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\begin{document}

\title{Projected climate change threatens significant range contraction of
\emph{}Cochemiea halei\emph{} (Cactaceae), an island endemic, serpentine
adapted plant species at risk of extinction}



\author[1]{Peter Breslin}%
\author[1]{Martin Wojciechowski}%
\author[1]{Fábio Albuquerque}%
\affil[1]{Arizona State University}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Aim: Threats faced by narrowly distributed endemic plant species in the
face of the Earth's sixth mass extinction and climate change exposure
are especially severe for taxa on islands. We investigated the current
and projected distribution and range changes of Cochemiea halei, an
island endemic cactus. This taxon is of conservation concern, currently
listed as vulnerable on the International Union for the Conservation of
Nature Red List and as a species of special concern under Mexican
federal law. The goals of this study are to 1). identify the
correlations between climate variables and current suitable habitat for
C. halei; 2). determine if the species is a serpentine endemic or has a
facultative relationship with ultramafic soils; 3). predict range
changes of the species based on climate change scenarios. Location: The
island archipelago in Bah\selectlanguage{ngerman}ía Magdalena on the Pacific coast, Baja
California Sur, Mexico. Methods: We used temperature and precipitation
variables at 30 arcsecond resolution and soil type, employing multiple
species distribution modeling methods, to identify important climate and
soil conditions driving current habitat suitability. The best model of
current suitability is used to predict possible effects of four climate
change scenarios based on best case to worst case representative
concentration pathways, with projected climate data from two general
circulation models, over two time periods. Main conclusions: The
occurrence of the species is found to be strongly correlated with
ultramafic soils. The most important climate predictor for habitat
suitability is annual temperature range. The species is predicted to
undergo range contractions from 21\% to 53\%, depending on the severity
and duration of exposure to climate change. The broader implications for
a wide range of narrowly adapted, threatened and endemic plant species
indicate an urgent need for threat assessment based on habitat
suitability and climate change modeling.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\section*{\texorpdfstring{Projected climate change threatens significant
range contraction of \emph{Cochemiea halei} (Cactaceae), an island
endemic, serpentine adapted plant species at risk of
extinction}{Projected climate change threatens significant range contraction of Cochemiea halei (Cactaceae), an island endemic, serpentine adapted plant species at risk of extinction}}

{\label{projected-climate-change-threatens-significant-range-contraction-of-cochemiea-halei-cactaceae-an-island-endemic-serpentine-adapted-plant-species-at-risk-of-extinction}}

Peter B. Breslin, School of Life Sciences, Arizona State University,
Tempe, AZ. pbreslin@asu.edu

Martin F. Wojciechowski, School of Life Sciences, Arizona State
University, Tempe, AZ.

Fabio Albuquerque, Science and Mathematics Faculty, Arizona State
University,~Mesa,~AZ.

Acknowledgments: Thanks to Janet Franklin for her assistance and
provision of R code in her extremely useful Biotic Distributions class
at Arizona State University; Erick Lundberg and his assistance with GIS
in R; Karla Moeller for reading the manuscript and offering helpful
suggestions; and Capitan Marco at Magdalena Bay Whales for hospitality
and reliable transportation to the islands. Funding for this project was
provided by more than 30 donors to a crowd funding effort on
Experiment.com, the Central Arizona Cactus and Succulent Society, the
San Diego Cactus and Succulent Society, the Cactus and Succulent Society
of America, and Arizona State University's College of Liberal Arts and
Sciences. A version of this paper was part of Peter Breslin's Ph.D.
dissertation, submitted in partial fulfillment of the requirements for
the Environmental Life Sciences Ph.D. program at Arizona State
University.

\subsubsection*{BIOGRAPHICAL SKETCH}

{\label{biographical-sketch}}

Peter B. Breslin began PhD studies in the fall of 2014, investigating
questions regarding the biogeography and evolution of Cactaceae. Under
the advisement of Martin Wojciechowski, with the help of committee
members Lucas Majure, Shannon Fehlberg, Fabio Albuquerque and Jon
Rebman, Breslin pursued research into the molecular phylogenetics,
ancestral biogeography, climate change impacts on habitat suitability
and population viability of the \emph{Mammillaria} and
\emph{Cochemiea}(Cactaceae) of Baja California and adjacent regions.

\section*{\texorpdfstring{Projected climate change threatens significant
range contraction of \emph{Cochemiea halei} (Cactaceae), an island
endemic, serpentine adapted plant species at risk of
extinction}{Projected climate change threatens significant range contraction of Cochemiea halei (Cactaceae), an island endemic, serpentine adapted plant species at risk of extinction}}

{\label{projected-climate-change-threatens-significant-range-contraction-of-cochemiea-halei-cactaceae-an-island-endemic-serpentine-adapted-plant-species-at-risk-of-extinction-1}}

ABSTRACT

\textbf{Aim:} Threats faced by narrowly distributed endemic plant
species in the face of the Earth's sixth mass extinction and climate
change exposure are especially severe for taxa on islands. We
investigated the current and projected distribution and range changes
of\emph{Cochemiea halei} , an island endemic cactus. This taxon is of
conservation concern, currently listed as vulnerable on the
International Union for the Conservation of Nature Red List and as a
species of special concern under Mexican federal law. The goals of this
study are to 1). identify the correlations between climate variables and
current suitable habitat for \emph{C. halei} ; 2). determine if the
species is a serpentine endemic or has a facultative relationship with
ultramafic soils; 3). predict range changes of the species based on
climate change scenarios.

\textbf{Location:} The island archipelago in Bah\selectlanguage{ngerman}ía Magdalena on the
Pacific coast, Baja California Sur, Mexico.

