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README.md
In SppTrend: Analyzing Linear Trends in Species Occurrence Data
SppTrend: Analyzing linear trends in species occurrence data
- Installation
- Workflow
- Pre-requisites checklist
- Phase 1: Fast diagnostic
- Phase 2: Environmental data
- Phase 3: Overall trends
- Phase 4: Species trends
- Phase 5: Ecological strategies
- Contact
The R package SppTrend provides a methodological framework to analyse temporal changes in species occurrence patterns in relation to spatial variables (longitude, latitude, and elevation) and temperature.
It is designed to support the development of explanatory hypotheses on the effects of environmental change on species assemblages by analysing opportunistically collected occurrence data that include temporal and geographic information.
Installation
You can install the released version of SppTrend from CRAN:
install.packages("SppTrend")
Alternatively, you can install the development version from GitHub:
install.packages("devtools")
library(devtools)
devtools::install_github("MarioMingarro/SppTrend")
library(SppTrend)
Overview of key features
SppTrend helps to characterise species responses to environmental change by analysing historical occurrence data that include:
-
Predictors - Sampling date (year, preferably month and year)
-
Responses - Geographic coordinates (latitude and longitude) and environmental variables (elevation and temperature).
The methodology assumes that observed species occurrences represent a temporal sequence of spatial and thermal responses to environmental change.
Workflow
SppTrend provides a structured workflow for analyzing these trends:
-
Exploratory diagnostic: Quickly visualize the spatial distribution of occurrences and temporal temperature trends within the area of presence using get_fast_info(), based on NetCDF environmental data.
-
Environmental data integration (optional): Enhance the occurrence records with environmental information using functions like get_era5_tme() for temperature data or get_elevation() for elevation.
-
Overall trend estimation: Estimate the average temporal trend of selected response variables across all species using overall_trend(). This trend serves as a baseline against which individual species' trends are compared.
-
Individual trend analysis: Estimate specific-specific temporal trends for each response variable using spp_trend(), enabling direct comparison between individual species responses and the overall trend.
-
Ecological strategy classification: Classify species into distinct spatial or thermal response categories based on the direction and statistical significance of their species-specific trends relative to the overall trend using spp_strategy().
Data requirements
To utilize the package effectively, the input dataset must include the following information for each record:
- Species name (e.g.,
species).
- Geographic coordinates: Latitude (
lat) and Longitude (lon). Note: Coordinates must be in the EPSG:4326 (WGS84) geographic coordinate reference system, which is the standard for biodiversity occurrence data.
- Temporal information: Year of observation (
year) is required. Including month (month) is strongly recommended to allow more detailed temporal analyses.
Important
Ensure that the column names in your input dataset match the default names expected by the SppTrend functions. These default names are:
- Species name:
species
- Year:
year
- Month:
month
- Longitude:
lon
- Latitude:
lat
- Environmental response variables (if applicable):
- Temperature:
tme
- Elevation:
ele
Pre-requisites checklist
Before starting the analysis, ensure you have the following components ready:
- [ ] Occurrence data: A dataframe with columns:
species, year, month, lat, and lon.
- [ ] Coordinates: Ensure your data is in WGS84 (EPSG:4326).
- [ ] Climate data (optional): A
.nc (NetCDF) file to analyze temperature trends. See get_era5_tme().
- [ ] Elevation data (optional): A
.tif (GeoTIFF) file for elevation analysis. See get_elevation()
- [ ] R packages: Install
readr, and SppTrend.
Real data example
The following is an example using ranidae example from GBIF and selected in the exted (lon: -10 >= record <= 10 & lat: -40 >= record <= 40) and in dates (year >= 1950).
A total of 13,808 records of 15 different species.
ranidae <- readr::read_csv2(system.file("extdata", "example_ranidae.csv", package = "SppTrend"),
col_types = cols(year = col_double(),
month = col_double(),
lon = col_double(),
lat = col_double()))
To capture intra-annual variations and treat time as a continuous high-resolution variable, we combine year and month into a single decimal predictor (year_month).
