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\begin{document}

\title{Reliability of presence-only data for assessing plant community
responses to climate warming}



\author[1]{Laura Camila Pacheco Riaño}%
\author[2]{Sabine Rumpf}%
\author[3]{Tuija Maliniemi}%
\author[4]{Suzette  Flantua}%
\author[5]{Jon-Arvid Grytnes}%
\affil[1]{University of Gothenburg}%
\affil[2]{University of Basel}%
\affil[3]{University of Oulu}%
\affil[4]{University of Bergen}%
\affil[5]{Universitetet i Bergen Institutt for Biologi}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Climate warming has triggered shifts in plant distributions, resulting
in changes within communities, characterized by an increase in
warm-demanding species and a decrease in cold-adapted species - referred
to as thermophilization. Researchers conventionally rely on
co-occurrence data from vegetation assemblages to examine these
community dynamics. Despite the increasing availability of presence-only
data in recent decades, their potential has largely remained unexplored
due to concerns about their reliability. Our study aimed to determine
whether climate-induced changes in community dynamics, as inferred from
presence-only data from the Global Biodiversity Information Facility
(GBIF), corresponded with those derived from co-occurrence plot data in
Norway. To assess the differences between these datasets, we quantified
a Community Temperature Index (CTI) from the co-occurrence data set and
compared this with CTI obtained from presence-only data. We also
examined the temporal trend in CTI (i.e., thermophilization) in both
datasets. To do this, we first established a species-temperature
relationship based on data before climate warming. In a preliminary
analysis, we assessed the performance of this relationship using three
datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a
broader European region organized into pseudo-plots (potentially
capturing more species niches), and 3) a combined dataset merging 1) and
2). The transfer function including both datasets performed best.
Subsequently, we compared the CTI for the co-occurrence plots paired up
spatially and temporally with presence-only pseudo-plots. The results
demonstrated that presence-only data can effectively evaluate species
assemblage responses to climate warming, with consistent CTI and
thermophilization values in comparison to co-occurrence data. Employing
presence-only data for evaluating community responses opens up better
spatial and temporal resolution and much more detailed analyses of such
responses, our results therefore outline how a large amount of
presence-only data can be used to enhance our understanding of community
dynamics in a warmer world.%
\end{abstract}%



\sloppy


Reliability of presence-only data for assessing plant community
responses to climate warming

\section*{Abstract}

{\label{abstract}}

Climate warming has triggered shifts in plant distributions, resulting
in changes within communities, characterized by an increase in
warm-demanding species and a decrease in cold-adapted species - referred
to as thermophilization. Researchers conventionally rely on
co-occurrence data from vegetation assemblages to examine these
community dynamics. Despite the increasing availability of presence-only
data in recent decades, their potential has largely remained unexplored
due to concerns about their reliability. Our study aimed to determine
whether climate-induced changes in community dynamics, as inferred from
presence-only data from the Global Biodiversity Information Facility
(GBIF), corresponded with those derived from co-occurrence plot data in
Norway. To assess the differences between these datasets, we quantified
a Community Temperature Index (CTI) from the co-occurrence data set and
compared this with CTI obtained from presence-only data. We also
examined the temporal trend in CTI (i.e., thermophilization) in both
datasets. To do this, we first established a species-temperature
relationship based on data before climate warming. In a preliminary
analysis, we assessed the performance of this relationship using three
datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a
broader European region organized into pseudo-plots (potentially
capturing more species niches), and 3) a combined dataset merging 1) and
2). The transfer function including both datasets performed best.
Subsequently, we compared the CTI for the co-occurrence plots paired up
spatially and temporally with presence-only pseudo-plots. The results
demonstrated that presence-only data can effectively evaluate species
assemblage responses to climate warming, with consistent CTI and
thermophilization values in comparison to co-occurrence data. Employing
presence-only data for evaluating community responses opens up better
spatial and temporal resolution and much more detailed analyses of such
responses, our results therefore outline how a large amount of
presence-only data can be used to enhance our understanding of community
dynamics in a warmer world.

\section*{Keywords}

{\label{keywords}}

plant community, climate warming, biodiversity informatics,
thermophilization, GBIF, assemblages

\section*{Introduction}

{\label{introduction}}

Ongoing climate warming induces range shifts of species which track
their optimal temperature conditions (Lenoir et al., 2020; Mamantov et
al., 2021; O'Sullivan et al., 2021; Steinbauer et al., 2018). These
shifts subsequently lead to local reorganizations of species communities
and assemblages (Men\selectlanguage{ngerman}éndez et al., 2006; Walther, 2010), often resulting
in a relative increase of warm-demanding species and/or a decreasing
number of cold-demanding species, a pattern referred to as
thermophilization (Gottfried et al., 2012). Thermophilization has been
reported globally from a wide variety of habitats and areas, from
mountain tops (Gottfried et al., 2012) to forests (Zellweger et al.,
2020) and from temperate (Pacheco\selectlanguage{english}-Riano et al., 2023) to tropical
regions (Fadrique et al., 2018).