\textbf{Methods:} We used temperature and precipitation variables at 30
arcsecond resolution and soil type, employing multiple species
distribution modeling methods, to identify important climate and soil
conditions driving current habitat suitability. The best model of
current suitability is used to predict possible effects of four climate
change scenarios based on best case to worst case representative
concentration pathways, with projected climate data from two general
circulation models, over two time periods.

\textbf{Main conclusions:} The occurrence of the species is found to be
strongly correlated with ultramafic soils. The most important climate
predictor for habitat suitability is annual temperature range. The
species is predicted to undergo range contractions from 21\% to 53\%,
depending on the severity and duration of exposure to climate change.
The broader implications for a wide range of narrowly adapted,
threatened and endemic plant species indicate an urgent need for threat
assessment based on habitat suitability and climate change modeling.

\textbf{Keywords} : climate change, species distribution modeling,
biodiversity, biogeography, range shifts, serpentine adaptation, island
endemism, Cactaceae, endangered species

\subsubsection*{INTRODUCTION}

{\label{introduction}}

Cactaceae are the 5\textsuperscript{th} most endangered plant or animal
family to be globally assessed to date by the International Union for
the Conservation of Nature (Goettsch et al., 2015). The primary known
threats to populations of cactus species are poaching of wild
populations for the horticultural trade, small scale farming and
ranching, mining operations, and the effects of climate change
(Anderson, Taylor, Montes, \& Cattabriga,1994;~Hernández \&
Godínez-Álvarez 1994; Oldfield, 1997; Bárcenas-Luna,
2003;~Godínez-Álvarez, Valverde \& Ortega Baes, 2003;~Téllez-Valdés \&
Dávila-Aranda, 2003;~Martorell \& Peters 2005; Ureta \& Marti 2012;
Goettsch et al.). Aside from these threats, increased risk of extinction
in Cactaceae due to climate change exposure is not well understood
(Goettsch et al.). This study aims to help fill this gap, using
predictive modeling to anticipate the extinction risk due to current
conditions and climate change impacts, faced by an island endemic,
threatened cactus.

Temperature and precipitation have been shown to be strong correlates
for the distribution of plant species (e.g., Guisan \& Zimmerman, 2000;
Guisan \& Thuiller, 2005; Hawkins et al., 2003; Elith \& Franklin,
2013). Cacti are often narrowly adapted to specific thermal niches, as
well as highly sensitive to seasonal precipitation patterns; for
example, the bi-seasonal winter/summer precipitation cycles of the
Sonoran Desert region, as well as longer precipitation cycles caused by
shifts in the California current and other factors (Gibson \& Nobel,
1986; Anderson, 2001). Islands often have both thermal and precipitation
differences from their nearest peninsular or continental land masses,
and these factors have been shown to contribute to island endemism and
increased risk to island biodiversity (Kreft, Jetz, Mutke, Kier, \&
Barthlott, 2008; Humphreys, Govaerts, Ficinski, Lughadha, \& Vorontsova,
2019). Island climates are often significantly distinct from those of
adjacent land masses, and, as a result, islands harbor a significant
amount of plant biodiversity (e.g., Kreft et al., Kier et al., 2009),
yet island ecosystems also host endemic plant species subject to a rate
of extinction 500 to 1000 times higher than the background rate
(Humphreys et al.).

Ultramafic soils, predominant in the distribution of \emph{C. halei} ,
such as ophiolite, amphibolites, serpentine, and gabbros, have been
shown to drive plant endemism (Kruckberg 1951; Kazakou, Dimitrakopoulos,
Baker, Reeves, \& Troumbis, 2008; Botha \& Slomka 2017). These soils
contain high proportions of heavy metals and low quantities of plant
nutrients, and are toxic to most plant species. Consequently, species
adapted to these soils have a competitive edge and are able to colonize
areas that other plants cannot (Brady, Kruckberg, \& Bradshaw, 2005;
Harrison, Safford, Grace, Viers, \& Davies, 2006; Anacker, Whittall,
Goldberg, \& Harrison, 2011). To date, no studies of habitat suitability
of cacti associated with ultramafic soils have been done, and the
importance of this substrate to the distribution of \emph{C. halei} is
unknown.

Climate change is likely to affect the future distribution of many plant
species due to shifts in temperature and precipitation (Bakkenes,
Alkemade, Ihle, Leemans, \& Latour, 2002; Walther et al., 2002; Kelly \&
Goulden, 2008; Urban, 2015; Warren, Price, Vanderwal, Cornelius, \&
Sohl, 2018). Prior to 2019, there were very few studies of habitat
suitability and the potential effects of climate change in Cactaceae
(Téllez-Valdés \& Dávila-Aranda, 2003; Martorell \& Peters, 2005;
Butler, Wheeler, \& Stabler, 2012; Albuquerque, Benito, Rodriguez, \&
Gray, 2018). In particular, climate projections under all representative
concentration pathways (RCPs) of atmospheric ``greenhouse gasses'' and
particulates, show increased mean temperatures ranging from 1.5 C to 4.5
C globally, and increased aridification of existing deserts due to
larger areas subject to lower amounts of annual rainfall (Collins et
al., 2013). Although cacti are adapted to arid conditions, prior studies
have shown that they are vulnerable to projected changes in both
temperature and precipitation under climate change scenarios
(Téllez-Valdés \& Dávila-Aranda, Martorell and Peters, Butler et al.,
Albuquerque et al.). The effect of climate change on the future suitable
habitat of \emph{C. halei} is unknown.