This transformation allows the models to use a continuous variable representing the exact temporal position of each record, providing a more nuanced analysis of temporal trends.
ranidae$year_month <- ranidae$year + ((ranidae$month - 1) / 12)
print(head(ranidae))
Phase 1: Fast diagnostic and visual summary
The get_fast_info() function provides a quick visual diagnostic of the input data.
It generates a map showing the spatial distribution of occurrence records together with a time-series plot derived from a NetCDF environmental dataste.
Using the geographic coordinates of the occurrence records, the function extracts the complete climate time-series (from the earliest to the latest year represented in the data) for the corresponding occupied cells.
All temperature values from occupied cells are then added annually to estimate and visualise the overall temperature trend (including slope and associated p-value).
This diagnostic step allows users to quickly assess the climate trajectory of the regions where the species have been recorded and to evaluate whether sufficient temporal and environmental variation is present for subsequent analyses.
Technical notes:
- See get_era5_tme()
nc_file <- "path/to/your/era5_data.nc"
info <- get_fast_info(ranidae, nc_file)
Phase 2: Environmental data generation
The SppTrend package provides functions to enhance species occurrence records with relevant environmental information.
At present, it supports the integration of monthly temperature data and elevation data associated with each occurrence.
2.1 ERA5 temperature data
ERA5 is the fifth-generation reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing a globally consistent representation of atmospheric, land, and ocean conditions.
If offers high spatial and temporal resolution climate data from 1940 to the present, making it a valuable and widely used resource for assessing the influence of climate on species distributions.
Explore the ERA5-Land monthly averaged data from 1950 to present dataset on the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at: ERA5 Land monthly.
For detailed information about the ERA5 dataset, please visit the ECMWF website.
The SppTrend package provides the function get_era5_tme() to incorporate ERA5-Land monthly climate data.
This function retrieves mean monthly air temperature values associated with species occurrence records based on their geographic coordinates (latitude and longitude) and sampling date (year and month).
Technical notes:
- Temporal coverage: ERA5 data is available from 1950 to the present.
- Source: Download data from ERA5 Land monthly.
- Format: Files must be in .nc (NetCDF) format.
nc_file <- "path/to/your/era5_data.nc"
ranidae <- get_era5_tme(ranidae, nc_file)
print(head(ranidae))
Missing temperature data (NA) for 572 points. Removing records.
2.2 Digital Elevation Model (DEM) data
The get_elevation() function retrieves Digital Elevation Model (DEM) values associated with species occurrence records, providing information ont the elevation at which each species was observed.
For obtaining elevation data for species occurrences, this example utilizes the WorldClim dataset (WorldClim).
However, users are encouraged to select alternative DEM sources depending on the spatial resolution and geographic scope required for their analysis.
For instance, the EU-DEM dataset offers high-resolution elevation data for Europe.
Technical notes:
- Format: DEM data must be in .tif format.
dem_file <- "path/to/your/dem.tif"
ranidae <- get_elevation(ranidae, dem_file)
print(head(ranidae))
Missing elevation data (NA) for 54 points. Removing records
Phase 3: Estimation of overall response trends
The overall_trend() function calculates the overall temporal trend (OT) of selected response variables across the entire dataset.
This trend integrates both environmental change and the cumulative effects of sampling bias, and serves as a neutral reference against which species-specific temporal trends are evaluated.
Technical notes:
Longitude (lon) values are transformed to a 0-360 range to ensure statistical consistency near the antimeridian.
A key feature of this function is its specialized handling of latitude. Because the Equator is set at 0, latitude values in the Southern Hemisphere are negative.
To ensure that a direction shift is interpreted consistently across the globe (where a negative increase in the South corresponds to a positive increase in the North), the function employs two complementary approaches:
- Hemispheric split: It divides the records based on their location (lat < 0 for South and lat > 0 for North) and performs separate analyses for each.
- Global analysis: It performs an analysis using the complete dataset (Global) by transforming all latitudes into absolute values (abs(lat)). This allows for a unified global trend estimation.
