The package, called Temporal Altitudinal Biogeographic Shifts, or tabs, is an R tool designed to reconstruct how biogeographic systems bounded by an altitudinal range have changed their spatial configuration over time. Its developers describe it as a globally applicable, reproducible workflow for modelling shifts in both terrestrial and marine systems in response to climate, topography, and geological dynamics. The key insight behind tabs is that many of the planet’s most important biogeographic systems can be defined by altitude: islands emerge where land rises above sea level, alpine biomes exist above the forest line, and coral reefs and shelf seas occupy specific depth ranges below the surface. By intersecting topographic data with curves describing how these altitudinal thresholds shifted through time, tabs can redraw the boundaries of such systems for any moment in the past or future.

Technically, the core of the package is a single function, reconstruct(), which operates on six main input parameters. The first, region, defines the area of interest, and tabs offers remarkable flexibility here: users can draw an extent interactively, supply coordinates, point to a local spatial file, pass an R spatial object, or simply type a name. In total, 32,255 region name-definitions are built in, covering islands, archipelagos, countries, tectonic plates, and mountains. The second parameter, topo, provides the topographic and bathymetric model, defaulting to the GEBCO 2024 global compilation at a resolution of 30 arc seconds. The third, curve, expresses the relative shift in altitude over time, such as a eustatic sea-level curve or the altitude of the upper forest line. The fourth, iso, defines the altitudinal offset from the curve, capturing the vertical range of the system being modelled, for instance the 0 to 30 meters below sea level where corals can grow. The fifth, correction, accounts for local topographic changes such as tectonic uplift and subsidence, and the sixth, reclabs, names each reconstructed shape.

What sets tabs apart from earlier workflows is its treatment of geophysical complexity. Many previous studies relied on uniform global eustatic sea-level curves, ignoring the fact that regional sea levels deviate substantially from the global average. Ice sheets deform the crust beneath and around them and exert gravitational pull on the oceans, so shorelines near former ice masses experienced very different sea-level histories than the tropics. tabs includes a global spatio-temporal sea-level curve at 0.2-degree spatial resolution that captures these regional variations induced by crustal deformation and gravitational forces. The package also allows topographic corrections based on local geological conditions. In the tectonically active Ionian and Aegean seas, for example, crustal uplift and subsidence rates range from 1 millimeter per year to minus 1 millimeter per year, which can translate into an absolute topographic change of 26 meters over 26,000 years, enough to submerge or expose entire islands.

The reconstruction itself works through a simple but powerful geometric operation. For each timestamp, tabs calculates which topographic pixels lie above the sum of the curve and correction values, producing what the developers call shapes: the spatial configurations of the biogeographic system at that moment. Emerging shapes are labelled automatically by identifying the highest point within each shape and intersecting it with labelling datasets, including the Global Shoreline Vector, the Global Mountain Biodiversity Assessment Mountain Inventory, and the GeoNames database. Each shape is assigned a unique identifier and spatial attributes such as area, coordinates, the curve and offset values, and a record of all reconstructed shapes it intersects with across time steps. This structure allows researchers to track how present-day islands merged into larger paleo-islands during low sea levels, or how a single alpine biome fragmented into isolated sky islands during warm periods.

The package ships with an impressive array of built-in datasets and curves. Running the setup() function downloads roughly 15 gigabytes of data, including the global topographic model, several sea-level curves spanning from 3 million years ago to the present, and even future projections based on four IPCC scenarios out to the year 2100. There is also a curve for the Northern Andes derived from the long fossil pollen record of Funza, Colombia, which reconstructs the altitude of the upper forest line over the last million years. Outputs are generated in standardised, open formats, including GeoTIFF rasters and GeoPackage vectors, and can be exported as directories, zip archives, or R objects. An explore() function generates interactive maps so users can visually compare reconstructed shapes to their present-day distributions.