Our investigations include identifying the environmental variables that
determine the habitat suitability of \emph{Cochemiea halei} . Abiotic
correlates for the distribution of rare, narrowly restricted endemic
species can provide important insight into suitable habitat, possible
threats to the persistence of populations, and the potential effects of
future climate change (Hawkins et al., 2003; Hijmans \& Graham, 2006;
Benito, Martínez-Ortega, Muñoz, Lorite, \& Peña, 2009; Franklin 2010;
Albuquerque, Castro-Díez, Rodríguez, \& Cayuela, 2011; Albuquerque,
Astudillo-Scalia, Loyola, \& Beier, 2019; Albuquerque, Rodríguez,
Búrquez, \& Astudillo-Scalia, 2019).

Our specific goals are to investigate: 1). the environmental correlates
to the distribution of \emph{C. halei} ; 2). whether populations
of\emph{C. halei} are more likely to occur on ultramafic soil; 3).
whether the species is likely to colonize the peninsula, or if it is
more likely to remain isolated on the islands; 4). the effect of varying
levels of climate change on the future range and as a contributor to the
risk of local and global extinction of \emph{C. halei} over the next 30
to 50 years. This study will help provide background for urgently needed
future analyses of the specific risks faced by narrowly distributed,
endemic and endangered cacti and other island endemic plant species.

METHODS

\textbf{The study site}

Bahía Magdalena is an ecologically significant embayment along the
Pacific coast of the southern Baja California peninsula (Bizzarro,
2008). In contrast to the adjacent coastal plains, the island
archipelago in Bahía Magdalena is part of the North American cordillera,
and has mountainous, rocky terrain as a result (Rangin, 1978; Blake,
Jayco, \& Moore, 1984; Sedlock, 1993). (Fig. 1).

The islands range in elevation from sea level along the bay coastline to
nearly 1000 m. (Blake et al., 1984). Several topographical variations on
the islands create heterogeneous terrain, including flats, sand dunes,
gravel coastlines and highly eroded arroyos. Westerly cliffs drop to the
Pacific, at angles as steep as 90\selectlanguage{english}\textsuperscript{*}. These cliffs are
predominantly exposed ultramafic rock and gravel (Rangin, 1978; Blake et
al., 1984).

The main soil mineralogical composition is serpentine rock and its
eroded derivatives or non-ultramafic basalt and sand (Rangin, 1978;
Blake et al., 1984; Sedlock, 1993). The mountainous ridges consist of
ultramafic, oceanic crustal rock formed through tectonic plate
collisions estimated to have occurred from the Late Jurassic to the Late
Cretaceous periods (Sedlock, 1993, Zaitsev, Sanchez-Montante, \&
Robinson 2007).

Climate data from WorldClim v. 2.0 (Fick \& Hijmans, 2017) shows
biseasonal summer and winter precipitation, with autumn and spring being
the dry months of the year. The moderating effects of the California
current system create narrower diurnal and annual temperature ranges,
increased precipitation and cooler seasonal averages for the islands
than for the adjacent peninsula (Hickey, 1979; Bakun, 1990; Robinson,
Gomez-Gutierrez, \& Gomez-Aguirre, 2007; Bizzarro, 2008). From 2010 to
2018, 13 tropical storms or hurricanes occurred in the study region,
with the majority occurring in the hottest month, September (National
Hurricane Center; Hurricane Research Division; Central Pacific Hurricane
Center).

The vegetation of the Bahia Magdalena region features 18 endemic
angiosperm taxa; the endemic cacti represent 33\% of the plant endemism
in the area (Leon de la Luz, Medel-Narvez, \& Dominguez-Cadena, 2015).
The area is recognized as one of nine regions of high plant endemism in
Baja California (Reimann \& Ezcurra 2007). The primary vegetative regime
is fog crassicaulescent and sarcocaulescent scrub, that is, a
combination of leaf and stem succulents, such as the endemic \emph{Agave
margaritae} Brandegee and scrub vegetation, generally less than 8 m high
(Rebman \& Roberts, 2012, Leon de la Luz et al.).

\emph{Cochemiea halei} Walton, the study species, is a mat forming stem
succulent with straight spines and presumably hummingbird pollinated
flowers (Craig, 1945; Pilbeam, 1999). \emph{C. halei} is of conservation
concern, assessed as vulnerable by the IUCN, and protected by Mexican
law. The factors leading to its classification as vulnerable are the
narrow geographic range, the low overall population size, and evidence
of declining populations (IUCN). No formal studies of its population
viability or quantified risk of extinction have been conducted
previously, however.

\textbf{Survey methods}

Occurrence data was gathered over a four year period with eight surveys,
in both winter and spring. Random points within the smallest convex
polygon around Isla Magdalena and Isla Santa Margarita were generated
using ArcMap (v. 10.6.1, ESRI, inc.). The random points were used as
centers of survey transects (Elzinga, Salzer, \& Willoughby, 1998;
Bonham, 2013). In addition to occurrence data, we recorded soil type
data and other topographical information, and the latitude and longitude
of occurrences was marked using a handheld GPS device (eTrex 30X, Garmin
Ltd. Olathe KS). A different surveying technique was employed on Cabo
San Lazaro due to the much smaller area of that land mass. At that site,
a belt transect method was used, with individuals counted within a 300 m
radius along a 3 km segment that covered the entire habitat
of\emph{Cochemiea halei} (Bonham). The only known peninsular population,
with six individuals, (Gorelick, 2007), was included in all analyses.