Note that this hemispheric division and absolute transformation logic is applied exclusively to the latitude (lat) variable.
predictor <- "year_month"
responses <- c("lat", "lon", "ele", "tme")
overall_trend_result <- overall_trend(ranidae, predictor, responses)
print(head(overall_trend_result))
Phase 4: Estimation of species-specific response trends
The spp_trend() function estimates the species-specific temporal trends for each selected response variable and statistically compares them with the overall temporal trend derived from the complete dataset.
Technical notes:
It compares individual species' trajectories against the OT using the interaction term of the lm().
Longitude (lon) values are transformed to a 0-360 range to ensure statistical consistency near the antimeridian.
A key feature of this function is its specialized handling of latitude. Because the Equator is set at 0, latitude values in the Southern Hemisphere are negative.
To ensure that a direction shift is interpreted consistently across the globe (where a negative increase in the South corresponds to a positive increase in the North), the function employs two complementary approaches:
- Hemispheric split: It divides the records based on their location (lat < 0 for South and lat > 0 for North) and performs separate analyses for each.
- Global analysis: It performs an analysis using the complete dataset (Global) by transforming all latitudes into absolute values (abs(lat)). This allows for a unified global trend estimation.
Note that this hemispheric division and absolute transformation logic is applied exclusively to the latitude (lat) variable.
predictor <- "year_month"
responses <- c("lat", "lon", "ele", "tme")
spp <- unique(ranidae$species)
spp_trend_result <- spp_trend(ranidae, spp, predictor, responses, n_min = 10)
print(head(spp_trend_result))
WARNING: Species Amnirana occidentalis has insufficient data (n = 4 and < n_min = 10) in North hemisphere.
WARNING: Species Lithobates clamitans has insufficient data (n = 1 and < n_min = 10) in North hemisphere.
WARNING: Species Rana draytonii has insufficient data (n = 9 and < n_min = 10) in North hemisphere.
WARNING: Species Rana temporaria has insufficient data (n = 1 and < n_min = 10) in North hemisphere.
WARNING: Species Amnirana fonensis has insufficient data (n = 2 and < n_min = 10) in North hemisphere.
Note on Sample Size (n_min)
In this example, we have set n_min = 10, meaning the function only considers species with more than 10 records.
This low threshold is used here specifically to accommodate the small sample size of the example_ranidae dataset.
However, a higher value is strongly recommended.
spp_trend() results definitions
| Variable | Description |
| :--- | :--- |
| species | Name of the analyzed species |
| responses | Name of the analyzed variable |
| trend | Estimated slope of the linear model ($\beta$) |
| t | t-statistic for the species-specific trend |
| pvalue | Statistical significance of the species-specific trend (null hypothesis $\beta = 0$). |
| ci_95_max | Upper 95% confidence interval bound for the slope. |
| ci_95_min | Lower 95% confidence interval bound for the slope. |
| dif_t | t-statistic of the interaction term, indicating the magnitude of the difference between the species trend and the Overall Trend (OT). |
| dif_pvalue | p-values of the interaction term. A low value indicates a significant deviation from the general trend. |
| n | Total number of occurrence records (sample size) for the specific species. |
| hemisphere | Geographical subset (North, South, or Global) used to ensure latitudinal symmetry in the analysis. |
Phase 5: Analysis of specific species responses
The spp_strategy() function analyses the outputs of spp_trend() to classify species into distinct spatial or thermal response categories based on the direction and statistical significance of their species-specific trends relative to the overall trend.
The function incorporates hemisphere-specific logic to correctly interpret poleward shifts in latitude and can also be applied to classify elevational trends.
The Bonferroni correction
To avoid false positives (Type I errors) due to multiple comparisons when analyzing many species, the Bonferroni correction sould be applied.
The significance level is adjusted as:
$$\alpha_{adj} = \frac{\alpha}{n}$$
where $n$ is the number of species.