Studies on multi-decadal vegetation responses to climate change such as
thermophilization commonly rely on permanent or semi-permanent
plot-based datasets as the main source of information (Bertrand et al.,
2011; Chytry et al., 2014; Fadrique et al., 2018; Freeman et al., 2021;
Gotzenberger et al., 2012; Kapfer et al., 2017; Richard et al., 2021).
Permanent plots are best suited to track vegetation dynamics, multiple
initiatives have been set up in the beginning of the
21\textsuperscript{st} century for continuous long-term monitoring
(Chytry et al., 2014; Gottfried et al., 2012; Haider et al., 2022).
Although the number of permanent plots is continuously increasing, they
are, however, still geographically scattered, and most cover relatively
short time spans. Historical co-occurrence plots (herein broadly defined
as species records co-occurring in a specific site), were initially done
to describe the structure and diversity of vegetation types (e.g.,
phytosociological plots, also called releves). Resurveys of these
semi-permanent plots have also proven to be a valuable source of
information to describe vegetation dynamics over decades (Kapfer et al.,
2017), study range shifts (Felde et al., 2012; Lenoir et al., 2008;
Rumpf et al., 2018) and thermophilization responses (Pacheco\selectlanguage{english}-Riano et
al., 2023).

To study vegetation dynamics over time, an alternative to co-occurrence
datasets is the aggregation of species occurrence records (i.e.,
presence-only data from individual species observations) such as museum
and herbaria collections, or, more recently, observations from various
structured or unstructured citizen science projects. Presence-only data
have been collected extensively over the last century and their number
has increased enormously over the past 20 years (Heberling et al.,
2021). This type of data generally has more extensive temporal and
spatial coverage compared to co-occurrence data due to the vast network
of data collectors but comes at the cost of missing information about
absences. The world's largest biodiversity data network, mediated by The
Global Biodiversity Information Facility -GBIF (http://gbif.org), stands
as the leading open-access data portal for geo-referenced species
occurrence data collected from a myriad of different sources (Konig et
al., 2019; Wuest et al., 2020). GBIF provides access to more than 1.5
billion species records from across the globe and the tree of life. In
addition to missing absence data, many records are prone to biases
stemming from identification errors and spatially biased sampling due to
the diversity of collectors and data sources, among other issues (Beck
et al., 2014; Meyer et al., 2016). Therefore, these data are commonly
considered unreliable for many community analyses and have so far only
been exploited to a limited extent to assess community responses to
global warming (Bottin et al., 2020; Duchenne et al., 2021; Feeley,
2012; Lajeunesse \& Fourcade, 2023). The availability of presence-only
data offers us the opportunity to investigate community responses in
various ways. This includes using presence-only data for regions or
species that lack historical co-occurrence data. Alternatively, we can
utilize presence-only data for regions with limited data availability in
the contemporary context. It's also worth considering a combination of
both approaches, where the integration of presence-only data and
co-occurrence data can help mitigate some of the biases inherent to each
data type.

Assessing thermophilization relies on species co-occurrences in a
specific area combined with species-specific thermal indicator values to
calculate community temperature indices (CTI), i.e., the (weighted)
average of the thermal indicator values for species assemblages. The CTI
approach is an effective way to summarize thermophilization trends by
comparing changes in CTI over time (Duque et al., 2015; Feeley et al.,
2020; Freeman et al., 2021; Richard et al., 2021). It can provide an
unbiased estimation of thermophilization regardless of sampling
differences, as long as there is no disproportionate collection of
warm-demanding or cold-adapted species compared to their actual
occurrences in the area (or vice versa). In other words, if the sampling
efforts do not favour one type of species over the other in terms of
their thermal preferences, the CTI can be assumed to accurately reflect
the degree of thermophilization in each community. Presence-only data
could thus hold a great potential to fill spatial and temporal gaps in
studies of species dynamics, and, therefore, allow for a more
comprehensive understanding of climate-driven responses of species
across their geographical ranges (Konig et al., 2019).