Presence points were spatially thinned to a minimum separation of 1 km
to reduce sampling bias (Tessarolo, Rangel, Araujo, \& Hortal, 2014;
Stolar \& Neilsen, 2015). A point raster layer of presence/absence
locations was generated with 1 km grid squares and multiple records per
square were reduced to at most a single record. Point pattern analysis
was performed using Ripley's K statistic to measure the degree of
spatial correlation of presence/absence records (Baddely, 2008).

\textbf{Environmental variables}

To investigate correlations between the distribution of \emph{Cochemiea
halei} and its environment, we chose 19 energy and precipitation
variables from WorldClim V. 2.0, averages from 1970 to 2000, at 30
arcsec resolution (Fick \& Hijmans, 2017). Soil type was determined
during field surveys using a Munsell soil identification color scale
(Munsell Color, Grand Rapids, MI), categorizing soils into ultramafic
(2.5Y hue with various color values and chroma) versus either
``non-serpentine,'' (approximately 7.5YR to 10YR), or sand (Roberts,
1980). Dense sampling of occurrences of \emph{C. halei} with soil type
data was performed in order to reduce error when interpolating for
missing values (Carl \& Kuhn, 2007; Dormann \& McPherson, 2007; Dormann
et al. 2013). The soil type data from the field was mapped onto zones of
ultramafic versus non-ultramafic substrate, as indicated in the
geological map of Isla Magdalena and Isla Margarita by Rangin (1978).
The soil type raster was generated using inverse distance weighted
interpolation (Goncalves, 2006; Grunwald, 2009) and improved using root
mean squared error and 5-fold cross validation (Goncalves, 2006).

Four representative concentration pathways (RCPs) were used in climate
change projections: 2.6, representing the best case future concentration
of carbon in the atmosphere, through intermediate levels 4.5 and 6.0, to
the worst case scenario of 8.5, as outlined in the Intergovernmental
Panel on Climate Change's Fifth Assessment Report (IPCC 2013; Liddicoat,
Jones, \& Robertson, 2013). The climate data itself was derived from two
general circulation models (GCMs). The GCMs used were the Hadley Center
Global Environmental Model version 2-ES (HadGEM2-ES) and the Community
Climate System Model v. 4 (CCSM4), both of which are frequently used in
studies of climate change effects on habitat suitability (e.g., Bellouin
et al., 2011; Leclere et al. 2014; McQuillan \& Rice, 2015; Albuquerque
et al., 2018). The HadGEM2-ES model scenarios include projections of
changes in ocean temperature and sea ice, and are especially recommended
for use in predicting changes in coastal habitat (Collins et al. 2008,
Caesar et al., 2013).

\textbf{Variable preparation}

Reduction of multicollinearity for all variables was performed by
constructing a correlation matrix and performing hierarchical cluster
analysis, which groups variables according to their mutually related
correlations (Benito, Cayuela, \& Albuquerque, 2013; Sarstedt \& Mooi,
2014; Albuquerque et al., 2018). A cutoff of 0.5 Pearson's correlation
index was used; all variables correlated higher than 0.5 were discarded
(Albuquerque et al.). Biserial correlation analysis, with variables
correlated to presence/absence data for \emph{Cochemiea halei} , was
performed for all variables below 0.5 (Kraemer, 2006; Stolar \& Nielsen,
2015, Albuquerque et al.). From each cluster of correlated variables as
derived from the hierarchical cluster analysis, the variable with the
highest correlation to the distribution of \emph{C. halei} was chosen
for use in modeling.

\textbf{Modeling methods}

Three methods were used for building models: boosted regression trees
(BRT), generalized linear models of the binomial family (GLM) and
maximum entropy (Maxent) (Hijmans \& Graham, 2006; Elith, Leathwick, \&
Hastie, 2008; Franklin, 2010). Models used field survey presence and 200
randomly generated pseudoabsence background points (Franklin, 1995;
Franklin, 2010; Elith, Kearney, \& Phillips, 2010; Elith \& Franklin,
2013; Phillips \& Elith, 2013, Guillera-Arroita et al., 2015).

BRT is an iterative machine learning optimization method, in which the
deviance residuals from a prior decision tree are used as the data for
the next step (called ``boosting''); the decision tree building process
continues until residual deviance is no longer decreased by iterations
(De'ath, 2007; Franklin, 2010). Decision trees, the underlying algorithm
of BRT, also known as classification and regression trees, perform well
with both continuous and categorical variables, and, unlike with GLM,
for example, they are robust to a lack of independence among predictors
(De'ath, 2007; Elith \& Leathwick, 2009; Elith \& Leathwick, 2017;
Albuquerque et al., 2018).

GLM is a well known regression method that uses maximum likelihood as
the measure of the contribution of a variable to a prediction of the
``state'' of a dependent variable, in this case the binary outcome of
presence/absence (Nelder \& Wedderburn, 1972; Guisan, Edwards, \&
Hastie, 2002).

Maximum entropy (Maxent) is a machine learning method that employs
multinomial logistic regression to estimate the probability of the
distribution of a species according to the ``maximum entropy'' of the
distribution, i.e., the most uniform distribution of a species possible
given the limits imposed by the predictor variables (Phillips, Anderson,
\& Schapire, 2006; Elith et al. 2011, Phillips, Anderson, Dudik,
Schapire, \& Blair, 2017).

\textbf{Model evaluation}

For BRT models, evaluation of model performance included measures of
residual deviance, k-fold cross validation and the area under the
receiver operator characteristic curve (AUC) (De'ath, 2007; Elith \&
Leathwick, 2009). GLM model performance was evaluated with AUC and
adjusted D\textsuperscript{2} (a measure of the difference between null
deviance and model deviance adjusted for degrees of freedom) and Akaike
information criterion (Guisan et al., 2002; Franklin, 2010). Maxent
models were evaluated by comparative training and test AUC, and omission
on test and training samples against random prediction
(specificity-sensitivity curves) (Hijmans, 2012; Elith \& Leathwick,
2017).