Only those trends where the pvalue (or dif_pvalue) is lower than $\alpha_{adj}$ are classified into categories. Species that do not meet this threshold are categorized as having non-significant responses (SC or TC).
spp_strategy_result <- spp_strategy(spp_trend_result, sig_level = 0.05/length(spp), responses = c("lat", "lon", "ele", "tme"))
print(head(spp_strategy_result))
Ecological strategies
The SppTrend package identifies several Spatial and Thermal response strategies based on species-specific temporal trends relative to the overall background trend.
Spatial Responses
-
Spatial Adaptation (SA): A significant positive temporal trend in the spatial position of species occurrences.
In the context of climate change, this pattern is commonly associated with a poleward shift, corresponding to a northward displacement (towards higher latitude values) in the Northern Hemisphere and southward displacement (towards lower latitude values) in the Southern Hemisphere, as species expand into newly suitable areas.
-
Spatial Discordance (SD): A significant negative temporal trend in the spatial position of species occurrences.
In the context of climate change, this pattern is often associated with an equatorward shift and may arise when other ecological and anthropogenic factors influence species distributions independently of, or in opposition to, climate-driven range shifts.
-
Spatial Conformity (SC): A spatial response pattern in which the species-specific temporal trend does not differ significantly from the overall trend.
Species showing spatial conformance share the same bias structure as the complete dataset, preventing the inference of a distinct, species-specific response to climate change at the scale of analysis.
Thermal Responses
-
Thermal Tolerance (TT): A thermal response pattern characterised by a significant positive temporal trend in the temperature conditions under which species are observed, relative to the overall trend.
This pattern suggest an increased likelihood of occurrence under warmer conditions and an apparent capacity to tolerate rising temperatures through physiological, behavioural, and evolutionary mechanisms.
-
Thermal Adjustment (TA): A thermal response characterised by a significant negative temporal trend in the temperature conditions associated with species occurrences, relative to the overall trend.
This indicates and increasing association with cooler temperature conditions over time, potentially reflecting microevolutionary change or phenotypic adjustment.
-
Thermal Conformity (TC): A thermal response pattern in which species-specific temperature trends do not differ significantly from the overall trend.
Species showing thermal conformance share the same background thermal signal as the complete dataset, preventing the formulation of specific hypotheses regarding climate-driven thermal responses.
In essence, while SA and SD describe the 'what' (change in presence) and SP/SE a potential 'how' (direction of geographic shift along the latitudinal gradient),
these spatial responses should be considered together with the thermal responses (TT, TA) to understand if both point towards a consistent overall direction of a species'
response to environmental change.
Applications and limitations
SppTrend provides a useful methodological framework for investigating how environmental change affects biodiversity through the analysis of temporal trends in species occurrence data.
However, results should be interpreted with caution, as opportunistic occurrence data are inherently subject to multiple sources of bias, including uneven sampling effort and variation in observer expertise.
Although the overall trend offers a valuable community-level reference, it represents an average signal across all species and may obscure contrasting species-specific responses to drivers such as climate warming.
Consequently, emphasis should be placed on species –level trends and their classification into ecological response strategies, which together provide a more nuanced and informative understanding of biodiversity responses to environmental change.
For more detailed information and examples, please refer to the package documentation within R:
help(package = SppTrend)
Example data
The data used in the example are also available in:
inst/extdata/example_ranidae.csv
path <- system.file("extdata", "example_ranidae.csv", package = "SppTrend")
ranidae <- read_csv(path)
References
This package is based on the methodology described in:
Lobo, Mingarro, Godefroid & García-Roselló (2023) Taking advantage of opportunistically collected historical occurrence data to detect responses to climate change: The case of temperature and Iberian dung beetles. Ecology and evolution, 13(12) e10674. https://doi.org/10.1002/ece3.10674
Contact
For any questions or issues, please feel free to contact:
Mario Mingarro
mario_mingarro@mncn.csic.es
Jorge M. Lobo
jorge.lobo@mncn.csic.es
Emilio García-Roselló
egrosello@esei.uvigo.es
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SppTrend documentation built on Feb. 7, 2026, 5:07 p.m.