One approach to quantify the CTI is a technique from paleoecology, the
transfer function approach (Bertrand et al., 2011; Pacheco\selectlanguage{english}-Riano et al.,
2023). Transfer functions are mathematical models that represent the
relationship between species occurrences and environmental variables
from a certain period, assuming that species have symmetrical, unimodal
response curves with an ecological optimum for climate variables
(Hutchinson, 1957). If the relationship between species and climate
remains constant through time (ecological uniformitarianism) (Rull,
2010), the inferred species-climate relationship can be used to
reconstruct past or present climates from community composition (Salonen
et al., 2011). Transfer functions have been used extensively by
palaeoecologists to reconstruct past climatic conditions from current
relationships between species co-occurrences and climatic conditions
(Guiot \& de Vernal, 2007; Juggins \& Birks, 2012; Schabitz et al.,
2013). Subsequently, community compositions from sediment cores are used
to reconstruct paleoclimates (Birks \& Simpson, 2013). This approach has
been used to reconstruct various environmental conditions, from water
chemistry (e.g. pH) using diatoms (ter Braak \& Juggins, 1993), to
temperature (Chevalier et al., 2020) and precipitation (Lu et al., 2019)
using fossil pollen. A corresponding approach has recently been utilized
in modern ecology to estimate thermophilization by inferring temperature
from co-occurrence vegetation data based on a CTI approach (Bertrand et
al., 2011; Pacheco-Riano et al., 2023). In this case historical
co-occurrence plots sampled prior to major climatic changes were used to
calibrate a transfer function, which was subsequently used to project
the CTI based on more recent vegetation plot data (Bertrand et al.,
2011; Bhatta et al., 2018; Pacheco\selectlanguage{english}-Riano et al., 2023). Based on this
approach, thermophilization can be estimated as the difference between
the floristically inferred temperature (i.e., CTI) and the observed
temperature from the calibrating period (Pacheco-Riano et al., 2023).

Exploiting the vast amount of presence-only data to analyse the
responses of communities to climate warming requires, however, a
rigorous evaluation of robustness and reliability (Bayraktarov et al.,
2019). Therefore, our aim was to answer two key questions. First, we
wanted to determine if changes in community dynamics due to climate
warming, as deduced from presence-only data from GBIF, corresponded with
co-occurrence plot data. Second, we aimed to assess whether these two
datasets could be used interchangeably, either individually or in
combination, to yield similar community responses during the model
calibration or prediction phases. In our study, we incorporated
co-occurrence plot data from Norway alongside spatially and temporally
aggregated presence-only data (referred to as pseudo-plots) in Europe.
We intentionally employed a broader geographical scope for the
presence-only data to avoid niche truncation, a benefit provided by the
GBIF spatial coverage. Within this context, we assessed the difference
in CTI and thermophilization index. We hypothesized that both types of
data would exhibit a consistent pattern and could be employed
interchangeably for our analyses.

\section*{Methods}

{\label{methods}}

All analyses were conducted in R v.4.0.2 (R Core Team, 2022). The
package tidyverse v.1.3.2 (Wickham et al., 2019) was used to clean and
handle the data, sf v.1.0-9 (Pebesma, 2018) and raster v.3.6 (Hijmans,
2023) for spatial data manipulation, and ggplot2 v.3.4.0 for
visualization (Wickham et al., 2019).

\subsection*{Data}

{\label{data}}

\subsection*{Co-occurrence dataset}

{\label{co-occurrence-dataset}}

We used the same non-standardized co-occurrence dataset used by
Pacheco\selectlanguage{english}-Riano et al., (2023) consisted of 605,637 taxa records of
terrestrial vascular plants (3,617 taxa) from particular locations
without marked spatial boundaries from 1900 until 2007 from Norway.
These records had their origins in a series of field campaigns,
primarily involving releves, conducted by various collectors across
Norway since the early 1900s. After that, these records were
meticulously documented in the field notes from The Agder naturmuseum og
botaniske hage, University of Oslo, University of Trondheim, and The
Norwegian University of Life Sciences, and integrated into GBIF and
stored as occurrence data. To reconstruct the co-occurrence plots, we
grouped the data by coordinates, year, elevation, and collector as
described in Pacheco-Riano et al. (2023), and only kept taxa at species
level or merged lower taxonomical units to species level, using GBIF's
backbone taxonomy tool (The Global Biodiversity Information Facility,
2020). This resulted in 41,993 co-occurrence plots with 2,888 species
covering the time span from 1905 to 2007 (\textbf{Fig. 1} ).

\subsection*{Presence-only dataset}

{\label{presence-only-dataset}}

We downloaded from GBIF all global georeferenced records for those plant
species included in the non-standardized co-occurrence dataset described
above (3,617 taxa, \textbf{Table S1} ). This included a total of
212,286,166 records from 6,831 datasets (December 8th, 2022,
https://doi.org/10.15468/DL.VZVGK7 GBIF.org, 2022).