For predictions of current habitat suitability, maps showing the
predicted probabilities of presence or absence are presented for the
best model with soil type and without soil type. Plots of the partial
response of \emph{C. halei} to each of the environmental variables and
the proportional contribution of each variable to model performance are
also presented.

\textbf{Climate change modeling}

Climate change scenarios were projected using the best predictive model
for current habitat suitability, for the periods 2009-2049 and
2009-2069. Current predicted suitable habitat was subtracted from
composite binary presence/absence maps using both general circulation
models (GCM's), with range differences counted as contractions if a
current presence was projected as a future absence, refuge if current
presences remained presences and expansion if current unsuitable habitat
was projected as suitable in the future (Elith et al., 2010; Hatten et
al., 2016; Albuquerque et al., 2018). Box plots showing the two most
significant environmental predictors and their influence on range
changes are presented. The proportion of expansion or contraction of
future ranges relative to current habitat suitability were calculated
and are shown. Prediction maps are also presented with visualizations of
projected expansion and contraction.

RESULTS

\textbf{Survey results}

A total of 1227 records were recorded in the field, with accompanying
soil type. After separating occurrence points by a minimum distance of 1
km and removing duplicate records, the occurrence and randomly generated
pseudoabsence data set used in modeling consisted of 44 presences and
207 pseudoabsences.

\textbf{Variable selection}

The variables that were below the 0.5 correlation threshold in the
cluster analysis but most strongly correlated to the occurrence
of\emph{Cochemiea halei} in the biserial correlation analysis were
annual temperature range, mean temperature of the warmest quarter,
precipitation of the warmest quarter and precipitation of the coldest
quarter. These variables, along with soil type, were used in the species
distribution models. Annual temperature range differs on the islands
versus the peninsula, with island temperature ranges of approximately
20\selectlanguage{english}\textsuperscript{*} C and the peninsular range
10\selectlanguage{english}\textsuperscript{*}wider at 30\selectlanguage{english}\textsuperscript{*} C. Mean temperature
of the warmest quarter ranges from 24\selectlanguage{english}\textsuperscript{*} C in the
northwestern region of Isla Magdalena to approximately
30\selectlanguage{english}\textsuperscript{*} C at the lowest elevation in the center of Isla
Margarita and on the peninsula. Precipitation of the warmest quarter
ranges from 20--40 mm on the islands, except for the highest elevations
on Isla Margarita at 70 mm. The majority of peninsular precipitation is
also at 70 mm. Precipitation of the coldest quarter is from 20--30 mm on
the islands, to slightly over 40 mm on the peninsula. (Fig. 2)

\textbf{Modeling Results}

The three modeling methods of generalized linear models, boosted
regression trees and maximum entropy all generated fair to excellent
results, as measured by AUC values, with values ranging from 0.80--0.88.
Boosted regression trees (BRT) produced the best models, as measured by
AUC values, 10-fold cross validation, model deviance versus null
deviance and relevance of information regarding predictors and their
contributions to the models. The model results presented below are
derived from BRT analysis. The first model presented here is a model
that included soil type, which was the best performing BRT model. The
map of predicted suitable habitat shows a low probability of
colonization of the peninsula, especially to the east of localized,
immediately adjacent regions on the western edge of Bahia Magdalena. The
highest predicted probabilities on the peninsula are near the town of
Puerto San Carlos, where the only known peninsular population occurs.
(Fig. 3).

Annual temperature range, a simple subtraction of the annual high
temperature minus the annual low temperature, has the highest marginal
response, which shows a sudden drop in the suitability of habitat
for\emph{Cochemiea halei} under an annual temperature range greater than
approximately 21.5\selectlanguage{english}\textsuperscript{*} C. The optimal mean temperature of
the warmest quarter is ranges from 24\selectlanguage{english}\textsuperscript{*} C to
26\selectlanguage{english}\textsuperscript{*} C, with an increased contribution to occurrence at
26\selectlanguage{english}\textsuperscript{*} C, but then a sharp drop off , with temperatures
above approximate 26.5\selectlanguage{english}\textsuperscript{*} C negatively correlated to
occurrence. Precipitation of the warmest quarter is positively
correlated with occurrence below 30 mm, but negatively correlated above
30 mm. Precipitation of the coldest quarter shows approximately the same
reponse of precipitation of the warmest quarter. (Fig. 4).

The percent relative contributions for each variable to the predictive
ability of the model described above show that the most significant
predictor is annual temperature range, accounting for nearly 66\% of
model performance. Thermal energy in general is a strong predictor of
suitable habitat, with the top two predictors accounting for 78.3\% of
model performance. (Fig.5).