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SppTrend: Analyzing linear trends in species occurrence data
- Installation
- Workflow
- Pre-requisites checklist
- Phase 1: Fast diagnostic
- Phase 2: Environmental data
- Phase 3: Overall trends
- Phase 4: Species trends
- Phase 5: Ecological strategies
- Contact
The R package SppTrend provides a methodological framework to analyse temporal changes in species occurrence patterns in relation to spatial variables (longitude, latitude, and elevation) and temperature.
It is designed to support the development of explanatory hypotheses on the effects of environmental change on species assemblages by analysing opportunistically collected occurrence data that include temporal and geographic information.
Installation
You can install the released version of SppTrend from CRAN:
install.packages("SppTrend")
Alternatively, you can install the development version from GitHub:
install.packages("devtools")
library(devtools)
devtools::install_github("MarioMingarro/SppTrend")
library(SppTrend)
Overview of key features
SppTrend helps to characterise species responses to environmental change by analysing historical occurrence data that include:
-
Predictors - Sampling date (year, preferably month and year)
-
Responses - Geographic coordinates (latitude and longitude) and environmental variables (elevation and temperature).
The methodology assumes that observed species occurrences represent a temporal sequence of spatial and thermal responses to environmental change.
Workflow
SppTrend provides a structured workflow for analyzing these trends:
-
Exploratory diagnostic: Quickly visualize the spatial distribution of occurrences and temporal temperature trends within the area of presence using
get_fast_info(), based on NetCDF environmental data. -
Environmental data integration (optional): Enhance the occurrence records with environmental information using functions like
get_era5_tme()for temperature data orget_elevation()for elevation. -
Overall trend estimation: Estimate the average temporal trend of selected response variables across all species using
overall_trend(). This trend serves as a baseline against which individual species' trends are compared. -
Individual trend analysis: Estimate specific-specific temporal trends for each response variable using
spp_trend(), enabling direct comparison between individual species responses and the overall trend. -
Ecological strategy classification: Classify species into distinct spatial or thermal response categories based on the direction and statistical significance of their species-specific trends relative to the overall trend using
spp_strategy().
Data requirements
To utilize the package effectively, the input dataset must include the following information for each record:
- Species name (e.g.,
species). - Geographic coordinates: Latitude (
lat) and Longitude (lon). Note: Coordinates must be in the EPSG:4326 (WGS84) geographic coordinate reference system, which is the standard for biodiversity occurrence data. - Temporal information: Year of observation (
year) is required. Including month (month) is strongly recommended to allow more detailed temporal analyses.
Important
Ensure that the column names in your input dataset match the default names expected by the SppTrend functions. These default names are:
- Species name:
species - Year:
year - Month:
month - Longitude:
lon - Latitude:
lat - Environmental response variables (if applicable):
- Temperature:
tme- Elevation:
ele
- Elevation:
Pre-requisites checklist
Before starting the analysis, ensure you have the following components ready:
- [ ] Occurrence data: A dataframe with columns:
species,year,month,lat, andlon. - [ ] Coordinates: Ensure your data is in WGS84 (EPSG:4326).
- [ ] Climate data (optional): A
.nc(NetCDF) file to analyze temperature trends. Seeget_era5_tme(). - [ ] Elevation data (optional): A
.tif(GeoTIFF) file for elevation analysis. Seeget_elevation() - [ ] R packages: Install
readr, andSppTrend.
Real data example
The following is an example using ranidae example from GBIF and selected in the exted (lon: -10 >= record <= 10 & lat: -40 >= record <= 40) and in dates (year >= 1950).
A total of 13,808 records of 15 different species.
ranidae <- readr::read_csv2(system.file("extdata", "example_ranidae.csv", package = "SppTrend"),
col_types = cols(year = col_double(),
month = col_double(),
lon = col_double(),
lat = col_double()))
To capture intra-annual variations and treat time as a continuous high-resolution variable, we combine year and month into a single decimal predictor (year_month).