We applied six automated cleaning procedures to eliminate known issues
with presence-only data using the package ``CoordinateCleaner'' (Zizka
et al., 2019): 1) equal coordinates (records with identical longitude
and latitude), 2) zero coordinates (plain zeros in the coordinates and a
radius around), 3) capitals (radius around capital cities), 4) centroids
(radius around country and province centroids), 5) sea coordinates
(non-terrestrial records), and 6) biodiversity institutions (radius
around biodiversity institutions). All flagged records or records with
missing coordinates or missing record years were removed. As for the
co-occurrence dataset, we harmonized the taxonomy using GBIF's backbone
taxonomy tool (The Global Biodiversity Information Facility, 2020), and
kept records identified at the species and infraspecific level, e.g.,
subspecies, but merged all records to species level. Subsequently,
duplicated records with the same species names, coordinates and year
were removed, and only occurrences within Europe (longitude: -12deg to
35deg, latitude: 45deg to 72deg) were kept. Lastly, to prevent any duplication
with the co-occurrence dataset, we excluded records with the same
species names and geographical coordinates as contained the
co-occurrence records. This process resulted in a cleaned, presence-only
dataset of 68,112,130 occurrence records of 2,742 species.

To build ``communities'' with spatio-temporal information from the
presence-only data, we created ``pseudo-plots'' by aggregating species
occurrences temporally and spatially by using the same grid cells as for
the temperature dataset described below (i.e., 30 arc seconds
resolution). Only pseudo-plots between 1905-2016 were kept for further
analyses (\textbf{Fig.1} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1 The density distribution of} assemblages. (a)
Co-occurrence plots covering Norway, and (b) presence-only pseudo-plots
spanning across Europe. Both panels represent all vascular plant species
recorded during the baseline period from 1950 to 1979 per 0.5 x 0.5\selectlanguage{ngerman}°
degrees cell.

\subsection*{Temperature data}

{\label{temperature-data}}

From here and henceforth, we will refer to assemblage for both
co-occurrence plots and presence-only pseudo-plots. Temperature was
extracted for each vegetation assemblage geographical location
(co-occurrence and pseudo-plot) using CHELSAcruts. This climate dataset
provide monthly data for every year from 1901 up to 2016 at a spatial
resolution of 30 arc seconds (Karger et al., 2017; Karger \& Zimmermann,
2018). We calculated mean annual temperatures (MAT) by averaging maximum
and minimum temperature values for all months of the respective year.
The final MAT for each vegetation assemblage was calculated as the
average of the five years preceding the respective field survey,
including surveyed year (Steinbauer et al., 2018).

\subsection*{Community thermal index -
CTI}

{\label{community-thermal-index---cti}}

To estimate CTI for each assemblage, a transfer function was first
calibrated with the assemblages from the period before the recent major
increase in temperature, the baseline period (1950-1979, \textbf{Fig. 1}
, see Pacheco-Riaño et al. (2023) for a justification for selecting this
period). Subsequently, this transfer function was used to project CTI
for the assemblages before and after this baseline period (i.e.
1905-1949 and 1980-2007/16, respectively). The transfer function was
calibrated using a weighted-averaging partial least squares regression
(WA-PLS) using the fxTWAPLS package (Liu et al., 2020). This is an
efficient approach to creating unbiased CTI estimates (Bhatta et al.,
2019; Liu et al., 2020).

The WA-PLS is based on estimating species optima along a gradient of MAT
and assumes that the full temperature range of a species is captured in
the calibration dataset. If this is not the case, the niches are
truncated leading to an overestimation of the CTIs at the lower end and
an underestimation at the higher end of the temperature range (Liu et
al., 2020). Prior to the main analyses, we therefore evaluated the
effect of using three different datasets in the calibration process, and
computed three different transfer functions based on either 1) the
co-occurrence plots from Norway, 2) the pseudo-plots derived from
presence-only data from Europe (assuming that the extent captured a
wider thermal gradient than Norwegian plant species have and thus avoid
niche truncation, at least at the warmer limit), and 3) the two datasets
combined. To ensure sufficient species representation for the transfer
function, we only retained species that occurred in more than ten
assemblages in each dataset. In the calibration subset, this resulted in
717 species for the co-occurrence dataset, 2,273 species for the
presence-only dataset, and 2,290 species for the combined dataset. In
addition, we kept only those assemblages that contained a minimum of ten
species for the projection of CTI. Through the application of these
criteria, we ensured that each assemblage had sufficient species
representation to estimate CTI using a transfer function (Bhatta et al.,
2018).

