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Journal of Biogeography. 2020;00:1–15. wileyonlinelibrary.com/journal/jbi
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1© 2020 John Wiley & Sons Ltd
Received: 8 August 2019
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Revised: 2 January 2020
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Accepted: 10 Februar y 2020
DOI : 10.1111/j bi.1 38 41
RESEARCH PAPER
Latitudinal patterns of species richness and range size of ferns
along elevational gradients at the transition from tropics to
subtropics
Adriana C. Hernández-Rojas1 | Jürgen Kluge1 | Thorsten Krömer2 |
César Carvajal-Hernández3 | Libertad Silva-Mijangos4 | Georg Miehe1 |
Marcus Lehnert5 | Anna Weigand6 | Michael Kessler6
1Faculty of Geography, Philip ps University
Marbur g, Marburg, G ermany
2Centro de Investigaciones Tropicales
(CITRO), Universidad Veracr uzana , Xalapa,
México
3Instit uto de Investigaciones B iológicas,
Universidad Vera cruzana, Xalapa, México
4Universidad de Ciencias y Artes de Chiapas,
sede Map astepec, Map astepec, Méx ico
5Martin-Luther Universität Halle-
Wittenberg , Halle, Germany
6Systemat ic and Evolutionary Bot any,
University of Zurich, Zürich, Switzerland
Correspondence
Hernán dez-Rojas Adriana, Facult y of
Geography, Philip ps University Marbur g,
Marbur g, Ger many.
Emails: adric.rojas@gmail.com, adriana.
hernandezrojas@geo.uni-marburg.de
Funding information
Deutsche Forschungsgemeinschaft, Grant/
Award Number: KL2183/8-1 and LE-1826-5;
Consejo Nacional de Cien cia y Tecnología,
Grant /Award Number: 57177537;
Deutscher Akademischer Austauschdienst,
Grant /Award Number: 57177537; Secretaría
de Educación Pública, Grant/Award Number:
“Beca s Complemento de Apoyo al Posgrado"
Handling Editor: Werner Ulrich
Abstract
Aim: To assess the range size patterns of ferns and lycophytes along elevational gra-
dients at different latitudes in an ecographical transition zone and search for predic-
tors of range size from a set of environmental factors.
Location: Mexico, from 15° to 23° N.
Tax o n: Ferns and lycophytes.
Methods: All terrestrial and epiphytic species were recorded in 658 plots of 400 m2
along eight elevational gradients. To test whether the range size within assemblages in-
creases with elevation and latitude, we calculated the latitudinal range using the north-
ern and southern limits of each species and averaged the latitudinal range of all species
within assemblages weighted by their abundances. We related climatic factors and the
changes with latitude and elevation with range size using linear mixed-effects models.
Results: Species richness per plot increased with elevation up to about 1,500–
2,000 m, with strong differences in overall species richness between transects and
a reduction with increasing latitude. The mean weighted range size of species within
assemblages declined with elevation, and increased with latitude, as predicted by
theory. However, we also found marked differences between the Atlantic and Pacific
slopes of Mexico, as well as low range size in humid regions. The best models de-
scribed about 76%–80% of the variability in range size and included the seasonality
in both temperature and precipitation, and annual cloud cover.
Main conclusion: Latitudinal and elevational patterns of range size in fern assemblages
are driven by an interplay of factors favouring wide-ranging species (higher latitudes with
increasing temperature seasonality; dryer habitat conditions) and those favouring spe-
cies with restricted ranges (higher elevations; humid habitat conditions), with additional
variation introduced by the specific conditions of individual mountain ranges. Climatically
stable, humid habitats apparently provide favourable conditions for small-ranged fern
species, and should accordingly be given high priority in regional conservation planning.
KEYWORDS
distribution, diversit y, elevation, endemism, latitude, pteridophytes, Rapopor t's rule
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ADRIANA et Al .
1 | INTRODUCTION
One of the most striking patterns in nature is the enormous variation
of range sizes of species, ranging from species which occur only in a
few square meters to others that are found across the entire globe
(Brown, Stevens, & Kaufman, 1996; Gaston, 1998). This variation is
not random, but shows distinct patterns related to environmental
and geographical conditions as well as the evolutionary history and
ecological requirement s of the t axa (Kreft, Jetz, Mutke, & Barthlott,
2010; Lomolino, Riddle, Brown, & Brown, 2006; Smith, 1993).
Accordingly, a number of ecogeographical rules have been devel-
oped to capture these relationships.
One of these rules is Rapoport 's rule (RR), which proposes that the
latitudinal range size of species is greater at higher latitudes, and that
tropical species tend to have smaller ranges allowing more species to
coexist in tropical versus temperate regions (Stevens, 1989). Originally
conceived for latitudinal gradients, the idea that range sizes may be
determined by climatic seasonality was later extended to elevational
gradients as well (Stevens, 1992, climatic variability hypothesis). While
these patterns have been documented for a wide range of taxa in
many regions (Addo-Bediako, Chown, & Gaston, 2000: insects; Ribas
& Schoereder, 2006: many groups; Morin & Lechowicz, 2011: trees;
Pintor, Schwarzkopf, & Krockenberger, 2015: lizards; Tomašových
et al., 2016: birds and ma rine bi valve s), there are also a good number of
stu dies, main ly along elevational grad ients in animal s but also in plants,
that do not corroborate the rule or even reporting a reverse pattern
or mixed results suggesting that it varies between taxa and continents
(Bhattarai & Veetas, 20 06; Pintor et al., 2015; Ribas & Schoereder,
2006; Rohde & Heap, 1996; Rohde, Heap, & Heap, 1993; Ruggiero,
1994; Zhou et al., 2019). Support for the rule is also scarce in the trop-
ics (Blackburn & Gaston, 1996; Rhode, 1996).
Even if a pat tern of range size distribution accords to RR, there are
a number of complications in understanding the underlying processes.
The classic assumption is that temperature conditions are more sea-
sonal at higher latitudes (Stevens, 1989, 1992). When species adapt to
these conditions, they widen their niche breadth (tolerance breadth;
Slatyer, Hirst, & Sexton, 2013; Stevens, 1992) and thus attain wider
geographical ranges. However, the spatial distribution of climatic con-
ditions may vary with latitude or elevation so that even if species have
constant niche breadths, this will result in different range size patterns.
