I am an Independent researcher (Spain), and I have recently developed a biodiversity model that is currently hosted on Zenodo.
Before moving forward, I would love to get some expert validation and feedback from this community. Specifically, I am looking to debate the methodology, data quality, and structure of the model. My ultimate goal is to prepare this work to be published as a Data Paper (potentially in the Biodiversity Data Journal).
I gave the paper a quick read, but I do not see how it is connected to biodiversity or that it can be published as a data paper. Could you explain it a little bit about how it interacts with biodiversity? Thanks!
Thank you very much for your time and your valuable feedback. I completely understand your observation. While the model is initially presented through the lens of human cognitive neuroscience, its underlying mathematical architecture is designed using complex systems principles, allowing for a direct and immediate translation into the fields of Cognitive Ecology and Biodiversity Conservation.
The interaction of my model with biodiversity is based on three critical ecological applications:
Modeling the Impact of Light Pollution (I_B(t)) on Wildlife: The denominator of the formula directly computes how environmental light interference degrades the sharpness of mental projection. In conservation ecology, this allows for the quantitative modeling of how artificial light at night (ALAN) disrupts the mental spatial maps of migratory birds, sea turtles, or nocturnal pollinators, actively preventing them from recalling and navigating their biological routes.
Quantifying Anthropogenic Stress and Human Disturbance (V_n(t)): The stochastic neuronal noise term parameterizes the physiological stress and fatigue experienced by species due to infrastructure noise, tourism, or habitat fragmentation. The model mathematically demonstrates that an animal under constant environmental stress (high V_n) undergoes “destructive memory retrievals” of its safe havens. In other words, chronic fear actively degrades the animal’s long-term memory regarding the location of water resources or shelters, accelerating population decline.
Optimizing Foraging Strategies and Survival (W(t) and S(t)): By integrating reward prediction errors into the past-accumulation engine, the formula serves to predict the migratory and foraging success of predators and gatherers. It allows researchers to simulate how the long-term memory of an animal cohort fluctuates when facing abrupt climate or habitat shifts.
Why can it be published as a Data Paper?
Because the practical value of this formula lies in its algorithmic implementation. My objective is to release this mathematical framework so that ecologists can plug in their own biological monitoring datasets (e.g., telemetry tracking data of migratory routes, satellite-derived light pollution maps, or wildlife blood cortisol levels). The equation then functions as a predictive computational engine to assess the cognitive health and survival fitness of endangered species.
I hope this perspective clarifies the profound connection between the model and biodiversity conservation. I remain entirely at your disposal for any further discussion.
You can find out more about data papers here: https://www.gbif.org/data-papers
Note that data papers are about primary data, not models. For example, many data papers describe occurrence datasets (an occurrence is the observation or collection of a given specimen at a given time and place).
Thank you so much for the clarification and for providing the link. I now clearly see the distinction. My work is indeed a mathematical and computational model, not a primary empirical dataset or an occurrence database, so it falls outside the scope of GBIF’s data papers.
I deeply appreciate your guidance and your time. Wishing you all the best as well!