Research Article |
Corresponding author: Jorge M. Lobo ( jorge.lobo@mncn.csic.es ) Academic editor: Christoph Knogge
© 2021 Ana Manjarrés-Hernández, Cástor Guisande, Emilio García-Roselló, Juergen Heine, Patricia Pelayo-Villamil, Elisa Pérez-Costas, Luis González-Vilas, Jacinto González-Dacosta, Santiago R. Duque, Carlos Granado-Lorencio, Jorge M. Lobo.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Manjarrés-Hernández A, Guisande C, García-Roselló E, Heine J, Pelayo-Villamil P, Pérez-Costas E, González-Vilas L, González-Dacosta J, R. Duque S, Granado-Lorencio C, Lobo JM (2021) Predicting the effects of climate change on future freshwater fish diversity at global scale. Nature Conservation 43: 1-24. https://doi.org/10.3897/natureconservation.43.58997
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The aim of the present study was to predict future changes in biodiversity attributes (richness, rarity, heterogeneity, evenness, functional diversity and taxonomic diversity) of freshwater fish species in river basins around the world, under different climate scenarios. To do this, we use a new methodological approach implemented within the ModestR software (NOO3D) which allows estimating simple species distribution predictions for future climatic scenarios. Data from 16,825 freshwater fish species were used, representing a total of 1,464,232 occurrence records. WorldClim 1.4 variables representing average climate variables for the 1960–1990 period, together with elevation measurements, were used as predictors in these distribution models, as well as in the selection of the most important variables that account for species distribution changes in two scenarios (Representative Concentration Pathways 4.5 and 6.0). The predictions produced suggest the extinction of almost half of current freshwater fish species in the coming decades, with a pronounced decline in tropical regions and a greater extinction likelihood for species with smaller body size and/or limited geographical ranges.
Distribution models, evenness, heterogeneity, Niche of Occurrence, species richness, rarity, taxonomic diversity
Predicting the consequences of climate change on organisms’ geographical distribution and, consequently, on their biodiversity, is a complex and monumental task, but one which is necessary (
Freshwater fish appear to be a group which is especially vulnerable to climatic changes (
The present study is not characterised by the use of complex algorithms with unreliable absence data; instead, we make use of the unique empirical data frequently available (occurrence observations) to infer the environmental conditions under which particular species seem to be able to maintain sustainable populations. This information was subsequently used to transfer spatial data on these suitable environmental conditions to potential future climatic scenarios. The purpose of this study, therefore, is to map the future location of the climatic conditions under which freshwater fish species are currently observed, assuming the general incapacity of freshwater fish species to colonise new river basins (
The dataset of geographical records for freshwater fish, developed by
We used the classification described by
It was not possible to include reproduction traits, such as life span, parental care or reproduction habitat, because of the difficulties inherent in the assignment of these functional traits to over 16,000 species.
The future distribution of species was estimated by a modelling procedure (NOO3D) available in the ModestR software (
In essence, both NOO and NOO3D aim to overcome the drawbacks associated with the general lack of reliable absence information (
Subsequently, the relevance of these environmental variables to explain the distribution of each one of the species was estimated by using the Instability Index described by
Once the most important environmental variables which affect the distribution of each species in their accessible area were identified, all cells with environmental conditions similar to those existing in the occurrence localities were delimited, thus predicting the changes in species diversity in the years 2050 and 2070, under the RCP 4.5 and RCP 6.0 climate scenarios. The most appropriate model outputs to be applied in NOO3D were selected by comparing estimated river basin species richness with the results of accumulation curves derived from occurrence records (
The final consequence of all this process is a geographic representation of each species’ distribution area both for present and future scenarios, taking into account the climatic conditions of the observed occurrences. In this manner, the most probable future distribution of each species was estimated according to the information derived from current occurrences and in accordance with the values of the environmental variables in potential future scenarios (see
The geospatial data for river basins imported into ModestR was obtained from the WaterBase project website (http://www.waterbase.org). WaterBase global river basin data were taken from the drainage basin dataset distributed with HYDRO1k, a hydrological database developed by the EROS Data Center of the U.S. Geological Survey (USGS). This database provides a collection of global geo-referenced layers at a 1 km resolution derived from GTOPO30, a 30 arc-second digital elevation model (DEM) of the world. The drainage basins dataset from HYDRO1k was projected on to latitude/longitude geographical coordinates. Vertices were smoothed by applying a 500 m threshold in order to generate the ESRI Shape files available via the WaterBase website.
The river basins dataset was originally obtained by combining flow accumulation and flow direction layers, which were, in turn, derived from the hydrologically-corrected DEM, based on the GTOPO30 dataset. The basins were organised according to the procedure first proposed by
We used level-two of the river basins data set (
For each river basin, both in present and in future projections (2050 and 2070), according to the results obtained for the RCP 4.5 and RCP 6.0 scenarios, several diversity indices were estimated. The DER function from the R package EcoIndR (
All statistical analyses were run with the RWizard application StatR (
As expected, the regression slope between river basin species richness, estimated with accumulation curves and observed species richness, obtained from records (Fig.
Relationships between the species richness in level-two river basins predicted by accumulation curves (abcissa), using the KnowBR package and those obtained with occurrence records (red) and after applying the proposed model approach with a Kernel density (smoothing value of 2) (blue; ordinate). Green line shows the 1:1 fit.