Furthermore, the classical interpretation of RR focusses only on tem-
perature seasonality, even though seasonal variations in humidity may
be equally important for explaining the range size distributions (Gaston
& Chown, 1999; Pintor et al., 2015). Especially in the tropics, where
there is little seasonal variation in temperatures, variations in precip-
itation patterns may play an important role. Finally, latitudinal and el-
evational climatic gradients, while sharing many similarities, also have
crucial differences. For instance, elevational ranges (amplitudes) of
species typically increase with elevation (Janzen, 1967; Kessler, 2001;
McCain, 2009; Sklenář & Jørgensen, 1999; Stevens, 1989, 1992), which
would support RR. However, geographical range sizes (total area) on
average decrease with an increase in elevation (Kessler, 20 00, 2002,
2010; Kessler & Kluge, 2008; Steinbauer et al., 2016). One explanation
is that rugged mountainous terrain habitats with patchy distributions
(‘sky islands’) support fragmented species populations that are more
prone to speciation than species inhabiting ex tensive habitats without
geographical barriers (Antonelli, Nylander, Persson, & Sanmartín, 2009;
Kessler, 2001; Kruckeberg & Rabinovitz, 1985; McCormack, Huang,
Knowles, Gillespie, & Clague, 2009). Besides, past climatic fluctuations
determining the connectivity between sky islands may be an important
driver of diver sifi cation by le ad in g to succ es si ve cyc le s of population ex-
pansion and fragmentation (‘flickering connectivity systems’; Flantua &
Hooghiemstra, 2018; Flantua, O'dea, Onstein, Giraldo, & Hooghiemstra,
2019). Clear ly, under st anding the spatial variation of species range size s
along latitudinal or elevational gradients requires more detailed under-
standing than suggested by the conceptually simply RR.
Thus, putting species range sizes into a broader context, range
sizes are influenced by a wide range of geographical and evolu-
tionary factors. For instance, species with restricted range sizes
are often found in localized habitats, either geographically such as
on oceanic islands or environmentally, such as on specialized geo-
logical substrates (Carlquist, 1974; Kier et al., 2009; Kruckeberg &
Rabinovitz, 1985; Major, 1988). In addition, the geological and evolu-
tionary history of a region also plays an impor tant role in determin-
ing current species distributions (Brown et al., 1996; Lomolino et al.,
2006). For example, Mexico is exceptionally rich in endemic species
in numerous taxonomic groups, which is related to its high geologi-
cal and environmental heterogeneity (Brummitt, Aletrari, Syfert, &
Mulligan, 2016; Myers, Mittermeier, Mittermeier, Fonseca, & Kent,
2000; Rzedowski, 1962, 2006; Tryon, 1972). In par ticular, dry forest
and desert areas are characterized by high endemism and super-en-
demism (high levels of neo- and paleo-endemics; Sosa & De Nova,
2012; Sosa, De-Nova, & Vásquez-Cruz, 2018). Accordingly, the arid
Pacific side of the countr y is a centre of endemism for many groups
of plants and animals, presumably due to the long-term environmen-
tal stability of the region (Lott & Atkinson, 2006; Rzedowski, 2006).
To a lesser degree, endemism has also been associated with humid
forests, which in Mexico are distributed as habitat islands forming an
intracontinental habitat archipelago (Llorente-Bousquets, Escalante-
Pliego, Dar win, & Welden, 1992).
Determining the causes of the geographical distribution of range
sizes is impor tant in a conser vational context because a small range
size is one of the main predictors of extinction risk of species (Purvis,
Gittleman, Cowlishaw, & Mace, 2000). In this sense, the current avail-
ability of large databases of species distributions and occurrence re-
cords offers outstanding opportunities to document and understand
range size patterns and other large-scale patterns of biodiversit y
across geographical and environmental gradients. Nevertheless, many
biases have been detected in large data banks, such as gaps in the
available information, uncertainties in species identification/taxon-
omy and distributional information, errors in occurrence coordinates,
and incomplete species richness for poorly explored regions (Meyer,
2016; Meyer, Weigelt, & Kreft, 2016; Qian et al., 2018). Yet, the im-
provement of these databases in the last years, and their careful and
critical use, depending on the study objectives and region, make them
an important tool in macroecology and biogeography.
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ADRIA NA et Al.
Ferns and lycophytes (hereaf ter jointly referred as ‘ferns’ for sim-
plicity) are taxonomically well-studied and well-suited groups to in-
vestigate biogeographical questions because of their spore dispersal
(wind-borne), which makes them largely independent from biotic dis-
persal agents (Barr ing ton, 1993), and thus links patterns of range sizes
and endemism mainly to abiotic factors. Additionally, ferns are a mod-
erately spe cies-rich group, still manageable to handle when seeking to
conduct a full census within a study area, but diverse enough to show
a wide range of range size patterns and to allow for quantitative anal-
yses. With more than 1,088 recorded species (J. D. Tejero-Díez, pers.
com., 2019), they are well represented in Mexico, which has one of
the best-documented fern floras in the world (Mickel & Smith, 20 04).
Generally speaking, ferns are physiologically more limited by drought
and low temperatures than angiosperms (Brodribb & McAdam, 2011;
Brodribb, McAdam, Jordan, & Field, 2009) so that their diversity de-
clines more steeply towards arid and cold climatic conditions (Kreft
et al., 2010). As a result, fern diversit y peaks in tropical montane cl oud
forests and declines towards lower and higher elevations and higher
latitudes (Kessler, Kluge, Hemp, & Ohlemüller, 2011; Khine, Kluge,
Kessler, Miehe, & Karger, 2019; Salazar et al., 2015).