This transformation allows the models to use a continuous variable representing the exact temporal position of each record, providing a more nuanced analysis of temporal trends.
ranidae$year_month <- ranidae$year + ((ranidae$month - 1) / 12)
print(head(ranidae))
Phase 1: Fast diagnostic and visual summary
The get_fast_info() function provides a quick visual diagnostic of the input data.
It generates a map showing the spatial distribution of occurrence records together with a time-series plot derived from a NetCDF environmental dataste.
Using the geographic coordinates of the occurrence records, the function extracts the complete climate time-series (from the earliest to the latest year represented in the data) for the corresponding occupied cells.
All temperature values from occupied cells are then added annually to estimate and visualise the overall temperature trend (including slope and associated p-value).
This diagnostic step allows users to quickly assess the climate trajectory of the regions where the species have been recorded and to evaluate whether sufficient temporal and environmental variation is present for subsequent analyses.
Technical notes:
- See get_era5_tme()
nc_file <- "path/to/your/era5_data.nc"
info <- get_fast_info(ranidae, nc_file)
Phase 2: Environmental data generation
The SppTrend package provides functions to enhance species occurrence records with relevant environmental information.
At present, it supports the integration of monthly temperature data and elevation data associated with each occurrence.
2.1 ERA5 temperature data
ERA5 is the fifth-generation reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing a globally consistent representation of atmospheric, land, and ocean conditions. If offers high spatial and temporal resolution climate data from 1940 to the present, making it a valuable and widely used resource for assessing the influence of climate on species distributions. Explore the ERA5-Land monthly averaged data from 1950 to present dataset on the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) at: ERA5 Land monthly. For detailed information about the ERA5 dataset, please visit the ECMWF website.
The SppTrend package provides the function get_era5_tme() to incorporate ERA5-Land monthly climate data.
This function retrieves mean monthly air temperature values associated with species occurrence records based on their geographic coordinates (latitude and longitude) and sampling date (year and month).
Technical notes:
- Temporal coverage: ERA5 data is available from 1950 to the present.
- Source: Download data from ERA5 Land monthly.
- Format: Files must be in .nc (NetCDF) format.
nc_file <- "path/to/your/era5_data.nc"
ranidae <- get_era5_tme(ranidae, nc_file)
print(head(ranidae))
Missing temperature data (NA) for 572 points. Removing records.
2.2 Digital Elevation Model (DEM) data
The get_elevation() function retrieves Digital Elevation Model (DEM) values associated with species occurrence records, providing information ont the elevation at which each species was observed.
For obtaining elevation data for species occurrences, this example utilizes the WorldClim dataset (WorldClim).
However, users are encouraged to select alternative DEM sources depending on the spatial resolution and geographic scope required for their analysis.
For instance, the EU-DEM dataset offers high-resolution elevation data for Europe.
Technical notes:
- Format: DEM data must be in .tif format.
dem_file <- "path/to/your/dem.tif"
ranidae <- get_elevation(ranidae, dem_file)
print(head(ranidae))
Missing elevation data (NA) for 54 points. Removing records
Phase 3: Estimation of overall response trends
The overall_trend() function calculates the overall temporal trend (OT) of selected response variables across the entire dataset.
This trend integrates both environmental change and the cumulative effects of sampling bias, and serves as a neutral reference against which species-specific temporal trends are evaluated.
Technical notes:
Longitude (lon) values are transformed to a 0-360 range to ensure statistical consistency near the antimeridian.
A key feature of this function is its specialized handling of latitude. Because the Equator is set at 0, latitude values in the Southern Hemisphere are negative.
To ensure that a direction shift is interpreted consistently across the globe (where a negative increase in the South corresponds to a positive increase in the North), the function employs two complementary approaches:
- Hemispheric split: It divides the records based on their location (lat < 0 for South and lat > 0 for North) and performs separate analyses for each.