The performance of each transfer function was evaluated through
leave-one-out cross-validation, specifically focusing on the calibration
assemblages located in Norway and utilizing only 75\% of the calibrating
data from these Norwegian assemblages. We also used these
cross-validation analyses to identify the statistically reliable
component of the WA-PLS, which was determined by the combination of the
lowest root mean square error of prediction (RMSEP), the highest
R\textsuperscript{2} value, and the lowest average bias (Liu et al.,
2020). For all three datasets the inclusion of the second component gave
the RMSEP, the highest R\textsuperscript{2}, and the lowest average bias
within Norway. For this evaluation, only assemblages from the same
period as used for the calibration itself were included. The
co-occurrence dataset had the highest values for RMSEP (3.54),
R\textsuperscript{2} (0.81) and average bias (0.117), followed by the
presence-only dataset with 3.12, 0.68 and 0.043, respectively, and the
combined dataset had the lowest values with 3.09, 0.67 and 0.032,
respectively (\textbf{Fig. S3} ).

Plotting the predicted CTI versus the observed MAT for the assemblages
in the baseline period demonstrated that all transfer functions
overestimated the CTI at low temperatures and underestimated the CTI at
high temperatures (\textbf{Fig.S4} ). To correct for this remaining
over- and underestimations due to niche truncation in the ends, we used
a local nonparametric regression (LOESS) with a span of 0.75 to fit the
residuals of the WA-PLS model. The difference between the residuals of
the WA-PLS model and the LOESS model was then used to correct the
initially predicted CTI in the main analyses. We fitted the LOESS only
for the vegetation assemblages within Norway due to their colder climate
relative to the rest of Europe, preventing potential underestimation of
CTI values (\textbf{Fig. S4} ). Finally, to reduce niche truncation
effects near the edges during projection phase, we further refined our
dataset by excluding communities with extreme temperatures and focused
our CTI projections on assemblages within a MAT range of -2.5°C to 8.5°C
(initial MAT range -5.4 ° - 13°C), representing 95\% of the calibration
data (Pacheco\selectlanguage{english}-Riano et al., 2023). After these corrections, the
co-occurrence dataset had an R\textsuperscript{2} of 0.86, the
presence-only dataset of 0.79 and the combined dataset of 0.78
(\textbf{Fig. S5} ). Next, we carried out CTI projections for each
vegetation assemblage, covering the period of 1905 to 1949 before the
baseline and for the contemporary period spanning from 1980 to 2007/16.
Because the evaluation of the different datasets indicated that the
three different transfer functions had different advantages based on the
RMSEP, R\textsuperscript{2}, and average bias, we made projections with
all three transfer functions to compare co-occurrence and presence-only
in the subsequent analyses.

\subsection*{Comparison of CTI between co-occurrence and
presence-only}

{\label{comparison-of-cti-between-co-occurrence-and-presence-only}}

Given that co-occurrence plots are commonly employed in CTI and
thermophilization analyses (De Frenne et al., 2013; Fadrique et al.,
2018; Richard et al., 2021), we established the CTI of co-occurrence
plots as a benchmark. We then assessed the degree of similarity between
the estimated CTI derived from pseudo-plots generated from presence-only
data and the CTI estimated from co-occurrence plots for the three
calibration models (co-occurrence, presence-only, and combined). To be
able to make this comparison, we reproduced any spatiotemporal biases of
the co-occurrence dataset in the presence-only dataset. For this we
paired up the co-occurrence plots with the pseudo-plots by subsampling
the latter, finding the closest available pseudo-plot that was sampled.
We only paired up a pseudo-plot with a co-occurrence plot if the centre
of a pseudo-plot was found within a radius of 1 km from the
co-occurrence plot, if the difference in sampling time was less than two
years, and if the MAT of the assemblages had a difference of less than
0.5deg C. If a co-occurrence plot did not have a corresponding pseudo-plot
given these criteria, it was discarded. As the co-occurrence dataset
only extends until 2007, the paired period was constrained to the same
timeframe, resulting in 3,839 assemblage pairs for the period 1905-2007.
For each assemblage pair we quantified the difference between the CTI
estimated based on the pseudo-plot and on the co-occurrence plot (\selectlanguage{greek}Δ
\selectlanguage{english}CTI). Positive \selectlanguage{greek}Δ \selectlanguage{english}CTI indicates an overestimation of CTI for the
pseudo-plot relative to the co-occurrence plot. A value of zero
indicates equal CTI estimation while negative \selectlanguage{greek}Δ \selectlanguage{english}CTI indicates an
underestimation of CTI for the pseudo-plots. To statistically assess
these differences, we conducted t-tests. Further, to evaluate the
effectiveness of the pseudo-plots in estimating community responses, and
to reveal any temporal deviance from the co-occurrence plots, we
examined the temporal trend of thermophilization independently for the
two already paired datasets. We estimated the thermophilization index as
the difference between the CTI and the observed baseline temperature
(mean MAT 1950-1979) for each vegetation assemblage in the paired
dataset (Pacheco-Ria\selectlanguage{ngerman}ño et al., 2023). A positive value indicates an
increase in the CTI (an increase in thermophilic species) compared to
the baseline period of 1950-1979, while a negative value suggests a
decrease in the CTI (an increase in cryophilic species) during the same
reference period. Note that this thermophilization trend was not
bias-corrected as it was done in Pacheco-Riaño et al. (2023), but the
same potential spatiotemporal bias was kept for both datasets.