Little is known about the distribution of range sizes in ferns. In
Costa Rica, Bolivia and Kenia (Kessler, 2001; Kluge & Kessler, 2006;
Zhou et al., 2019), fern ranges tend to decrease with elevation, but
the latitudinal patterns and the relationship to climatic factors remain
unexplored. Nevertheless, considering that fern diversity peaks in the
most humid habitats, and that such ver y wet habitats have a local-
ized and patchy dist ribution (Killeen, Douglas, Consiglio, Jørgensen , &
Mejia, 2007; Llorente-Bousquets et al., 1992; Sanginés-Franco et al.,
2015), it seems reasonable to expected that fern species adapted to
such conditions have similarly localized and patchy ranges.
In this study, we explored the patterns of latitudinal range size of
ferns along eight elevational gradients located at different latitudes in
the Mexican transition zone from the tropics (south of Mexico) to the
subtropic s (30 km south of th e Tropic Can cer Line), which is considered
a global biodiversity hotspot (Myers et al., 20 00) and a centre of fern
endemism (Brummitt et al., 2016). We asked whether mean range sizes
of fern assemblages var y with latitude and elevation, specifically hy-
pothesizing that mean range sizes increase with latitude as Rapoport's
rule proposes (H1) and decrease with elevation (H2). We further hy-
pothesized that mean range sizes increase with increasing environmen-
tal stress factors such as low temperature, precipitation, humidity and
high climatic seasonality (H3a). Conversely, we predict that mean range
sizes decrease with increasing humidity due to the water dependenc y
of the study group, related to the geographical fragmentation of envi-
ronmentally suitable areas for specialized ferns (H3b).
2 | MATERIALS AND METHODS
2.1 | Study area
The Mexican transition zone is the complex area where the
Neotropical and Nearctic biotas overlap, and in a strict sense
corresponds to the mountain highlands of Mexico, Guatemala, El
Salvador and Nicaragua (Halffter & Morrone, 2017). We here pre-
sent data from eight elevational gradients at a range of 0 to 3,500 m
elevation at 15–23° latitude N on both the Pacific and Atlantic
(Gulf of Mexico) sides of Mexico (Figure 1; Table S4). Three tran-
sects have been considered in previous studies: Los Tuxtlas (Acebey,
Krömer, & Kessler, 2017; Krömer, Acebey, Kluge, & Kessler, 2013),
Perote (Carvajal-Hernández & Krömer, 2015; Carvajal-Hernández,
Krömer, López-Acosta, Gómez-Díaz, & Kessler, 2017) and Oaxaca
(Hernández-Rojas et al., 2018). Los Tuxtlas including abundances
was not published before (‘Los Tuxtlas a’). Both transects from Los
Tuxtlas were combined for the majority of the analysis.
2.2 | Fern sampling
On eac h gra di en t, we sam pled th e fern as se mblages at re gular ele va -
tional inter vals of 100–300 m (every 500 m at Perote), depending on
accessibility. At each elevation, depending on the suitability of the
slope, 4–8 plots of 20 × 20 m (400 m2) were sampled with a consist-
ent, standardized methodology (Karger et al., 2014; Kessler & Bach,
1999). The plots were established in natural zonal forest, avoiding
special structural features like canopy gaps, ridges, ravines, riparian
areas, tree fall gaps, landslides and other disturbed areas whenever
possible, which all change microenvironmental conditions and have
sp e cia l fer n asse m blag e s. In ea c h plot , all fe rn sp e cie s and th eir ab u n-
dances were recorded for terrestrial (soil, rocks and dead wood) and
for epiphytic substrates. Species with long creeping rhizomes were
counted as patches. Epiphytes were sampled up to heights of 8 m
with trimming poles and recorded at greater heights using binocu-
lars, climbing lower parts of trees, and searching recently fallen trees
and branches within and adjacent to the plots (Gradstein, Nadkarni,
Krömer, Holz, & Nöske, 2003; Sarmento Cabral et al., 2015).
Samples of all fern species were collected and deposited in the
University Herbarium, University of California (UC) in Berkeley,
USA, herbarium XAL of the Instituto de Ecología, A. C. (Xalapa,
Mexico), MEXU of the Universidad Autónoma de México (Mexico
City, Mexico), CIB of the Instituto de Investigaciones Biológicas
(Universidad Veracruzana, Xalapa, Mexico), HEM of the Universidad
de Ciencias y Artes de Chiapas (Tuxtla Gutiérrez, Mexico) and UAMIZ
of the Universidad Autónoma Metropolitana-Iztapalapa (Mexico
City, Mexico). Collections were identified by A. R. Smith (UC), A.
Hernández-Rojas and C. Carvajal-Hernández. Taxonomy primarily
followed Mickel and Smith (20 04) and the current classification for
ferns and lycophytes established by the Pteridophyte Phylogeny
Group (PPG I, 2016). Species names and authors were checked on
the International Plant names Index (IPNI).
2.3 | Explanatory variables
Fe r ns ar e clo s e ly de p ende nt on cli mat ic varia b l es re l ate d to hu midi t y be-
cause their sexu al reproduc tion is lin ked to the presence of water (P age,
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ADRIANA et Al .
2002) and because of thei r poo r sto matal cont rol (Brodr ibb & McAdam ,
2011; Kessler, 2001). Because water stress is not only determined by
water input into a system (by precipitation or fog) but also by evapo-
transpiration which is related to high temperatures, we specifically in-
cluded energ y- and humidity-related variables as predictors of species
distribution and their range size. Besides temperature and precipitation
and their temporal variability, cloud cover is also a suitable predictor in
this context because clouds reduce solar radiation and provide extra
‘occult’ precipitation (Bruijnzeel & Veeneklaas, 1998; Hartmann, 1993).
Thus, we extracted the following climatic variables per plot from the
global climate database set CHELSA (Karger et al., 2017): Annual mean
temperature and precipitation (Bio1, Bio12), as well as temperature and
precipitation seasonality (Bio4, Bio15). From‘Ear thEnv’, we extracted
annual cloud cover and its seasonality (CloudA, CloudS; Wilson & Jetz,
2016). We checked for collinearity between the climatic variables using
the Variance Inflation Factor (VIF; Naimi, Hamm, Groen, Skidmore, &
Toxopeus, 2014). Variables with values > 6 were not used in the same
model (e.g. elevation and annual temperature), but all variables were in-
cluded in different models of the same analysis. We also included the
position in the country (Pacific and Atlantic side) as a fixed factor be-
cause the sides are known to have dif ferent biogeographical histories
and habitat connectivity, leading to markedly different pat terns of end-
emism for many groups of organisms (Rzedowski, 2006).
