DOI: 10.5061/dryad.f4qrfj751
title: Data from: Turning the tide: A 2°C increase in heat tolerance can halve climate change induced losses in four cold-adapted kelp species

description: Kelp forests are susceptible to climate change as their sessile nature and
 low dispersal capacity hinders tracking of suitable conditions. The
 emergence of a wide array of approaches to increasing thermal tolerance
 seeks to change the outlook of biodiversity in a changing climate but
 lacks clear targets of impactful thermal resilience. Here, we utilize
 species distribution models (SDMs) to evaluate the potential of enhanced
 thermal tolerance to buffer the effects of climate change on cold-adapted
 kelp species: Saccharina latissima, Alaria esculenta, Laminaria
 hyperborea, and Laminaria digitata. For each species, we compared a
 baseline model - where the thermal niche remained unchanged - to models
 where the simulated maximum sea surface temperature tolerance was
 increased by 1-5°C. These models were projected into three climate change
 scenarios: sustainability (Shared Socioeconomic Pathway (SSP) 1-1.9, Paris
 Agreement), regional rivalry (SSP3-7.0) and fossil-fuel development (SSP
 5-8.5). Our SDMs demonstrate that an increase of 1-2°C in thermal
 tolerance could recover over 50% of predicted losses of suitable habitat
 for cold-adapted kelps. However, A. esculenta, a species of growing
 commercial interest, still faced persistent habitat contraction across all
 climate change scenarios and simulated tolerance increases including up to
 15% unrecovered losses under SSP5-8.5, even with a simulated 5°C increase
 in thermal tolerance. Our findings highlight the need for a two-pronged
 approach to conserve cold-adapted kelp forests: stringent reductions in
 greenhouse gas emission reductions in line with the SSP1-1.9 scenario, and
 strategies to boost kelp’s thermal tolerance by at least 1-2°C. This dual
 approach is crucial to maintain 90% of the current suitable habitat of S.
 latissima and L. digitata, and 70% for A esculenta and L. hyperborea.
 Relying on mitigation or adaptation alone will likely be insufficient to
 maintain their historic range under projected climate change.
description: # Data from: Turning the tide: A 2°C increase in heat tolerance can halve
 climate change induced losses in four cold-adapted kelp species Access
 this repo on
 Dryad: [https://doi.org/10.5061/dryad.f4qrfj751](https://doi.org/10.5061/dryad.f4qrfj751) ## Description of the data and file structure Primary script *Kelpmodel_submit.R* collects occurrence data and environmental layers, produces a baseline SDM in the present, projects it in the future, and implements simulated tolerance increases from 1-5 degrees C. Each species results are a separate run of the script. The "outputs" folder is provided populated with the results associated with the manuscript as an example (results_species_2024-09-25_). Our results are provided and described in the published article and its supplements. Extension to species not included in our analysis is possible by updating the "*my_species*" variable and calculating an appropriate transition function via iterative review of the thermal response curves produced by the base and modified models. Extension to other environmental variables and/or climate change scenarios can be done within the script through the modification of the included queries to the [Bio-ORACLE ERDAP database](https://doi.org/10.1111/geb.13813) (details below). #### Turning\_the\_tide.zip contents:  **inputs** - raster layers comprising EEZs (*eez_all_sf.shp*), coastlines (coastLineRes005.tif), and marine ecoregions (*WCMC-036-MEOW-PPOW-2007-2012.shp*). A pruned brown algae dataset in R environment (*dataBrownAlgaePruned.RData*) format is also provided, as documented in [Assis et al., 2020](https://doi.org/10.1038/s41597-020-0459-x). **outputs** - per species and climate change scenario results, including associated processed layers and raw/filtered occurrences. Created and organized programmatically within script. Variables, including downloaded environmental layers and intermediate data structures, are preserved at the end of each run in a .RData file. Overviews of model performance and variable contribution as the model is modified for each tolerance increase are provided in .csv files within each transition folder. Any variable available through [Bio-ORACLE](https://www.bio-oracle.org/) in the present and future can be used and retrieved for modeling with the current framework of the script by updating variable names in the script. Layers retrieved from the Bio-ORACLE ERDDAP server in this way are stored as .nc files, but named randomly and are therefore deleted and retrieved with each run. Keys of variable names and units are available through Bio-ORACLE both via the [biooracler R package](https://github.com/bio-oracle/biooracler) and the main website. Primary results (figures of the modeling region, modeled suitability, binarized model suitability, area lost and recovered as .png files) are available within the scenario folder. Outputs are organized as: ``` ├───scripts ├───inputs ├───outputs │ ├─── │ │ ├───layers │ │ ├───occurrences │ │ ├───results__ │ │ │ ├─── │ │ │ │ ├───transition_1 │ │ │ │ ├───transition_2 │ │ │ │ ├───transition_3 │ │ │ │ ├───transition_4 │ │ │ │ └───transition_5 ``` **scripts** - *Kelpmodel_submit.R* reads in data, creates models (species and layers to be user specified), writes results to outputs folder. This is the only script required for analysis. If applying to new species, transition function values (manuscript section 2.4) must be determined via iterative review of the thermal response curves of the base and modified models. Some of the most computationally intensive portions of the script are associated with cross-validation and variable selection which need only be completed once per species. Commenting out these sections (labeled in R script) can substantially reduce run time for subsequent modeling efforts on the same species. #### Code compatibility information: The majority of analysis was performed in R version 4.4.0 (2024-04-24) with RStudio version 2023.9.0.463 Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.5 LTS in a cluster computing Linux environment, but collaborators successfully repeated the analysis on local machines running both Mac and Windows using R version 4.3.3 (2024-02-29 ucrt).