\section*{Results}

{\label{results}}

\subsection*{Comparison of predicted values for co-occurrence and
presence-only
data}

{\label{comparison-of-predicted-values-for-co-occurrence-and-presence-only-data}}

The average \selectlanguage{greek}Δ \selectlanguage{english}CTI in the paired dataset (difference between pseudo and
co-occurrence assemblages ) for the study period (1905-2007) was low
independent of which calibration model was used, but it was
statistically significantly different when calibrating the transfer
function with the co-occurrence data only (\textbf{Fig.2a-c;} paired
t-test average difference = 0.062, std.error 0.016, t = 3.7068, df =
3,838, p-value \textless{}= 0.001). The model calibrated with
presence-only data showed an average difference of 0.029 (std.error
0.021, t = 1.35, df = 3,838, p-value = 0.17), and the model calibrated
with the combined dataset showed an average difference of 0.014
(std.error 0.021, t = 0.68, df = 3,838, p-value = 0.49). In all cases
the presence-only CTI were slightly higher than those of the
co-occurrence dataset.

Since the transfer function calibrated with the combined dataset
displayed the lowest difference in paired CTI (\textbf{Fig. 2c} ), we
relied on this calibration model as the basis for illustrating the
temporal pattern of thermophilization for both datasets (\textbf{Fig.
2d} ). This revealed that there was no statistically significant
difference between thermophilization trends base on the co-occurrence
and presence-only datasets, and that the thermophilization values
increased steadily around the same time (\textbf{Fig. 2d} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig. 2} Comparison of the Community Temperature Index (CTI) of
the paired dataset (\selectlanguage{greek}Δ \selectlanguage{english}CTI, 3,839 pairs) for the different calibrated
models (panels a, b and c). In the paired dataset, co-occurrence plots
were matched with presence-only by subsampling the latter and selecting
the closest available pseudo-plot that had been sampled. X-axis: Mean
annual temperature (MAT) of the average of the five years preceding the
respective assemblage survey of the co-occurrence dataset. Y-axis:
Difference between the estimated CTI for the paired dataset (\selectlanguage{greek}Δ \selectlanguage{english}CTI). A
LOESS regression line is shown in each panel. Panel d: Temporal
thermophilization trend. Thermophilization is estimated using the
projection of the combined calibrated model using the paired dataset.
Blue solid line: co-occurrence dataset, green dashed line: presence-only
dataset.

\subsection*{Ad-hoc analyses of the CTI
deviations}

{\label{ad-hoc-analyses-of-the-cti-deviations}}

Across all transfer functions, there was a tendency for CTI
overestimation in the presence-only data, particularly at lower
temperatures (\textbf{Fig. 2 and S7} ). This overestimation remained
within the credibility interval when utilizing the transfer function
based on the combined dataset, but deviations from the credibility
interval were noted when using the transfer functions derived solely
from either co-occurrence data or presence-only data (\textbf{Fig 2. and
S7} ). Because the areas of colder temperatures tend to be placed in
areas of higher topographical reliefs in Norway, we suspected that these
deviations were due to elevational biases in the presence only data
towards more accessible and warmer valley bottoms compared to the
co-occurrence plots. To evaluate this, we assessed elevation differences
between the co-occurrence plots and their correspondingly paired
presence-only pseudo-plots. Elevations were extracted from DEM (Digital
Elevation Model) with 25 m resolution (Copernicus, 2016) using original
coordinates of the co-occurrence data and coordinates provided for the
presence-only data. Subsequently, we computed the average elevation of
all observations of all species occurring in each co-occurrence plot and
pseudo-plot. When multiple observations existed for a single species
within a pseudo-plot, we randomly chose one record per species. Our
findings indicate a general tendency that observations in the
presence-only pseudo-plots were situated at lower elevations compared to
the co-occurrence plots with a mean difference of -35.3 meters (std.
error of the mean = 1.41, \textbf{Fig. S8} ). Furthermore, it is evident
that larger disparities in elevations correspond to greater variations
in CTI values. The observed trend was quantified using linear models,
yielding a slope of -1.36 \selectlanguage{ngerman}× 10\textsuperscript{-4} °C/m for the
co-occurrence calibration model (std. error =2 × 10\textsuperscript{-4},
p = 0.645) and -1.07 × 10\textsuperscript{-3} °C/m for the presence-only
calibration model (std. error = 4 × 10\textsuperscript{-4}, p = 0.005),
with a slope of -1.07 × 10\textsuperscript{-3} °C/m (std. error = 3 ×
10\textsuperscript{-4}, p = 0.003) the combined model (\textbf{Fig.3} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\selectlanguage{greek}\emph{Fig. 3 Relationship between Δ CTI (the difference in CTI based on
presence only data and CTI based on co-occurrence data) and Δ Elevation
(the difference in average elevation for specimens of presence-only
pseudo-plot and co-occurrence plots) in different calibrated models
using the paired dataset.}\selectlanguage{english} \emph{In the paired dataset, co-occurrence
plots were matched with pseudo-plots by subsampling the latter and
selecting the closest available pseudo-plot that had been sampled. The
x-axis represents the elevational difference between presence-only and
co-occurrence dataset. Here we used the 95th percentile of the paired
dataset.}