Two case studies showcase the tool’s versatility. In the first, the developers reconstructed the coastlines of the Sporades archipelago in Greece since the Last Glacial Maximum, incorporating a correction raster for local uplift and subsidence. The results reveal that most islands within the archipelago were connected between 25,000 and 18,000 years ago. Twelve of the 18 present-day islands were interconnected 24,000 years ago, forming a larger Skopelos island group, while the other six formed the Skantzoura group 14,000 years ago. Intriguingly, four paleo-islands that no longer exist today emerged within the Sporades during the reconstruction, three of which merged with the two main island groups and one that existed briefly as a separate island 19,500 years ago. Such findings matter because island connectivity during glacial periods influenced species migrations, gene flow, and colonisation dynamics that still echo in present-day biodiversity patterns.

The second case study transported the analysis to the Venezuelan Andes, where the developers modelled the extent of the páramo, the high-altitude alpine biome above the upper forest line, over the last million years. Using decile statistics of the Funza forest-line curve, they quantified how much time the páramo spent above different altitudinal thresholds. The results are striking: the present-day upper forest line, standardised to 3,250 meters above sea level, falls within the upper decile, meaning the current páramo extent of 2,203 square kilometers is among the smallest of the last million years. Only for roughly 12,000 years, or 1.2 percent of the time, was the forest line higher and the páramo smaller. Half of the time, the forest line sat below 2,750 meters, making the páramo 1.84 times larger than today, and for 10 percent of the time it dropped as low as 2,213 meters, producing an extent 3.4 times the present-day area. In other words, today’s alpine islands in the sky are near their historical minimum, a sobering baseline as warming pushes forest lines further upslope.

The developers are candid about the tool’s limitations and trade-offs. Reconstruction speed depends on complexity: simple global models run quickly, while high-resolution local topographic models with spatio-temporal curves and correction rasters take longer but yield more accurate results. Resolution matters critically for labelling, as narrow straits like the 40-meter channel separating Evia from mainland Greece will not be distinguished without fine-grained topographic data. Automated labelling currently works only for shorelines and mountain ranges, and preparing correction inputs for tectonically active regions requires literature screening and data preparation by the user. Future developments aim to integrate global shoreline reconstructions, databases of topographic change rates, additional isolation metrics such as distance between shapes and timing of isolation, and publication-ready visualisations. The team also hopes to link tabs with complementary tools like PleistoDist, which quantifies Pleistocene sea-level effects on islands, and DAISIE, which models island biota change through colonisation, speciation, and extinction rates.

For a field long dominated by snapshot reconstructions, the arrival of a standardised, user-friendly workflow for continuous spatio-temporal modelling represents a genuine leap. By capturing the nuances of continuously changing environments rather than isolated moments, tabs promises a more complete understanding of how past climates sculpted present-day biodiversity, and how future change may redraw the maps again. The package is available on CRAN, with its development version and all default datasets openly archived, lowering the barrier for researchers anywhere on the globe to press the tab key and watch islands rise, merge, and drown across deep time.

Subject of Research: Biogeography

Article Title: Temporal Altitudinal Biogeographic Shifts ( tabs ): R package for reconstructing biogeographic shifts in terrestrial and marine systems over time

Article References: De Groeve, J., Rijsdijk, K. F., Rentier, E. S., Flantua, S. G. A., & Norder, S. J. (2025). Temporal Altitudinal Biogeographic Shifts (tabs): R package for reconstructing biogeographic shifts in terrestrial and marine systems over time. Frontiers of Biogeography, 18, Article e151677. https://doi.org/10.21425/fob.18.151677

Image Credits: AI Generated

DOI: 10.21425/fob.18.151677

Keywords: biogeography, R package, sea level, alpine biomes, island biogeography, climate change, tabs, Temporal, Altitudinal, Biogeographic, Shifts, package

The importance of soil pH in agriculture cannot be overstated, as it is often referred to as the “master variable” because it influences nearly every aspect of farm productivity, from nutrient availability to plant growth. However, traditional methods for tracking pH have significant limitations. Professor Gregory Whiting, who leads the Boulder Experimental Electronics and Manufacturing Laboratory (BEEM Lab) at CU Boulder, noted that conventional pH sensors are often ill-suited for soil environments. These traditional devices typically feature fragile glass bulbs that require frequent recalibration. The measurements provided by these sensors drift over time and vary across different environments, necessitating cumbersome manual calibration that is impractical for continuous field monitoring.