In order to gauge the impact of soil type on the habitat suitability
of\emph{C. halei} , a model was created with the same predictor
variables as the above model, but without soil type. The habitat
suitability map without soil type shows a higher probability of
occurrence on the peninsula, where the species does not occur in any
large populations and where ultramafic soils do not occur. A higher
suitability is also predicted within the islands themselves overall,
with a higher saturation in general of suitable habitat. Suitable
habitat is also predicted on some pure sand features, such as the sand
that connects Isla Magdalena with Cabo San Lazaro. There is also a
higher predicted probability on basalt and non-ultramafic soil types on
the islands. The low, sandy trough in the middle of Isla Margarita,
however, remains an area of low suitability, as does the peninsular
region to the east of the small footholds predicted for \emph{C. halei}
. The 10-fold cross-validated AUC of this model was .85, slightly lower
than the model with soil type. (Fig. 6)

\textbf{The effects of climate change on the range of \emph{Cochemiea
halei}}

For all projections, loss of between 21\%--53\% of suitable habitat is
predicted for the species. In the case of the lower representative
concentration pathways (RCPs), range contraction is partially offset by
expansion into previously unsuitable habitat. As the climate change
scenarios increase in RCP, especially over the longer time period to
2070, expansion is reduced significantly. (Table 1) The range maps
showing projected future areas of contraction and expansion for \emph{C.
halei} indicate the greatest potential loss of habitat is on Isla
Margarita, with regions on that island accounting for 40\% to 65\% of
the total contraction. Only two scenarios predict expansion to the
peninsula under the effects of climate change, and in both cases, the
predicted new suitable habitat is an isolated patch. (Figs. 7 and 8).

Boxplots of the effect of the two most important predictors, annual
temperature range and mean temperature of the warmest quarter, on future
habitat suitability indicate significant changes for all climate change
projections to 2070. The greater variability of annual temperature range
for range contraction areas is consistent with the species having more
suitable habitat within a narrower temperature range. The significantly
higher mean temperatures of the warmest quarter for all projected future
areas also contribute to habitat loss. Predicted areas of expansion also
feature higher temperatures, which is a result of temperatures across
the study site increasing due to climate change. (Fig. 9).

DISCUSSION

We investigated the effects of environmental and soil variables on the
distribution of \emph{Cochemiea halei} , as well as the possible impacts
of climate change, using species distribution models. Our results show
significant factors affecting the current distribution of the species
and potential threats to the persistence of the species under climate
change as a result of significant range contraction.

The island endemism of \emph{Cochemiea halei} is strongly correlated
with both soil and climate effects. The island archipelago in Bahia
Magdalena, the primary suitable habitat for \emph{C. halei} , has
significantly different soil and climate from the nearby peninsula.
These contrasting conditions are similar in other island habitats near
coastal areas along the Pacific Ocean, where conditions are
significantly different even a short distance inland (Reimann \&
Ezcurra, 2005; Bizzarro, 2008; Ratay, Vanderplank, \& Wilder, 2014).

\emph{Cochemiea halei} occupies a narrow range of temperature and
precipitation correlates. The moderating temperature effects of the
California current system (Hickey, 1979; Huyer, 1983; Bakun, 1990;
Robinson et al. 2007) are important to habitat suitability for the
species. The two most influential climate variables in the best model
were annual temperature range and average temperature of the warmest
quarter, accounting for approximately 73\% of the model's predictive
power. Both of these variables show significantly lower values on the
islands than on the peninsula, patterns typical of coastal areas
moderated by the upwelling of the California current system, especially
in summer (Bakun, 1990). The temperature range on the islands is
approximately 10\selectlanguage{english}\textsuperscript{*} C narrower than on the peninsula,
and the mean temperature of the warmest quarter is 4\selectlanguage{english}\textsuperscript{*}C
cooler. The bi-seasonal precipitation patterns of the Sonoran Desert
region (Shreve \& Wiggins, 1964) are represented by the influence on
model performance of the precipitation of both the warmest and coldest
quarters. However, precipitation is lower on the islands than inland,
during both seasons, with the most significant differences occurring
during the warmest quarter. These localized effects have been shown to
drive endemism (Hijmans \& Graham 2006; Snyder, Sloan, Diffenbaugh, \&
Bell, 2003; Gogol-Prokurat, 2011; Humphreys et al., 2019). \emph{C.
halei} is an example of a species that has a localized, well-defined
climate response, with the highest probability of suitable habitat
predicted to be within a relatively narrow band of thermal and
precipitation parameters.

In addition to strong climate influences on the current distribution
of\emph{Cochemiea halei} , soil type plays an important role. Narrowly
restricted endemic plant species, including cacti, have been shown to be
strongly dependent on soil types for habitat suitability (Kruckberg,
1951; Kruckberg \& Rabinowitz, 1985; Harrison et al., 2006). Several
studies of plant distributions have determined the importance of
ultramafic soils in particular as a driver of plant endemism (e.g.,
Kruckberg, Kazakou et al., 2008; Botha \& Slomka, 2017). While \emph{C.
halei} does not appear to be an obligate endemic to ultramafic soils,
the species is more likely to occur on those soil types, with 60\% of
occurrences on ultramafic soil. This is similar to other species in the
Cactaceae that occur on ultramafic soils, in particular on the islands
of Cuba and Puerto Rico (Reyes-Fornet, Fornet-Hernandez, \& Martinez
Ondaro, 2019). Obligate and facultative adaptations to ultramafic soils
have been shown to provide a competitive advantage (Brady et al., 2005;
Anacker et al., 2011; Harrison et al., 2006; Pollard, Reeves, \& Baker,
2014). The model with soil type had stronger predictive performance, and
indicated a more fragmented, lower habitat suitability for areas in our
surveys where population density was low, suggesting that ultramafic
soils are an important constraint on the distribution of the species.

\emph{Cochemiea halei} 's observed establishment on virtually
unweathered ultramafic rock and exposed gravel, in addition to lower
precipitation on the islands than on the peninsula, suggest that the
species is adapted to evaporation of soil moisture and drier conditions,
a common characteristic of cacti distributed in rocky environments
(Gibson \& Nobel 1986). In summary, \emph{C. halei} favors cooler, drier
habitat, on ultramafic rock and soil, with a moderated annual
temperature range, a suite of abiotic predictors that characterize the
island habitat in contrast to the nearby peninsula.