2.4 | Species ranges
We used the latitudinal range (range between the northern and
southern range limits) of each species as a simple gradual measure
of range size. To quantif y the latitudinal ranges of the species, we
used American species occurrences combined with our own records
for a total of 173.110 species records. Data were obtained from
the Biodiversity Information Facility (Gbif, www.gbif.org, accessed
August–September 2018) databank using the ‘rg bif’ package in r (R
Core Team, 2019). Coordinates of fossil records and specimens from
botanic al gardens or herbaria were excluded. To detect errors and
suspicious patterns (outliers) within the dataset, we mapped the co-
ordinates and checked the latitudinal range of each species using
‘maptool s’ (Bivand & Lewin-Koh, 2019). Range sizes were checked
against TROPICOS, Catalogue of Life (Hassler 2020), Mickel and
Smith (2004), Labiak and Prado (2007), Vasco, Moran, and Rouhan
(2009), Larsen, Martínez, and Ponce (2010), Vasco (2011), Labiak
(2011), Lehner t (2013), Smith and Tejero-Díez (2014), Lóriga, Vasco,
Regalado, Heinrichs, and Moran (2014), Arana, Larsen, and Ponce
(2016), Barbosa-Silva et al. (2016), Villaseñor (2016), Kessler and
Smith (2017), Ponce, Rio, Ebihara, and Dubuisson (2017) and Smith
et al. (2018), and suspicious and wrong observations were corrected
(e.g. coordinates in the sea).
FIGURE 1 Location of the eight study transect s and their fern species richness patterns at the transition from tropics to the subtropics in
Mexico between 15° and 23° N. Red points represent the mean latitude of all plots per site, indicated in every richness panel
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ADRIA NA et Al.
With these latitudinal ranges, we calculated the mean range size
of all species (excluding species varieties and species identified only
up to genus) in each individual plot as an index of range size within
the assemblage (plot). To account for different species abundances
within assemblages, we also calculated a 'weighted mean' including
the number of individuals of the species, thus down-weighting rare
spe cie s. The ai m of th is weighting was to reduce the infl uence of spe-
cies that do not belong to the core communities at a site: Because of
thei r sp ore dis per sal, ma ny fer n spe ci es ca n oc cas io nally or te mp ora r-
ily occur outside of their core ranges, and such sink populations can
strongly impact species richness patterns (Kessler, Hofmann, Krömer,
Cicuzza, & Kluge, 2011; Kessler, Salazar, Homeier, & Kluge, 2014).
2.5 | Statistical analysis
We used linear mixed-effec ts models (LMMs) to control for the non-
independence among data points in assessing changes in the species
ranges with elevation, latitude and in relation to climatic variables
(fixed effects) because these models allow for spatial autocorrelation
between neighbours (Crawley, 2007; Zuur et al., 20 09), and likelihood
ratio tests (LRT) or ‘deviance tests’ to compare between a null model
without the term of interest and the model including this term to de-
termine if one is a better fit to the data than the other (Luke, 2017;
Winter, 2019). For the model including climatic variables or many fixed
effects, we used the mixed function in the package ‘afex’ that per-
forms a full suite of likelihood ratio tests for all fixed effects in a model
and constructs the correspondent comparison model providing p val-
ues for all fixed effects in a model (Singmann, Bolker, Westfall, Aust,
& Ben-Shachar, 2019; Winter, 2019). All variables used in the models
were scaled.
We also tested the random structure of our models using the
restricted maximum likelihood (Winter, 2019) choosing the different
transects and groups of plots in the same elevation (Transect/Step)
as random effect s for the analysis of all tr anse ct s toge th er. The ana l-
ysis by individual transects was performed using ‘Step’ or group of
plots in the same elevation as a random effect to avoid overfit ting
the model with a complex random structure.
To evaluate the association between range size, latitude, eleva-
tion and the climatic variables, we calculated the Spearman correla-
tions. Bec ause climatic variables interact in complex ways in relation
to latitude, elevation and position in the country (Atlantic and Pacific
sides of Mexico, ‘Side’) and because our data were not perfec tly bal-
anced with regard to these factors (e.g. different elevational spans of
the transec ts), we additionally ran a model with these climatic vari-
ables against the residual of the model including latitude, elevation
and side.
For model selection, we used the dredge function in the r ‘mumin’
package (Barton, 2019). To decide whether such a simplified model
was an enhancement to the previous model, we c alculated the cAIC
(conditional Akaike Information Criterion; Saefken, Ruegamer, Kneib,
& Greven, 2018), with a lower cAIC indicating a better model. The
amount of variation explained by the fixed (marginal R2) and random
effects (conditional R2) of ea ch mo d e l wa s ca l cu l a t e d us i n g th e ‘M u M In’
package (Barton, 2019; Nakagawa & Schielzeth, 2013). Residuals of
models were checked (see example in Appendices). All analyses were
performed with the statistical platform r (R Core Team, 2019), using
the packages ‘usdm’ (Nai mi et al. , 20 14) , ‘lme4’ (Bates, Maechler, Bolker,
and Walker (2015), ‘afex’ (Singmann et al., 2019) ‘mumin’ (Barton,
2019), ‘caic4’ (Saefken & Rueganer, 2018), ‘vegan’ (Oksanen et al.,
2019) and ‘ggeffects’ (Lüdecke, 2018, to plot the models).
3 | RESULTS
In total, in the 658 plots along the eight study transects, we re-
corded 410 fern species and 8 varieties, representing about 40%
of the Mexican fern flora (Mickel & Smith, 2004; Villaseñor, 2016;
J. D. Tejero-Díez, 2019, pers. com., Table S3). Generally speak-
ing, species richness per plot increased with elevation up to about
1500–2000 m, but with strong differences in overall species rich-
ness between transects and a reduction with latitude (Figures 1 and
2). No fewer than 17.1% of the species were recorded in only one
plot, 26.2% in 2–5 plots, 22.5% in 6–15 plots and only 34.0% in 16 or
more plots. The most species-rich families were Polypodiaceae (97),
Dryopteridaceae (76), Pteridaceae (44) and Hymenophyllaceae (31).
