Blas M. Benito

Blas M. Benito

Data Scientist and Team Lead

Biome Makers Inc.


Hello there!

My name is Blas, and I am a spatial data scientist and engineer in AgTech, holding a PhD in computational ecology and an MSc in geographic information systems.

My expertise lies at the intersection of spatial and temporal modeling, soil and plant ecology, remote sensing, machine learning, and environmental dynamics and monitoring.

My work

I’m deeply passionate about crafting automated data and modeling pipelines to tackle complex environmental challenges.

Currently, I lead the Environmental Data Team at Biome Makers Inc. In this role, I research and develop cutting-edge smart farming technologies, create essential R packages to enhance our Data Science Department’s capabilities, and oversee the design and maintenance of an environmental data infrastructure to further empower our flagship product, BeCrop®.

My Tech Stack

My tech stack is built entirely on open-source tools: I rely on R and git+ GitHub for collaborative software development and version control. For pipeline design, I harness the power of targets, and to encapsulate code I employ renv and docker.

For GIS tasks, I turn to industry-standard tools like GRASS GIS, Quantum GIS, and PostGIS.

My data management and processing are handled by PostgreSQL, DuckDB, Apache Arrow, and Apache Spark.

Computationally-intensive pipelines find their home in my tiny home-cluster managed by slurm.

For developing and deploying REST APIs, I turn to plumber, while interactive apps are crafted with Shiny. My interactive reports come to life using either Rmarkdown or Quarto.

My Academic Journey

Before delving into AgTech, I honed my research and technical skills during a successful academic career in Computational Ecology. I worked in world-class labs in Spain ( IISTA and Maestre Lab), Denmark ( Jens-Christian Svenning Lab), and Norway ( EECRG).

My research primarily focused on unveiling the environmental drivers shaping the distribution of biological diversity in space and time. During this journey, I developed scientific R packages for various purposes, such as time-series comparison and analysis of lagged effects, spatial modeling with Random Forest, and ecological simulation.

Throughout this journey, I collaborated with 210 esteemed coauthors from 22 countries to publish 49 research papers in reputable peer-reviewed journals. To date, our collective work has garnered over 1600 citations. Notably, three of these papers have received recognition as ‘most downloaded papers’ in prestigious journals, and two have been honored as ‘editor’s picks’.

Beyond Work

In my leisure time, I cherish moments with my family, tinker on the piano with enthusiasm (regardless of the results!), embrace the serenity of the sea on my stand-up paddle board, and continue my passion for developing R packages.

Connect with Me

I’m always eager to connect with fellow data enthusiasts, researchers, and professionals. Feel free to connect with me on LinkedIn to explore potential collaborations and discussions within our shared field.


  • Spatial Data Science and Engineering
  • Ecology of the soil microbiome
  • Soil/crop mapping and modeling
  • Machine Learning and Remote Sensing in AgTech
  • Scientific code development
  • Automated data and modeling pipelines


  • Ph.D. in Computational Ecology, 2006 - 2009

    University of Granada

  • UNIGIS International Masters Degree in Geographical Information Sys-tems, 2007 - 2009

    University of Girona

  • Masters Degree in Management and Environmental Auditing, 2005 - 2006

    University of Cadiz

  • Degree in Biology, 1999 - 2003

    University of Granada



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Data Scientist and Team Lead

Biome Makers Inc.

Jan 2022 – Present US and Spain
  • Founder and lead of the Environmental Data Team.
  • Design and development of an automated Environmental Data Infrastructure to enhance the company flagship products.
  • Development of 5 R packages to improve the workflow within the Data Science department.
  • Introduced remote-sensing as a tool for product research and development.
  • Acquired the skillset to put models and workflows in production.

Staff researcher

University of Alicante

Jan 2020 – Dec 2021 Alicante, Spain
  • Published 15 papers in peer-reviewed international journals.
  • One paper received a “Most Downloaded Paper Award”.
  • Developed the R package spatialRF for spatial modelling, with 8000 downloads.

Invited Instructor

Stockholm University

Jan 2017 – Dec 2017 Stockholm, Sweden
  • Designed and instructed the post-graduate course “Practical Introduction to Species Distribution Modelling” (20 hours, 20 students).