Consequently, \emph{Cochemiea halei} is not likely to migrate to the
peninsula except for small foothold regions along the peninsular shore.
This characterizes \emph{C. halei} as a ``stranded'' endemic, making its
persistence more vulnerable to changes in climate (Crawford \& Stuessy,
1997; Cowie \& Holland, 2006; Stuessy, Takayama, Lopez-Sepulveda, \&
Crawford, 2014). The only known peninsular population of \emph{C.
halei}consists of approximately six individuals limited to a patch of
sand measuring 150 m\textsuperscript{2}. At that site, there is no sign
of dispersal in the surrounding area, in spite of the plants being
large, seed bearing and apparently well-established (Gorelick, 2007).

The suitable habitat for \emph{Cochemiea halei} is a patchwork of sites
even within its narrow range on the islands. Major geographical
distinctions within the islands that are illustrated on the prediction
map from the model with the best predictive ability (Fig. 4) include two
distinct regions on Isla Margarita, zones of less suitable habitat on
Isla Magdalena, and a narrow zone of suitability at Cabo San Lazaro,
with few areas of on-the-ground connectivity between suitable habitats.
Endemic plant species often occur in fragmented habitat with
geographical barriers and low connectivity between sites (Rabinowitz,
1981; Kotliar \& Wiens, 1990). As a narrowly restricted endemic,
essentially stranded on the islands, the species is at increased risk
for stochastic environmental, demographic and genetic setbacks
(Ellstrand \& Ellam, 1993; Lande, 1993; Menges, 1992; Matthies, Brauer,
Maibom, \& Tscharntke, 2004; Melbourne \& Hastings, 2008; Mubayi, Kribs,
Arunachalam, \& Castillo-Chavez, 2019). Even without the impacts of
climate change exposure, the species appears to be at elevated risk for
local extinction events, population bottlenecks and increased
fragmentation.

\textbf{Climate change}

Future climate change scenarios indicate a contraction of\emph{Cochemiea
halei} 's range of 21\% to 53\%, depending on the severity of climate
change and the length of time the species is exposed to climate change
effects (Table 1). The range contraction reduces suitable habitat on the
islands, and the species is unlikely to expand to the peninsula within
the climate change conditions and time periods projected here. The
unique adaptations of narrowly distributed endemic plant species such as
\emph{C. halei} also make them vulnerable to changing climate
conditions, as those adaptations are often in response to significantly
different local climates or soil types that are unsuitable for related
species (Damschen, Harrison, Ackerly, Fernandez-Going, \& Anacker,
2012).

Specifically, each of the climate change scenarios projected in this
study indicates a widening of the annual temperature range on the
islands, which significantly increases the range contraction and limits
expansion. The areas of predicted contraction under all scenarios are
lower elevation, mostly bayside, leeward flats (Figs. 7 and 8). The
areas of expansion are mostly into the higher elevation ridges,
especially on Isla Margarita. But the opportunity to expand into these
higher elevation locales is greatly reduced as climate change becomes
more severe, or persists for a longer time period.

Projections under all climate change scenarios are for a higher mean
temperature of the warmest quarter, ranging from approximately
3\selectlanguage{english}\textsuperscript{*} C to as high as 6\selectlanguage{english}\textsuperscript{*} C.
Precipitation of the warmest and coldest quarters is projected to
decrease by from 10 mm to 15 mm for regions of predicted range
contraction. The wider thermal span and the warmer mean temperature from
July--September, along with reduced precipitation, are combined factors
that contribute to range contraction, driven by hotter, drier climate.
Predicted range contractions are consistent with \emph{Cochemiea halei}
's narrow adaptation to a distinct island climate.

For the first time, temperature and precipitation correlates are
identified that drive the fragmented, highly restricted distribution of
an island endemic, vulnerable cactus. We used multiple modeling methods
to determine the correlations between topographical and climate
variables and the habitat suitability of \emph{Cochemiea halei} , a
little-studied, island isolated cactus. Our results support the
following conclusions: (1) both the moderating effects of Pacific
coastal island climate and ultramafic soils unique to the islands
strongly determine suitable habitat, which is fragmented, (2) the
species is unlikely to disperse to the peninsula, (3) the species has a
facultative but not obligate relationship with ultramafic soils, (4)
climate change in all scenarios is likely to contract the range of the
species, as a result of greater variability in annual temperature range,
higher mean temperatures in the summer and reduced precipitation. Our
findings indicate that this narrowly restricted endemic cactus is at
increased risk of extinction, and populations should be carefully
monitored over at least the next 50 years.

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\textbf{Data Accessibility Statement} : The data that support the
findings in this paper, including locality data and raster files of the
climate variables used in modeling, are available at the Dryad data
repository.