To overcome these challenges, the research team pivoted toward a new type of electronic sensor that utilizes a pH-sensitive dye called alizarin to provide more dependable readings. While the underlying technology is not entirely new, with scientists worldwide having studied it for years and demonstrated its efficacy in simple settings, a critical question remained unresolved: whether the sensor could reliably monitor real soil outside of laboratory conditions for weeks or months. The CU Boulder team aimed to bridge the gap between laboratory proof-of-concept and practical agricultural application by testing the device in diverse, realistic soil environments.

The study, led by PhD student Juan Cisneros Barba, involved burying a series of electrochemical sensors in various types of soil to assess their performance. The researchers selected soil samples that differed in organic matter content, with some being more sandy or muddy. To mimic the changing environmental conditions encountered in actual fields, the team included both compacted and uncompacted soils. This rigorous testing protocol was designed to evaluate the sensor’s robustness against the variability inherent in agricultural landscapes, ensuring that the device could function effectively regardless of the specific soil composition or physical structure.

In addition to testing the sensor’s response to different soil types, the team improved the original design to enhance its manufacturability and practicality. They transitioned to a printed circuit board-based design, which is easier to produce at scale. This redesign was paired with an inexpensive, field-deployable readout system, making the technology more practical for distribution and outdoor use. The goal was to create a device that was not only scientifically accurate but also economically viable and user-friendly for farmers who may lack access to sophisticated laboratory equipment or technical support.

The results of the field and laboratory tests exceeded the team’s expectations. Across the multiple soil types tested, the redesigned sensor delivered reliable, continuous pH measurements over a period of several months. A multi-month outdoor monitoring test in soil demonstrated stable performance without the need for constant recalibration. This stability is a significant improvement over traditional sensors, which often require frequent manual adjustments to maintain accuracy. The findings suggest that the new sensor can maintain its integrity and precision in the harsh and variable conditions of an agricultural field, providing a consistent data stream for growers.

Professor Whiting emphasized that the primary advantage of this new technology is its usability. He stated that the devices cost very little to manufacture, function with minimal human effort, and are compatible with various soil types. These characteristics allow the technology to be genuinely useful in farm settings, where cost and ease of use are critical factors for adoption. By reducing the barrier to entry for continuous soil monitoring, the researchers hope to enable a broader range of farmers to access real-time data on their soil health, thereby improving the overall management of agricultural land.

The study was published in the journal Scientific Reports, marking a significant contribution to the field of agricultural technology. It represents the latest development in a series of recent discoveries by Whiting and his team, whose work is helping farmers listen to soils, monitor living plants, and gather important information for future farms. The research highlights a shift toward more integrated and continuous monitoring systems in agriculture, moving away from sporadic, manual sampling toward automated, real-time data collection. This approach has the potential to transform how farmers interact with their land, providing insights that were previously inaccessible or too costly to obtain.

By enabling continuous monitoring of soil pH, this technology could help growers detect changes in soil conditions sooner, allowing for more timely and effective interventions. Whiting noted that in many cases, by the time farmers discover that something is wrong with their soil or plants, the window for effective treatment may have already passed. Continuous monitoring offers a way to mitigate this risk by providing early warnings of potential issues. This proactive approach to soil management could lead to more sustainable agricultural practices, improved crop yields, and greater food security. The development of such low-cost, durable sensors represents a significant step toward making advanced soil monitoring accessible to a wider audience of farmers and agricultural professionals.

Subject of Research: New technology could help farmers keep better tabs on soil health

Article Title: New technology could help farmers keep better tabs on soil health

Article References: New technology could help farmers keep better tabs on soil health. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: technology, help, farmers, keep, better, tabs, soil, health, scientific research