\section*{Discussion}

{\label{discussion}}

Our assessment demonstrated the potential of employing presence-only
data for evaluating community responses to climate warming, specifically
by quantifying the Community Temperature Index (CTI) and
thermophilization. The aggregation of presence-only data in pseudo-plots
yields CTI values remarkably consistent with those obtained by
co-occurrence plots, which are conventionally perceived as more reliable
(Bertrand et al., 2011; Pacheco-Ria\selectlanguage{ngerman}ño et al., 2023). Presence-only data
typically originate from museum collections and citizen science
projects. As such, they often suffer from different spatial and
taxonomical biases, e.g., variable sampling density per area and
low-quality control of species identification (Beck et al., 2014).
Despite the inherent biases and errors associated with presence-only
data, our study reveals a consistent estimate of the CTI for the paired
plots and a consistent temporal trend in thermophilization from the two
data sets. This consistency can be attributed to the absence of biases
in species observations regarding their temperature indicator values. In
simpler terms, there is no tendency to record more cold-adapted or
warm-adapted species in presence-only data compared to co-occurrence
data. Even if species in an area is under-sampled in the presence-only
pseudo-plots, the likelihood of observing a species, regardless of its
temperature indicator value, remains constant. Likewise for the
misidentification of species, there is no relationship with consistently
higher or lower thermal indicator values. In addition, even if a
proportion of the species was misidentified, we expect this to be minor
compared to the substantial number of correct identifications and thus
expect misidentification to have a minor impact on our analyses.

By including presence-only data in CTI analysis, we are able to cover
larger geographic areas than based on traditional co-occurrence plots
alone (König et al., 2019). This can be useful towards global
completeness and representativeness of species data and producing more
realistic projections of the community's responses. Larger temporal and
spatial coverage also provide improved opportunities to unravel the
impacts of various climatic drivers and how climate interacts with other
variables. This becomes particularly valuable in situations where
co-occurrence surveys are restricted, either in terms of spatial or
temporal coverage. The inclusion of presence-only data also provides a
cost-effective way of monitoring ongoing dynamics in species
communities, an alternative to more intensive co-occurrence surveys,
which are often considered information of the highest quality but can be
time-consuming and expensive (Dengler et al., 2011). This can also be
particularly advantageous in areas where field data is difficult to
obtain or in cases where intensive fieldwork is not feasible. However,
in both datasets, there are still some underrepresented areas like the
tropics or continents like Africa, particularly those associated with
colder climates and more remote areas, such as higher latitudes and
elevations.

Although the CTI values were very similar, we observed that the
differences in predicted CTI values between the two datasets (\selectlanguage{greek}Δ \selectlanguage{english}CTI)
were more pronounced towards the colder end of the thermal gradient.
This deviation could potentially be attributed to the distinct
methodologies employed in data collection. Co-occurrence data is often
gathered through more or less organized expeditions and may as such
cover a more representable distribution of the topographic relief in an
area including higher and more remote areas. The presence-only data,
however, are often compiled from more random observations made in
unplanned citizen science projects. In colder areas, which in most cases
will entail mountain regions in our study area, the more accessible
parts are in the valley bottoms (where roads are placed) resulting in a
bias towards lower elevations in topographically heterogenous areas. Our
ad hoc analysis substantiated this expectation (\textbf{Fig. 3} ),
revealing that co-occurrence plots are predominantly situated at higher
elevations in areas where CTI from pseudo-plots is overestimated
compared to the co-occurrence plots. Being aware of this, it would be
possible to adjust for this potential bias by also incorporating
elevation when aggregating species to pseudo-plots, or by adjusting for
the bias in CTI by using the observed elevations of the presence-only
data. This would be especially important when comparing pseudo-plots, or
when combining co-occurrence plots and presence-only data in areas of
different topographical relief.

