fut = surv_adult_fut predicted_fut_distribution = reclassify(ASM_r_fut, c(0,t,0,t,1,1)) plot(predicted_distribution, , main="Cur dist") points(rana,pch=".") plot(predicted_fut_distribution, main="Fut dist") points(rana,pch=".") change = predicted_distribution + predicted_fut_distribution*2 plot(change, main = "Difference Between Current and Future Distributions (MIROC-ESM)") fut_suit_area = cellStats(predicted_fut_distribution * area(predicted_fut_distribution),sum) cur_suit_area = cellStats(predicted_distribution* area(predicted_distribution),sum) area_change = fut_suit_area/ cur_suit_area prob_extinct = 1 - (area_change^.25) area_change prob_extinct