Latitudinal range sizes of species ranged from 0.6° in Goniopteris
tuxtlensis, a localized endemic, to 138.3° in the widespread species
Cystopteris fragilis. Overall, mean latitudinal range size was 30.7°. The
family Dryopteridaceae presented the smallest mean range sizes
(19. 3 ° ± 17.7°SD, latitude), Polypodiaceae (24.7° ± 19.2°), Pteridaceae
interme diate ranges (38.5° ± 20.3 °) an d Hymenophyllaceae the larg-
est ones (45.1° ± 15.0°).
Mean latitudinal range sizes of species in an assemblage in-
creased with latitude (X2(1) = 7.71, p < .01) on both the Atlantic
and Pacific sides, and decreased with elevation on the Atlantic side
(X2(1) = 9.56, p < .01). Over all , Pa cif ic an d At lanti c sides diff ere d, pr e-
senting smaller range sizes on the Atlantic side (X2(1) = 11.5, p < .01,
Figure 3). Including a random intercept and random slope models
(Range size–Elevation) allowed us to see different tendencies be-
tween transects (Figure S5 in supplementary material).
The analysis of individual transects showed contrasting re-
sults with different climatic factors related to latitudinal range
size along each transect (Table 1; Figure 2). The same was true
when separating the data by side and by elevational group. When
separating sides, variables related to humidity were important for
the Pacific side, whereas the seasonal variability in temperature
and humidity were important on the Atlantic side. Incorporating
all transects and separating elevational groups, we found that
in the upper part of the mountains, temperature was crucial, in
the lowlands, precipitation and at intermediate elevations, the
seasonal variation of precipitation. Also, using these elevational
gro ups bu t separate d by sid es , we found that o n the Atla nt ic side,
precipitation seasonality was important at all elevations, whereas
on the Pacific side seasonalit y in b oth precipit ation and tempera-
ture was important.
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7
ADRIA NA et Al.
Th e us e of all tran sec t s toget her all owed us to fi nd gene ral pat tern s
and the explanatory power increased significantly in terms of the vari-
ance described by the fixed effect s when combining all transect s using
side (Pacific and Atlantic) as fixed effect in the models (Table 1).
Integrating all environmental variables in a global model (full
model) including all transects revealed that the most important
terms were side, precipitation seasonality annual cloud cover and
temperature seasonality (Figure 4b,c,f), this model explained 62%
of the variability in range size but even more (70%) when using
weighted values (Table 1 and Appendices). With this division, high
seasonality in temperature was related to larger range sizes on
both sides, whereas high precipitation seasonality was related to
small ranges on the Atlantic slope and large ranges on the Pacific
slope. A nnual clou d cover sh owed a negative relationship to r ange
size on the Pacific side but not on the Atlantic side (Figure 4c).
When the ef fect of latitude, elevation and side of the country
was controlled using the residuals of this model (Model less) against
the climatic variables still some climatic variables remained import-
ant (Table 2), showing that they have strong effect on range size,
mainly the seasonality.
In general, small values of range size were found at intermedi-
ate values of precipitation seasonality, low temperature seasonality
and a high annual cloud cover. The humid Atlantic side presented
a higher proportion of small range size species relative to the dry
Pacific side (Figure 4). All models were checked and no pattern was
left in the residuals.
4 | DISCUSSION
The main results of our study can be summarized in the follow-
ing five points. First, overall latitudinal range size increased with
increasing latitude. Second, range size decreased with elevation
on the Atlantic slope but not on the Pacific slope. Third, range size
decreased in areas with high humidity, low temperature seasonal-
ity and intermediate precipitation seasonality, as well as constant
cloud cover. Fourth, there was a strong difference in range size be-
tween the Pacific and Atlantic sides that was not captured by the
clim at ic fa cto rs, with ranges on the Pacifi c sid e being much broade r.
Fifth, we found great variation between individual transects.
Our results confirm the first hypothesis, that on average, lat-
itudinal species ranges become wider at higher latitudes, which
is in accordance with Rapoport's Rule (Stevens, 1989). This pat-
tern has been previously documented for algae (Santelices &
Marquet , 1998) and other plant (Stevens, 1992) and animal groups
(Stevens, 1996: marine fishes; Fleishman, Austin, & Weiss, 1998:
butter flies; Swaegers et al., 2014: dragonflies; Böhm et al., 2017:
snakes) mainly in the northern hemisphere, and while not funda-
mentally novel, it is confirmative for ferns and reflec ts the repre-
sentativeness of our data. Because temperature and precipitation
seasonality showed a linear trend with latitude, we can exclude
the possibility that this pattern was driven by a spatially unequal
distribution of climatic seasonality, which would result in differ-
ent range sizes despite equal climatic niche breadths (Gaston &
Chown, 1999; Tomašových, Jablonski, Berke, Krug, & Valentine,
2015). Rather, it seems likely that increasingly stressful and vari-
able climatic conditions require broader climatic tolerances of the
species, resulting in wider climatic niches and accordingly larger
ranges (Janzen, 1967; Stevens, 1989).
In contrast, our second hypothesis that range sizes of ferns
should decrease with elevation (Zhou et al., 2019) was supported
only on the Atlantic (Gulf of Mexico and Caribbean) side of Mexico,
whereas on the drier Pacific side we detected no elevational trend.
A decrease in range size with elevation has also been found in ferns
in Costa Rica (Kluge & Kessler, 2006) and Bolivia (Kessler, 2002),
as well as in other plants and animals (e.g. Gifford & Kozak, 2012;
Steinbauer et al., 2016), and is likely linked to topographic complex-
ity, leading to geographically fragmented species ranges which fos-
ter allopatric speciation (Antonelli et al., 2009; Kessler, 20 01). This
effect appears to be most pronounced in wet tropical climates (Kier
et al., 2009) or areas of favourable ocean currents that create refu-
gia for endemics (Harrison & Noss, 2017), as found on the Atlantic
slope. In addition, formation of endemic species might also be re-
lated to past climatic fluctuations that led to successive periods of
habitat connectivity and disruption (Flantua & Hooghiemstra, 2018),
although this remains to be tested for the Mexican mountains.