Overall, we noticed that the variations among the CTIs are comparable
when calibrating the transfer functions using all three types of
datasets. However, the difference observed in the model relying only on
the co-occurrence dataset can be attributed to the extent of the dataset
employed for calibrating the model. We found that including plant
communities from a larger area improved the transfer function, notably
when including communities with thermophilic species from lower
latitudes (e.g., from central Europe). This inclusion effectively
addresses the issue of niche truncation in the warmer end of the thermal
range. However, including presence-only data from outside our focus area
did not improve the accuracy of the CTI values at the colder end; this
is likely due to the lack of species in the dataset from higher
latitudinal areas or elevations. This consideration is of particular
significance given the ongoing global warming trend, as adjusting the
overestimation of the cold end would be less necessary as there will be
a higher representation of thermophilic species in the communities.
However, giving special attention to species adapted to warmer
conditions holds significant relevance in Norway, given its
predominantly cold climate. In contrast, in locations with milder
temperatures, especially those not situated at high latitudes or
elevations, relying on presence-only data would help mitigate the
truncation of species at both the cold and warm extremes. Moreover, we
saw that the differences were larger for older assemblages than for more
recent ones, which could be attributed to the improved accuracy of the
newer information.

It is important to note that the accuracy of the CTI values produced by
pseudo-plots will depend on the quality of the presence-only data being
used. The exponential growth of presence-only data records in the last
two decades has resulted in greater public access to these records (Jin
\& Yang, 2020). However, before using these data for ecological
analyses, cleaning and standardization procedures must be undertaken.
This step is especially important due to the varying sources and
properties of the data, as temporal, spatial, and taxonomic criteria all
need to be considered (Meyer et al., 2016).

Integrating presence-only data into species distribution modelling has
been widely used (Beck et al., 2014; Pacifici et al., 2017; Smith et
al., 2023). However, very few studies explore their use in understanding
community responses, such as thermophilization, to environmental changes
(Feeley, 2012). Our study demonstrates that by including presence-only
data, we can better understand and learn from their advantages and
drawbacks in biogeographical analyses. Although the outcomes produced by
this method may have some flaws, they could be the first approximation
for many regions and taxonomical groups and provide a good starting
point for further research. As demonstrated here, open data portals,
such as GBIF, can be utilized to consolidate datasets that are used to
analyse communities' responses to environmental change. To bridge
existing data gaps, the digitization and mobilization of scientific
biological collections and personal archives of researchers must be
continued. This will help to monitor species composition, abundance, and
diversity changes and identify potential threats to ecosystem
functioning, leading to a better understanding of the environmental
impact of global climate change.

Our study compellingly indicates that presence-only data can be used to
estimate community indicators (e.g., CTI) accurately. It serves as an
additional source of information to complement more traditional
co-occurrence plot-based datasets. Our findings suggest that the data
integration of presence-only data can be used to improve the calibration
of transfer function models and our understanding of vegetation
responses to climate change. Nevertheless, the overall patterns and
trends of thermophilization remain largely consistent across the two
datasets, suggesting that the thermophilization values are not
significantly different when using pseudo-plots. Even more important is
the fact that both datasets show a consistent trend in
thermophilization, independent of the calibration dataset. We
additionally presented an outline that can be used to study community
responses for global change research. Our main findings, therefore,
demonstrate that presence-only data can be used to quantify
thermophilization. Though some careful attention is needed when
integrating traditional co-occurrence plots with presence-only data,
there is a substantial potential to unlock new opportunities for rapid
and cost-effective monitoring of communities in response to changes in
climate.

\section*{Data Sharing and Data
Accessibility}

{\label{data-sharing-and-data-accessibility}}

The data used in this study was obtained from GBIF at the following
DOIs: Presence-only dataset: https://doi.org/10.15468/DL.VZVGK7.
University of Agder (2022). Vascular plant herbarium (KMN) UiA
https://doi.org/10.15468/2g6i0v, University of Oslo
https://doi.org/10.15468/w8gru5, https://doi.org/10.15468/tvnjk7,
Norwegian University of Science and Technology
https://doi.org/10.15468/kkb2x0, Norwegian University of Life Sciences
(NMBU) https://doi.org/10.15468/mbh-mmt. The climate data used are
available from http://chelsa-climate.org/chelsacruts/ . The R scripts
required to reproduce the results are available on Zenodo at the
following DOI: \emph{{[} We will insert Zenodo DOI here} {]}

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