Amongst all of the procedures used to select the accessible area in the modelling procedures (convex hull, alpha shape with different α values, Kernel density with different smoothing values etc.), the model displaying the best fit when compared with the species richness estimated from accumulation curves was generated with a Kernel density estimator using a smoothing value of two. The intercept of this relationship was not significantly different from zero (ANCOVA, P = 0.292) and the slope was not significantly different from one (ANCOVA, P = 0.512) with a value of 0.99 (Fig.
The decline in species richness was very similar between scenarios RCP 4.5 and RCP 6.0. These models predicted the complete disappearance of the distributional areas of half of all freshwater fish species (from 45.3% to 46.7%, independent of the year or the climatic scenario). As an example of the predicted decline, Fig.
Predictions of the changes in species richness in river basins (in numbers in the upper panel and in percentages in the lower panel), by the year 2070 under the RCP 4.5 scenario, as compared to current species richness. The river basins with grey backgrounds had no records, no species and/or distribution model estimation was impossible. High negative values represent basins with high species extinction rates.
Fig.
Relative contribution, with LMG method, of the significant climatic predictors obtained from a stepwise multiple regression, in which the dependent variable is the predicted change in species richness from the present to the year 2070 (RCP 4.5 scenario). The explanatory variables were the minimum, maximum and mean values of the climatic WorldClim variables mentioned in the Material and methods section, which were averaged for each level-two river basin. Plots above the bars show the relationships between the dependent variable and each one of the statistically-significant independent variables.
The values of the different diversity components (richness, rarity, heterogeneity, evenness, taxonomic diversity functional diversity) were very similar between the two scenarios (Fig.
Boxplots of the rate of change in richness, rarity, heterogeneity (Shannon-Wiener), evenness (Simpson evenness), taxonomic diversity (taxonomic distinctness) and functional diversity (functional richness) in each river basin, as predicted for the years 2050 (RCP 4.5 scenario) and 2070 (RCP 6.0 scenario). A value less than 1 means that the Diversity Index is lower in the future scenarios than in the present and vice versa. Outliers are not shown in the boxplots. The numbers indicate median values for all river basins.
Fig.
Boxplot showing the extent of occurrence (EOO, in km2) of the species for each scenario and year. The numbers within each plot indicate mean EOO values for all species present in each scenario. The numbers of species predicted as present in each scenario are indicated in the x-axis. The category “Compared.2000” is the mean EOO of the species in the present, but only considering those species predicted as present in the scenario with a higher number of species projected to be extinct (RCP 4.5 2070). Notched box plots show median values (horizontal line), interquartile range values between upper and lower quartiles (top and bottom of the box), distribution of 99% of data (upper and lower dashed lines) and notch lengths representing classic 95% confidence intervals. Note that, when notches do not overlap, medians may be seen to differ significantly (
Predictions of the change in the Extent of Occurrence (EOO, mean value of all species present in the river basin in km2) in river basins for the year 2070, with the RCP 4.5 scenario, as compared to the current species EOO. River basins with grey backgrounds had no records, no species and/or distribution model estimation was impossible.
The comparison between the freshwater fish species richness scores, derived from accumulation curves and those generated by stacking individual SDMs, allowed the selection of the most appropriate geographical extent or accessible area (
The predictions, provided by this study, are similar to those suggested by
The estimated predicted loss of species richness may also be attributed to the inclusion of poorly-studied tropical areas, which support elevated levels of endemism. Such endemic species may be less likely to adapt to climate change (
These results support the species-energy theory (
There is an important gap, which was not considered in this study: the effect of changing flow regimes on freshwater fish diversity (
In addition to the effect of climate change on species richness and geographic species range size, the climate-induced changes in taxonomic diversity observed in the present study, which have rarely been addressed for freshwater fish (
We are indebted to all the organisations, institutions and people whose efforts have made it possible to compile the valuable biological data that now are freely available. We are grateful for the insightful comments offered by the Dra. Abigail Mary Moore. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).
Appendix 1
Data type: data occurrences
Explanation note: Sources describing all the data downloaded from the Global Biodiversity Information Facility (GBIF; see https://www.gbif.org/), which were used in this study.
Table S1
Data type: species data
Explanation note: Description of the species included in the analysis, as well as future species predictions in 2050 and 2070 and under both scenarios. If a species is categorised as “NOT”, the complete disappearance of their distributional area is predicted. The maximum body length of each species is also included, obtained from https://www.fishbase.org/ or from the original manuscripts, when not available at FishBase.
Appendix 2
Data type: species data
Explanation note: Biological traits assigned to each one of the considered species. We used 6 traits divided into three biological functions: food acquisition, life habitat and locomotion. Food acquisition traits include the feeding habitat (pelagic, benthopelagic and benthic) and the trophic guild (primary consumer, secondary consumer, top-predator, omnivorous and detritivorous). Life habitat traits comprise habitat type (pelagic, benthopelagic and demersal) and migration type (potamodromous, anadromous, catadromous, amphidromous, oceanodromous and no migration). Finally, locomotion traits include body length (in cm: small < 15, medium 15–50, large 50–150 and extra-large > 150) and rheophily (rheophilic, limnophilic and eurytopic).
Appendix 3
Data type: tutorial
Explanation note: Full description of the NOO3D procedure followed to predict the future distribution of world freshwater fish.