The lack of this pattern on the Pacific side is puzzling, but may
be related to its overall aridity, since we found that fern range
sizes increase with increasing aridity (Figure 4e,f). Interestingly, for
drought-adapted plant groups such as Bursera (Rzedowski, 2006),
Ipomoea (Lott & Atkinson, 20 06) or many ferns that prefer arid con-
ditions such as Anemia or cheilantoid ferns like Argyrochosma, Gaga,
Myriopteris and Notholaena (Mickel & Smith, 20 04), the Pacific slope
of Mexico is a well-known centre of endemism. The same is true for
insect groups like bees that thrive in arid environments (Bye, Lot,
Fa, & Conzalez-Montagut, 1993). It thus appears that in the case of
ferns on the Pac if ic slo pe of Me xi co , th e ex pec te d el ev at ional ef fe ct
on species range sizes is overridden by stressful climatic factors.
In this regard, we found overall that latitudinal range sizes of ferns
were smallest in areas of high precipitation and cloud cover. Ferns are
well known to have highest diversity in wet habitats (Hemp, 2001;
Hietz, 2010; Kessler, Kluge, et al., 2011; Kluge & Kessler, 2005), pre-
sumably as a result of their less efficient control of stomatal transpi-
ration as compared to angiosperms (Brodribb & McAdams, 2011;
Brodribb et al., 2009; Page, 2002). Accordingly, it is reasonable to
propose that wet habitats, which in Mexico are present mainly on
the Atlantic side (e.g. 4,000–7,000 mm/a at Los Tuxtlas; Gutiérrez-
García & Ricker, 2011 or La Chinantla; Meave, Rincón-Gutiérrez,
Ibarra-Manríquez, Gallardo-Hernández, & Romero-Romero, 2017) and
whose distribution decreases northwards, act as localized refuges for
FIGURE 2 Richness and range size patterns of ferns, and environmental factors along eight elevational transects in Mexico at 15°–23° N.
Transects are ordered by continental sides (Atlantic, Pacific) and latitude (from south to north within each side)
8
|
ADRIANA et Al .
many fern species that depend on such conditions. Because of the
localized distribution of the habitats, the species will accordingly have
localized ranges. In contrast, species capable of surviving in dryer,
more widespread habitats will have broader ranges. Species range
sizes decreased in areas with less seasonality of both precipitation and
cloud cover only on the Atlantic side. This may reflect the generally
more favourable conditions for ferns on this side (Figure 4).
In addition, we also found transect-specific patterns that are
not captured by the general relationships discussed so far. This
supports the idea that individual mountain ranges are unique de-
pending on their geology, topographical profiles and past climatic
fluctuations, resulting in individual ‘mountain fingerprints’ (Flantua
& Hooghiemstra, 2018). We refrain from discussing the individual
transect patterns in more detail pending replicated sampling in the
different mountain ranges to confirm the patterns, but point out
that there appear to be range-specific patterns that merit future
investigation.
Based on all of the above, we conclude that the distribution of
range size of Mexican ferns is driven by an interplay of factors favour-
ing wide-ranging species (higher latitudes with increasing temperature
seasonality; dr yer habitat conditions) an d those favouring spe cies with
restricted ranges (higher elevations; more humid habitat conditions),
with additional variation introduced by the specific conditions of the
individual mountain ranges. The interactions of these factors are com-
pl ex and ar e str iki ngl y di f fer ent bet ween th e At lan tic and Paci fic slop es
of Mexico so that under certain conditions, specific relationships may
be overshadowed by other relationships (Tejero-Díez, Torres-Díaz, &
Gual-Díaz, 2014). This shows that understanding the distribution of
species range sizes should not be simplified too much and that under-
standing the distribution of range sizes must take into consideration
a wide range of fac tors at various spatial scales. This is also relevant
for conservation action, in which range-restricted or endemic species
are frequently given priority due to their highe r extinction risks (Purvis
et al., 2000). Since climatic conditions are currently strongly changing,
understanding the underlying causal relationships rather than only
the current patterns of the distribution of range-restricted species is
crucial to making informed predictions about the future of many plant
species. Our study points to the overriding importance of climatically
humid and stable habitat islands for fern conservation while acknowl-
edging regional variation.
ACKNOWLEDGEMENTS
We thank Alan Smith fo r th e priceless support in th e species iden-
tification and comments to the manuscript, Daniel Tejero for the
comments and the information provided, Roger Guevara for ideas
FIGURE 3 Relationship between latitudinal range size of ferns,
latitude and elevation along eight elevational transects in Mexico
between 15° and 23° N (Spearman correlation). Pacific side, black
circles; Atlantic side, gray triangles. For weighted range sizes, only
the seven transects were included
Pacific R = 0.55, P = 0.001
Atlantic R = 0.32, P = 0.001
25
50
75
16 18 20 22
Latitude
Range size
(a)
Pacific R = 0.30, P < 0.001
Atlantic R = 0.14, P < 0.05
0
25
50
75
16 18 20 22
Latitude
Weighted range size
(b)
Pacific R = −0.06, P= 0.28
Atlantic R = −0.16, P < 0.01
25
50
75
0 1000 2000 3000
Elevation
Range size
(c)
Pacific R = −0.02, P = 0.65
Atlantic R = −0.47, P < 0.001
0
25
50
75
0 1000 2000 3000
Elevation
W
eighted range si
ze
(d)
|
9
ADRIA NA et Al.