- Global analysis: It performs an analysis using the complete dataset (Global) by transforming all latitudes into absolute values (abs(lat)). This allows for a unified global trend estimation.
Note that this hemispheric division and absolute transformation logic is applied exclusively to the latitude (lat) variable.
predictor <- "year_month"
responses <- c("lat", "lon", "ele", "tme")
overall_trend_result <- overall_trend(ranidae, predictor, responses)
print(head(overall_trend_result))
Phase 4: Estimation of species-specific response trends
The spp_trend() function estimates the species-specific temporal trends for each selected response variable and statistically compares them with the overall temporal trend derived from the complete dataset.
Technical notes:
It compares individual species' trajectories against the OT using the interaction term of the lm().
Longitude (lon) values are transformed to a 0-360 range to ensure statistical consistency near the antimeridian.
A key feature of this function is its specialized handling of latitude. Because the Equator is set at 0, latitude values in the Southern Hemisphere are negative.
To ensure that a direction shift is interpreted consistently across the globe (where a negative increase in the South corresponds to a positive increase in the North), the function employs two complementary approaches:
- Hemispheric split: It divides the records based on their location (lat < 0 for South and lat > 0 for North) and performs separate analyses for each.
- Global analysis: It performs an analysis using the complete dataset (Global) by transforming all latitudes into absolute values (abs(lat)). This allows for a unified global trend estimation.
Note that this hemispheric division and absolute transformation logic is applied exclusively to the latitude (lat) variable.
predictor <- "year_month"
responses <- c("lat", "lon", "ele", "tme")
spp <- unique(ranidae$species)
spp_trend_result <- spp_trend(ranidae, spp, predictor, responses, n_min = 10)
print(head(spp_trend_result))
WARNING: Species Amnirana occidentalis has insufficient data (n = 4 and < n_min = 10) in North hemisphere.
WARNING: Species Lithobates clamitans has insufficient data (n = 1 and < n_min = 10) in North hemisphere.
WARNING: Species Rana draytonii has insufficient data (n = 9 and < n_min = 10) in North hemisphere.
WARNING: Species Rana temporaria has insufficient data (n = 1 and < n_min = 10) in North hemisphere.
WARNING: Species Amnirana fonensis has insufficient data (n = 2 and < n_min = 10) in North hemisphere.
Note on Sample Size (n_min)
In this example, we have set n_min = 10, meaning the function only considers species with more than 10 records.
This low threshold is used here specifically to accommodate the small sample size of the example_ranidae dataset.
However, a higher value is strongly recommended.
spp_trend() results definitions
| Variable | Description |
| :--- | :--- |
| species | Name of the analyzed species |
| responses | Name of the analyzed variable |
| trend | Estimated slope of the linear model ($\beta$) |
| t | t-statistic for the species-specific trend |
| pvalue | Statistical significance of the species-specific trend (null hypothesis $\beta = 0$). |
| ci_95_max | Upper 95% confidence interval bound for the slope. |
| ci_95_min | Lower 95% confidence interval bound for the slope. |
| dif_t | t-statistic of the interaction term, indicating the magnitude of the difference between the species trend and the Overall Trend (OT). |
| dif_pvalue | p-values of the interaction term. A low value indicates a significant deviation from the general trend. |
| n | Total number of occurrence records (sample size) for the specific species. |
| hemisphere | Geographical subset (North, South, or Global) used to ensure latitudinal symmetry in the analysis. |
Phase 5: Analysis of specific species responses
The spp_strategy() function analyses the outputs of spp_trend() to classify species into distinct spatial or thermal response categories based on the direction and statistical significance of their species-specific trends relative to the overall trend.
The function incorporates hemisphere-specific logic to correctly interpret poleward shifts in latitude and can also be applied to classify elevational trends.
The Bonferroni correction
To avoid false positives (Type I errors) due to multiple comparisons when analyzing many species, the Bonferroni correction sould be applied. The significance level is adjusted as:
$$\alpha_{adj} = \frac{\alpha}{n}$$
where $n$ is the number of species.