Staff researcher

University of Bergen (Norway)

Sep 2016 – Aug 2019 Bergen, Norway
  • Executed a €199000 project by the Norwegian National Science Foundation (NFR).
  • Published 6 papers in peer-reviewed international journals.
  • One paper received the Most Downloaded Paper Award and was highlighted as an Editor’s Pick by the Journal
  • Developed 3 R packages with a total of 53000 downloads for time-series comparison and analysis.
  • Enhanced my skills in time-series analysis and developer of R packages.

Staff researcher

Aarhus University

Apr 2014 – Sep 2016 Aarhus, Denmark
  • Published 8 papers in peer-reviewed international journals.
  • One paper received the “Most Downloaded Paper Award” and was highlighted as an Editor’s Pick in Science.
  • Executed a €300000 research project by the AU-Ideas Foundation (Aarhus University).
  • Expanded considerably my international collaboration network.
  • Contributed positively to the success of a world-class laboratory.

Invited Instructor

Jan 2011 – Dec 2019 Madrid, Spain
  • Designed and instructed 9 editions of the post-graduate course “Workshop in Ecological Niche Modelling” (184 hours, 225 students).
  • My lectures were recorded and made available in the outreach plataform of the National Research Council of Spain (CSIC).

Staff researcher

Institute for Earth System Research

May 2010 – Apr 2014 Granada, Spain
  • Patented ‘MODELER, a model repository as knowledge based for experts’. Registration code 201299900779498, Expedient Number: GR-188-12.
  • Executed a €12000 contract with the Andalusian Government for plant diversity mapping.
  • Executed a €8000 contract with the Andalusian Government to enhance their Biodiversity Data Infrastructure.
  • Executed a €250000 grant to study the effects of Climate Change on the flora of Sierra Nevada National Park.
  • Published 11 papers in peer-reviewed international journals.
  • Designed and instructed the Master’s course ‘Ecoinformatics’ (60 hours, 25 students).
  • Instructed 3 undergraduate ecology courses (40 hours, 200 students).
  • Enhanced my skills as independent researcher.
  • Participated in an International Consortium to model and protect the plant diversity in Central America.

PhD student

University of Granada

May 2006 – Apr 2010 Granada, Spain
  • Obtained a 120.000€ PhD grant (Andalusian Government).
  • Completed a PhD in Computational Ecology.
  • Published 9 papers in national and peer-reviewed international journals.
  • Completed a Master’s in Geographic Information Systems.
  • Completed a Master’s en Environmental Auditing.
  • Developed technical skills in GIS and R programming.
  • Designed and instructed a full Master’s course on Geographic Information Systems (60 hours, 25 students).
  • Instructed two undergraduate courses (25 hours, 120 students) .


R programming


Geographic Information Systems


Data Analysis and Reporting


Remote Sensing


Machine Learning & Statistics


Team leadership


Data Engineering




Problem Solving




Research & Development


Project Management




R package collinear

R package for multicollinearity management in data frames with numeric and categorical variables.

R package spatialRF

R package for spatial regression with Random Forest

R package distantia

R package to compare multivariate time-series.

R package memoria

R package to assess ecological memory in multivariate time-series.

R package virtualPollen

R package to simulate pollen production of mono-specific tree populations over millennia.

Recent Posts

Recent Publications

Quickly discover relevant content by filtering publications.

Species Distribution Models predict abundance and its temporal variation in a steppe bird population.