to improve some analysis, authorities of the study localities for
granting research permits (CONANP corresponding to Reser vas
de la Biósfera El Triunfo and Sierra de Manantlán, SEMARNAT,
SEMAHN-Chiapas, SEDERMA-Nayarit, CUCSUR UDG, Instituto
Tecnológico de Ciudad Victoria, Tamaulipas and Pronatura Sur
A.C. and indigenous communities of Oaxaca), and the guides,
field assistants and people involved for valuable help: Abiligam
López, Abraham Esqueda, Aurelia Dumas, Benito Martinez,
TABLE 1 Likelihood ratio test results with p values for all fixed effects in the Linear mixed models (Coefficients), by transects, sides
(A = Atlantic and P = Pacific) and elevational groups, using Step or group of plots in the same elevation as a random factors. For all transects
together, the Transect/Step random factor structure was used. Similar values in the marginal (R2m) and conditional R2 (R2c) indicates that a
linear model is the adequate model with the same coefficients. Bio1, Bio4: annual mean temperature and its seasonality, Bio12, Bio15: annual
precipitation and its seasonality and CloudA, CloudS: annual cloud cover and its seasonality. Significance codes: ***p < .001, **p < .01, *p < .05
Transect Side Bio1 Bio4 B io1 2 Bio15 CloudA CloudS R2mR2c
Triunfo (16.5) 490.06*** 80.92*** −15.85*** 0.21 0.34
Manantlan (19.6) −113.45* −25.92*** 0.38 0.38
Nayarit (21.4) 80.88*** 1 87. 7 7 * 156.0 6** −2 19. 29* * 0.41 0. 59
Chiapa sN (17.1) −14 .9 4* * −1 3 .03* −7 3 . 8 5 * * 0.55 0.55
Oax ac a (17.5) 38.22*** −95.07*** 11.14* 0.64 0.83
Tux tla s a (18. 5) 72.28* −2 2 .82 ** −462.44** 0.34 0.34
Tuxtlas b(18.5) 3 8. 2 1* 99. 6 0 * 0.45 0 .45
Perote (19.4) 23.24* 46.97* 0.58 0.62
Cielo (23.1) −82 . 21* −2 42 .1 2* −83.17** 0.38 0.57
Pacific: all plots −15 . 4 8* −10.22*** −12.99*** 0.23 0.35
Atlantic: all plot s −3. 66* −5.49*** −7. 0 6 * * −8.09** 0.56 0.77
Atlantic per elevational groups
0 –70 0 −33.51*** −14.98*** −7. 0 8* −12.84*** −44.82*** 0.40 0.40
70 1–1 3 0 0 19. 66** 10.12* −11 . 35 * * 10.36* 0. 76 0.82
13 0 1–1 8 0 0 −23.4 8** −56.58* 0.86 0.86
18 0 1–1 4 0 0 −17. 8 8* −9.15 ** −1 0 . 56** −33 .4 6** − 40.44** 0 .71 0.71
24 01–3 500 73.30* −52.53*** 0.92 0.92
Pacific per elevational groups
0 –70 0 138.13* 920.32* 824.89* −15 2 . 6 4* * −222.10** 0.68 0.68
70 1–1 3 0 0 −188.60* −189.81* 0. 24 0.24
13 0 1–1 8 0 0 −1 5.60 * * −18 .95 * 0.30 0.34
18 0 1–1 4 0 0 66.37*** 179.57*** 63.85*** −119.05*** 0.45 0.58
24 01–3 500 0.46 0.46
By elevational groups (no division between Atlantic and Pacif ic)
0 –70 0 −11 . 0 9** −8.14*** −9. 6 8* * 0.33 0.33
70 1–1 3 0 0 −7. 4 8** 0.58 0.85
13 0 1–1 8 0 0 −9.46*** −6.94* 0.46 0.68
18 0 1–1 4 0 0 −8.69** −6.87** 5.90** 0.50 0.71
24 01–3 500 32.6 6** 47.9 1* * 0 .76 0.83
All transect s (no
division between
Atlantic and
Pacific)
−5.0 4** −10. 1 2* −5.40*** −5.7** 0.23 0.93
All transect s (Step as random ef fect)
A: 49.55*** −3.63* −10 . 8 8** −5.34*** −5.53*** 0.62 0.81
Best model
(climatic
variables
Random effect:
Step)
P: 47.95*** 5.62*** −10.85*** −2.96*** 0.59 0 .76
10
|
ADRIANA et Al .
CrystianVenegas, David Villareal, José Aragón, Juan Gálvez, Jesús
Mojica, Marcos Escobar, Mauricio Juárez, Miguel Hernández, Nada
Nikolic, Pedro Osorio and Ramiro Osorio. Financial support from
the Consejo Nacional de Ciencia y Tecnología and the German
Academic Exchange Service (CONACyT-DAAD, 57177537), and
Secretaría de Educación pública (Becas Complemento de Apoyo
al Posgrado) to AH, the German Research Foundation (DFG), to
JK (KL2183/8-1) and ML (LE-1826-5) is gratefully acknowledged.
DATA AVAILAB ILITY STATE MEN T
The data on which this study was based are available in Dryad.
Dryad DOI: doi:10.5061/dryad.7h44j 0zr2
ORCID
Adriana C. Hernández-Rojas https://orcid.
org/0000-0003-3681-1326
Jürgen Kluge https://orcid.org/0000-0001-8574-5746
Thorsten Krömer https://orcid.org/0000-0002-1398-8172
César Carvajal-Hernández https://orcid.
org/0000-0002-5070-4140
Libertad Silva-Mijangos https://orcid.
