talk · Thursday 24 September · 11:15–⁠11:30 · FORUM

Rethinking “absence” in biodiversity data collection and modeling

Rob Stevenson · FAIR data in practice - eDNA, modelling and biodiversity concepts

Recording time 2:59:29–3:12:43Open on Vimeo ↗

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The short versionSeparating observation from inference means recording 'detected' and 'not detected' rather than presence and absence.

Overview

What this was about

Rob Stevenson presented the outcome of a working group (with Abby Benson, Ruben Perez, Deborah Arlt, Yiming and Wesley Hochachka) on confusing 'absence' terminology. He argued presence and absence are asymmetric (presence is a fact, absence a question) and absence is only meaningful against an expectation frame such as a Humboldt Extension survey target. Surveying methodology and analysis texts, the group found the museum (presence-only) and biological survey (repeated, effort-based) communities use different data and models, with detectability as the unifying concept. The main outcome was the TDWG Executive's approval in May of changing Darwin Core occurrenceStatus values from present/absent to detected/not detected.

Why it matters. The occurrenceStatus vocabulary change affects how every data manager records and interprets non-detections, and clarifies terminology for distribution modelling.

Key ideas

In the room

  • Many overlapping absence terms exist in the literature (confirmed absence, zero count, pseudo-absence, etc.).
  • Presence and absence are asymmetric: presence is a fact, absence is a question.
  • Absence is meaningful only against a model of what should have been present; survey targets in the Humboldt Extension provide that frame.
  • Survey methodology books rarely define absence terms in data-sharing terms; Hill et al.'s handbook was most useful.
  • Museum data (presence-only, Bioclim/GARP/MaxEnt niche models) and survey data (repeated surveys, occupancy and hierarchical models) come from different communities.
  • Detectability, from mark-recapture, distance sampling and occupancy models, is the key clarifying concept.
  • Darwin Core occurrenceStatus changed from present/absent to detected/not detected.
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Notable moments

In their words

Transcript

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Programme abstract

Rethinking “absence” in biodiversity data collection and modeling

Rob Stevenson

The rapid growth of biodiversity data through GBIF and related sites has accelerated the development of species distribution models (SDMs) and occupancy models. Simultaneously, the literature has accumulated many partially overlapping terms related to “absence”, including absence record, confirmed absence, negative detection, zero count, and pseudo-absence. A working group with experience in biodiversity data collection, curation, and modeling met monthly for approximately 18 months to sort out this confusing terminology. Most biodiversity sampling compendia did not explicitly define absence-related terms. When they did, usage varied: some sources used presence-absence language, whereas others used detection-non-detection language. The occupancy-modeling literature provided a stronger conceptual framework for data collection (MacKenzie (2005). Here detection and non-detection characterize observations, whereas presence and absence are inferred ecological states that can be estimated probabilistically with repeated survey data. This perspective motivated a petition, introduced by members of our working group and others, to change the values of occurrenceStatus in the Humboldt Extension from “present/absent” to “detected/nonDetected.” SDMs use another set of absence-related terms, such as pseudo-absence, presence-pseudoabsence data, and true absence. For modeling, ordinary language is insufficient. These terms have technical meanings that depend on model class, data type, sampling assumptions, and historical context. During this innovative phase of model development, it seems difficult to create standardized terminology, MacKenzie, D. I. 2005. What are the issues with presence-absence data for wildlife managers? Journal of Wildlife Management 69: 849-860.

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