Only those trends where the pvalue (or dif_pvalue) is lower than $\alpha_{adj}$ are classified into categories. Species that do not meet this threshold are categorized as having non-significant responses (SC or TC).
spp_strategy_result <- spp_strategy(spp_trend_result, sig_level = 0.05/length(spp), responses = c("lat", "lon", "ele", "tme"))
print(head(spp_strategy_result))
Ecological strategies
The SppTrend package identifies several Spatial and Thermal response strategies based on species-specific temporal trends relative to the overall background trend.
Spatial Responses
-
Spatial Adaptation (SA): A significant positive temporal trend in the spatial position of species occurrences. In the context of climate change, this pattern is commonly associated with a poleward shift, corresponding to a northward displacement (towards higher latitude values) in the Northern Hemisphere and southward displacement (towards lower latitude values) in the Southern Hemisphere, as species expand into newly suitable areas.
-
Spatial Discordance (SD): A significant negative temporal trend in the spatial position of species occurrences. In the context of climate change, this pattern is often associated with an equatorward shift and may arise when other ecological and anthropogenic factors influence species distributions independently of, or in opposition to, climate-driven range shifts.
-
Spatial Conformity (SC): A spatial response pattern in which the species-specific temporal trend does not differ significantly from the overall trend. Species showing spatial conformance share the same bias structure as the complete dataset, preventing the inference of a distinct, species-specific response to climate change at the scale of analysis.
Thermal Responses
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Thermal Tolerance (TT): A thermal response pattern characterised by a significant positive temporal trend in the temperature conditions under which species are observed, relative to the overall trend. This pattern suggest an increased likelihood of occurrence under warmer conditions and an apparent capacity to tolerate rising temperatures through physiological, behavioural, and evolutionary mechanisms.
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Thermal Adjustment (TA): A thermal response characterised by a significant negative temporal trend in the temperature conditions associated with species occurrences, relative to the overall trend. This indicates and increasing association with cooler temperature conditions over time, potentially reflecting microevolutionary change or phenotypic adjustment.
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Thermal Conformity (TC): A thermal response pattern in which species-specific temperature trends do not differ significantly from the overall trend. Species showing thermal conformance share the same background thermal signal as the complete dataset, preventing the formulation of specific hypotheses regarding climate-driven thermal responses.
In essence, while SA and SD describe the 'what' (change in presence) and SP/SE a potential 'how' (direction of geographic shift along the latitudinal gradient), these spatial responses should be considered together with the thermal responses (TT, TA) to understand if both point towards a consistent overall direction of a species' response to environmental change.
Applications and limitations
SppTrend provides a useful methodological framework for investigating how environmental change affects biodiversity through the analysis of temporal trends in species occurrence data.
However, results should be interpreted with caution, as opportunistic occurrence data are inherently subject to multiple sources of bias, including uneven sampling effort and variation in observer expertise.
Although the overall trend offers a valuable community-level reference, it represents an average signal across all species and may obscure contrasting species-specific responses to drivers such as climate warming.
Consequently, emphasis should be placed on species –level trends and their classification into ecological response strategies, which together provide a more nuanced and informative understanding of biodiversity responses to environmental change.
For more detailed information and examples, please refer to the package documentation within R:
help(package = SppTrend)
Example data
The data used in the example are also available in:
inst/extdata/example_ranidae.csv
path <- system.file("extdata", "example_ranidae.csv", package = "SppTrend")
ranidae <- read_csv(path)
References
This package is based on the methodology described in:
Lobo, Mingarro, Godefroid & García-Roselló (2023) Taking advantage of opportunistically collected historical occurrence data to detect responses to climate change: The case of temperature and Iberian dung beetles. Ecology and evolution, 13(12) e10674. https://doi.org/10.1002/ece3.10674
Contact
For any questions or issues, please feel free to contact:
Mario Mingarro mario_mingarro@mncn.csic.es
Jorge M. Lobo jorge.lobo@mncn.csic.es
Emilio García-Roselló egrosello@esei.uvigo.es
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