org/0000-0003-1787-0507
Marcus Lehnert https://orcid.org/0000-0002-7202-7734
Anna Weigand https://orcid.org/0000-0002-3707-0307
Michael Kessler https://orcid.org/0000-0003-4612-9937
FIGURE 4 Relationship between climatic variables and range size of ferns along eight elevational transects in Mexico between 15° and
23° N (Spearman correlation). Pacific side, black circles; Atlantic side, gray triangles
Pacific R = −0.03, P = 0.58
Atlantic R = 0.48, P = 0.001
0
25
50
75
16 20 24
Annual Temperature
Weighted range size
(a)
Pacific R = −0.30, P = 0.001
Atlantic R = −0.24, P = 0.001
Pacific R = −0.30, P = 0.001
Atlantic R = −0.24, P = 0.001
0
25
50
75
1000 2000 3000
Annual precipitation
Weighted range size
(b)
Pacific R = −0.34, P <.001
Atlantic R = 0.05, P = 0.32
0
25
50
75
40 50 60 70 80 90
Annual cloud cover
Weighted range size
(c)
Pacific R = 0.38, P = 0.001
Atlantic R = 0.27, P = 0.001
0
25
50
75
10 20 30
Temperature seasonality
Weighted range size
(d) Pacific R = 0.29, P = 0.001
Atlantic R = −0.56, P = 0.001
0
25
50
75
6000 8000 10000 12000
Precipitation seasonality
Weighted range size
(e) Pacific R = 0.25, P = 0.001
Atlantic R = −0.59, P = 0.001
0
25
50
75
0123
Cloud cover seasonality
Weighted range size
(f)
TABLE 2 Model selection table derived from the model including the residuals of range size (and weighted range size)
explained by latitude, elevation and Side (Model les) using as random ef fect the transect. Full model: Residuals of model
les ~ Bio1+Bio4 + Bio12+Bio15 + CloudA+CloudS. Bio1, Bio4: annual mean temperature and its seasonality, Bio12, Bio15: annual
precipitation and its seasonalit y and CloudA, CloudS: annual cloud cover and its seasonalit y
Model Response variable Fixed effects df AICc Delta Weight
1Residual of non-weighted
range size
Bio15 + Bio4+ CloudA 74,306.3 0.00 0.25
2 Bio15 + Bio4+CloudA + Bio12 8 4,306.3 0.04 0.25
3Residual of weighted r ange
size
CloudS + CloudA+Bio4 + Bio1 84,186 .0 00.0 0.45
4CloudA + CloudS+Bio4 74,186.4 0.42 0.37
|
11
ADRIA NA et Al.
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BIOSKETCH
Adriana Hernández-Rojas is a researcher interested in ecology
and biogeography, with focus on distribution patterns of ferns,
lycophytes and bromeliads as well as in epiphytes management.
Author contributions: A. H., J. K . & M. K . conceived the ideas,
designed the methodology and analysed the data; A. H., A. W.,
C. C., L. S., M. K. & T. K. collected the data; A. H. led writing the
manuscript. All authors contributed critically to the manuscript
and gave final approval for publication.
SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Adriana H-R, Jürgen K, Thorsten K ,
et al. Latitudinal patterns of species richness and range size
of ferns along elevational gradients at the transition from
tropics to subtropics. J Biogeogr. 2020;00:1–15. ht t p s :// do i .
org /10.1111/jbi.13 841
TABLE A1 (Continuation) Likelihood ratio test results (Non-weighted range sizes), with p values for all fixed effects in the linear mixed
models (Coefficients), by transec ts, sides (A = Atlantic and P = Pacific) and elevational groups, using step or group of plots in the same
elevation as a random factors. For all transects together the Transect/Step random factor structure was used. Similar values in the marginal
(R2m) and conditional R2 (R2c) indicate that a linear model is the adequate model with the same coefficients. Bio1, Bio4: annual mean
temperature and its seasonality, Bio12, Bio15: annual precipitation and its seasonality and CloudA, CloudS: annual cloud cover and its
seasonality. Significance codes: *** p < .0 01, * *p < .01, *p < .05
Transect (latitude) Side Bio1 Bio4 Bi o12 B io15 CloudA CloudS R2mR2c
Triunfo (16.5) 196. 28 ** 0.32 0.39
Manantlan (19.6) −10 0. 21** 5 0.15* −19.52*** 0.31 0.31
Nayarit (21.4) 48.16*** 75.36** −112 . 8 3 * * 0.60 0.72
Chiapa sN (17.1) −7.45** −37.43*** 0.48 0.48
Oax ac a (17.5) 0.46 0.62
Tux tla s ( 18 .5) b 72.28* −2 2.8 2** −462.44** 0.34 0.34
Perote (19.4) 23.24* 46.97 * 0.58 0.62
Cielo (23.1) −77. 9 3 * −2 0 0. 12 * −56.49* 0.29 0.53
Pacific: all plots −8. 21*** −7.40*** 0.40 0.52
Atlantic: all plot s −3.64*** 0.22 0.63
Atlantic per elevational groups
0 –70 0 −14. 97** −27. 9 9** −13.66*** −10.30** 0.47 0.58
70 1–1 3 0 0 −5.16*** 3.73* 5.06** 0.56 0.65
13 0 1–1 8 0 0 0.54 0.58
18 0 1–1 4 0 0 0.31 0.65
2 2. 24* −29.3 9 * 0.54 0.64
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ADRIA NA et Al.
Transect (latitude) Side Bio1 Bio4 Bi o12 B io15 CloudA CloudS R2mR2c
Pacific per elevational groups
0 –70 0 −62.97* 0.63 0.63
70 1–1 3 0 0 81.45* 42.98 −62.16* 0.79 0.79
13 0 1–1 8 0 0 −6.39* 0.56 0.56
18 0 1–1 4 0 0 30.76*** 91.91*** 29.5 9* * −67.49*** 0.36 0.67
24 01–3 500 0.38 0.38
By elevational groups (no division between Atlantic and Pacif ic)
0 –70 0 −8.63* −7.1 9 * * −6. 01* 0.31 0. 59
70 1–1 3 0 0 −2 . 53 * 0.48 0.75
13 0 1–1 8 0 0 −2 .17 * −4. 66* * 0.50 0.59
18 0 1–1 4 0 0 −2. 19* 0.17 0.43
24 01–3 500 6.4 6* 0.43 0.64
All transect s
(no division
between
Atlantic and
Pacific)
−2 . 25* * 0.18 0.67
All transect s (transect/step as random effects)
A: 19.20*** 3.7** −4.83* −2 . 49 ** 0.31 0. 65
Best model
(climatic
variables
random effec t:
STEP)
P: 17.16*** 3.45*** −4.11*** −2.36*** 0.28 0.62
Latitude model
A: 7.39* 3.42* 0. 24 0. 61
Elevation model
−1 .65 ** 0.13 0.62
TABLE A1 (Continued)