\textbf{Tables} :

\textbf{Table 1. Percent expansion, contraction and net habitat loss
of\emph{Cochemiea halei} under four representative concentration pathway
(RCP) climate change scenarios and two time periods.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Time Period} & \textbf{RCP} & \textbf{expansion} &
\textbf{contraction} & \textbf{net habitat loss}\tabularnewline
\midrule
\endhead
\textbf{2050} & 2.6 & 19\% & -46\% & -27\%\tabularnewline
& 4.5 & 10\% & -48\% & -38\%\tabularnewline
& 6.0 & 14\% & -47\% & -33\%\tabularnewline
& 8.5 & 25\% & \textbf{-}52\% & -27\%\tabularnewline
\textbf{2070} & 2.6 & 29\% & \textbf{-}50\% &
\textbf{-}21\%\tabularnewline
& 4.5 & 5\% & \textbf{-}50\% & -45\%\tabularnewline
& 6.0 & 6\% & \textbf{-}50\% & -44\%\tabularnewline
& 8.5 & 2\% & -55\% & -53\%\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures:}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\selectlanguage{ngerman}\textbf{Fig. 1: The Bahía Magdalena region and islands, the known area
of distribution of \emph{Cochemiea halei}} . The islands and named land
masses in Bahía Magdalena: (A): Cabo San Lazaro, the most northwesterly
land mass of the islands. (B): The main land mass of Isla Magdalena.
(C): Isla Margarita. Map created using ArcGIS® software by Esri. ArcGIS®
and ArcMap\selectlanguage{english} are the intellectual property of Esri and are used herein
under license. Copyright (c) Esri. All rights reserved.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2: Maps of the environmental variables used in model building. (a):
annual temperature range; the difference between the maximum temperature
of the warmest month (September) and minimum temperature of the coldest
month (January). (b): mean temperature of the warmest quarter of the
year, July-September. (c): precipitation of the warmest quarter of the
year, July--September. (d): precipitation of the coldest quarter of the
year, December--February. (e): soil type; light green areas are
ultramafic soils and tan are all other soil types, including basalt and
the sand of the Magdalena Plains on the peninsula. Temperature variables
are in degrees C, and precipitation in mm.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3: Predictions of suitable habitat for \emph{Cochemiea halei} . The
map is of predictions of habitat suitability, on a scale of zero
(transparent) to 1 (dark green). The model predictions derive from a BRT
method, using WorldClim V. 2.0 data at 30 arcsec resolution. 44
presences and 207 pseudoabsences were used. The following variables were
used: annual temperature range, the mean temperature of the warmest
quarter (July-September), precipitation of the warmest quarter
(July-September), precipitation of the coldest quarter
(December-February), and soil type. The model fitted 4950 trees, with a
10-fold cross-validated AUC of .88. The parameters used for the boosted
regression tree analysis were a tree complexity of 2, a learning rate of
.0007, bag fraction of .7 and a step size of 25.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 4: Partial response plots of climate variables used in species
distribution modeling for \emph{Cochemiea halei} . The plots show the
marginal response of \emph{C. halei} to each variable. The variables are
(a): annual temperature range, ``Trng'', (b): average temperature of the
warmest quarter, ``Awarm'', (c): precipitation of the warmest quarter,
``Pwarm'', and (d): precipitation of the coldest quarter. The \emph{y}
-axis for each plot is on a logit scale, showing the relative impact of
values of the variable on the probability of occurrence. The \emph{x}
-axis for the temperature variables, (a) and (b), is in degrees C.
Precipitation variables, (c) and (d), are in mm. The \emph{x} -axis is
marked with a decile rug plot.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Fig. 5: Percent contributions of predictors to model
performance.} Percent influence: annual temperature range, 65.6\%;
average temperature of the warmest quarter, 12.7\%; soil type, 7.7\%,
precipitation of the coldest quarter (December-February), 7.1\%;
precipitation of the warmest quarter (July-September), 6.7\%.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Fig. 6: Comparison of predicted suitable habitat with soil type
as a predictor and without soil type as a predictor.}(a). the prediction
map resulting from the best BRT model with soil type, the same results
shown in Fig. 4. (b). the map from the best BRT without soil type.
Variables are identical in both models except for omission of soil type
in model B: annual temperature range, the mean temperature of the
warmest quarter (July-September), precipitation of the warmest quarter
(July-September), and precipitation of the coldest quarter
(December-February). The best model without soil type fitted 2270 trees,
with a 10-fold cross-validated AUC of .85. The parameters used for the
boosted regression tree analysis were a tree complexity of 1, a learning
rate of .0007, bag fraction of .7 and a step size of 15.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\par\null

Fig. 7: Predictions of range expansion, refuge, and contraction
for\emph{Cochemiea halei} , to 2050. Light green areas represent
projected areas of range expansion (from 2 to 3 on the scale), red areas
represent projected areas of range contraction (from 0 to -1 on the
scale). Increasing representative concentration pathways (RCP) are
indicated at the top of each map; increased RCP corresponds to increased
severity of climate change exposure. (a): RCP 2.6, (b): RCP 4.5, (c):
RCP 6.0, and (d): RCP 8.5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\par\null

Fig. 8: Predictions of range expansion, contraction and refuge
of\emph{Cochemiea halei} , to 2070. Predicted range changes for the four
representative concentration pathways are shown for climate scenarios
projected to 2070. Light green areas represent projected areas of range
expansion (from 2 to 3 on the scale), red areas represent projected
areas of range contraction (from 0 to -1 on the scale). Increasing
representative concentration pathways (RCP) are indicated at the top of
each map; increased RCP corresponds to increased severity of climate
change exposure. (a): RCP 2.6, (b): RCP 4.5, (c): RCP 6.0, and (d): RCP
8.5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Fig 9: Influence of the two strongest climate predictors on
habitat contraction, refuge, and expansion, for \emph{Cochemiea halei},
for four representative concentration pathway scenarios, projected to
2050 and 2070.} Each box plot shows the range,
1\textsuperscript{st}quartile, median and 3\textsuperscript{rd} quartile
of annual temperature ranges and average temperature of the warmest
quarter for each type of predicted future habitat prediction:
contraction, refuge, expansion and the present habitat. The \emph{y}
-axes are degrees C. All data is from the projections to 2070.

\selectlanguage{english}
\FloatBarrier
\end{document}

