Reply to Roff: Ecological readiness must evolve with AI

I thank Roff for extending the discussion on AI in ecology and agree that ecologists should actively shape how AI develops and is used (1). The growing use of AI in automated monitoring, remote sensing, species identification, and other data-intensive applications demonstrates its value across parts of ecology. My argument that ecology is “not yet ready” for AI (2), however, was not a call to delay its use or remain outside its development. Rather, it questioned whether the scientific and institutional practices surrounding rapidly expanding AI applications are developing quickly enough to ensure that computationally convincing outputs remain ecologically meaningful.

Technological adoption and scientific readiness are not equivalent. Importantly, readiness is also task-dependent: Success in AI-assisted detection, classification, or monitoring does not necessarily imply equivalent readiness for ecological inference or prediction under novel environmental conditions. Ecological observations are heterogeneous and unevenly distributed, relationships are context- and scale-dependent, and systems change through shifting baselines, feedbacks, historical contingencies, and environmental nonstationarity (2). Increasing model sophistication does not remove these constraints. Readiness therefore requires ecological expertise, transparent validation, interdisciplinary training, and institutional practices capable of identifying these boundaries and communicating uncertainty appropriately.

Roff rightly highlights the cost of underusing AI and proposes a “hybrid ecology” in which data-driven approaches complement mechanistic models, natural history, spatial ecology, and field experimentation (1). I see this as an important expression of readiness rather than an alternative to it. The urgency and scale of contemporary ecological change make AI increasingly valuable, but they also increase the consequences of poorly validated inference. AI can accelerate monitoring, identify patterns across heterogeneous observations, and guide field sampling and experimentation. Yet as AI increasingly influences where and what ecologists monitor, it may also shape the evidence on which subsequent ecological understanding is built. Existing spatial, temporal, or taxonomic biases could therefore be propagated through AI-guided research priorities. Ecological scrutiny must extend from model outputs to the processes through which AI influences knowledge generation itself.

Roff is also correct that readiness will partly emerge through use, failure, replication, standards, and institutional investment (1). I would add that readiness should therefore evolve alongside AI rather than represent a threshold ecology must cross before participation. Ecologists should help define meaningful validation, determine which uncertainties matter biologically, identify where predictions should not be transferred, and distinguish algorithmic performance from evidence for ecological inference. Shaping AI and strengthening ecological readiness are mutually reinforcing responsibilities. The goal is not simply greater AI adoption, but an ecology in which computational capability advances together with ecological theory, field knowledge, critical evaluation, and scientific accountability.

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