Habitat Suitability Index (HSI) derived from Species Distribution Model (SDM) has been used to infer or predict local demographic properties such as abundance for many species. Across species studied, HSI has either been presented as a poor predictor of abundance or as a predictor of potential rather than realized abundance. The main explanation of the lack of relationship between HSI and abundance is that the local abundance of a species varies in time due to various ecological processes that are not integrated into correlative SDM. To better understand the HSI-abundance relationship, in addition to the study of the association between HSI and mean abundance, we explored its variation over time. We used data from 10-years monitoring of a Houbara bustard (Chlamydotis undulata undulata) population in Morocco. From various occurrence data we modelled the HSI. From (independent) count data we calculated four local abundance indices: mean abundance, maximum abundance, the temporal trend of abundance and the coefficient of variation of abundance over the study period. We explored the relationship between HSI and abundance indices using linear, polynomial and quantile regressions. We found a triangular relationship between local abundance (mean and maximum) and HSI, indicating that the upper limit of mean and maximum abundance increased with HSI. Our results also indicate that sites with the highest HSI were associated with least variation in local abundance, the highest variation being observed at intermediate HSI. Our results provide new empirical evidence supporting the generalization of the triangular relationship between HSI and abundance. Overall, our results support the hypothesis that HSI obtained from SDMs can reflect the local abundance potentialities of a species and emphasize the importance of investigating this relationship using temporal variation in abundance.

Human practices behind the aquatic and terrestrial ecological decoupling to climate change in the tropical Andes.

Anthropogenic climate change and landscape alteration are two of the most important threats to the terrestrial and aquatic ecosystems of the tropical Americas, thus jeopardizing water and soil resources for millions of people in the Andean nations. Understanding how aquatic ecosystems will respond to anthropogenic stressors and accelerated warming requires shifting from short-term and static to long-term, dynamic characterizations of human-terrestrial-aquatic relationships. Here we use sediment records from Lake Llaviucu, a tropical mountain Andean lake long accessed by Indigenous and post-European societies, and hypothesize that under natural historical conditions (i.e., low human pressure) vegetation and aquatic ecosystems’ responses to change are coupled through indirect climate influences—that is, past climate-driven vegetation changes dictated limnological trajectories. We used a multi-proxy paleoecological approach including drivers of terrestrial vegetation change (pollen), soil erosion (Titanium), human activity (agropastoralism indicators), and aquatic responses (diatoms) to estimate assemblage-wide rates of change and model their synchronous and asynchronous (lagged) relationships using Generalized Additive Models. Assemblage-wide rate of change results showed that between ca. 3000 and 400 calibrated years before present (cal years BP) terrestrial vegetation, agropastoralism and diatoms fluctuated along their mean regimes of rate of change without consistent periods of synchronous rapid change. In contrast, positive lagged relationships (i.e., asynchrony) between climate-driven terrestrial pollen changes and diatom responses (i.e., asynchrony) were in operation until ca. 750 cal years BP. Thereafter, positive lagged relationships between agropastoralism and diatom rates of changes dictated the lake trajectory, reflecting the primary control of human practices over the aquatic ecosystem prior European occupation. We interpret that shifts in Indigenous practices (e.g., valley terracing) curtailed nutrient inputs into the lake decoupling the links between climate-driven vegetation changes and the aquatic community. Our results demonstrate how rates of change of anthropogenic and climatic influences can guide dynamic ecological baselines for managing water ecosystem services in the Andes.

Density-dependence of reproductive success in a Houbara bustard population.

Although density-dependent processes and their impacts on population dynamics are key issues in ecology and conservation biology, empirical evidence of density-dependence remains scarce for species or populations with low densities, scattered distributions, and especially for managed populations where densities may vary as a result of extrinsic factors (such as harvesting or releases). Here, we explore the presence of density-dependent processes in a reinforced population of North African Houbara bustard (Chlamydotis undulata undulata). We investigated the relationship between reproductive success and local density, and the possible variation of this relationship according to habitat suitability using three independent datasets. Based on eight years of nests monitoring (more than 7000 nests), we modeled the Daily Nest Survival Rate (DNSR) as a proxy of reproductive success. Our results indicate that DNSR was negatively impacted by local densities and that this relationship was approximately constant in space and time: (1) although DNSR strongly decreased over the breeding season, the negative relationship between DNSR and density remained constant over the breeding season; (2) this density-dependent relationship did not vary with the quality of the habitat associated with the nest location. Previous studies have shown that the demographic parameters and population dynamics of the reinforced North African Houbara bustard are strongly influenced by extrinsic environmental and management parameters. Our study further indicates the existence of density-dependent regulation in a low-density, managed population.