# How to optimally allocate sampling effort in experimental ecology

## TL;DR

This paper explores the best way to distribute limited sampling resources in ecological experiments to accurately predict environmental responses.

## Contribution

The study introduces a data-driven approach to determine optimal sampling strategies for ecological gradients.

## Key findings

- Unreplicated, systematic sampling is best for unknown response patterns.
- Replication improves accuracy when prior knowledge of simple response shapes exists.
- Simulations help identify optimal sampling strategies for environmental gradients.

## Abstract

A major aim of experimental ecology is to quantify responses to environmental change. Study designs which optimally capture response patterns are currently debated. A key point in the discussion is how a limited total number of samples should ideally be allocated to replication versus the number of locations along the environmental gradient. Here, we assess how to optimally allocate sampling effort for maximizing prediction accuracy in gradient designs. For this we performed artificial data simulations for different sampling approaches with or without a priori knowledge of the underlying patterns, and applied a set of commonly observed response shapes. Overall, unreplicated sampling with equidistant, systematic placement along the gradient of interest at as many locations or levels as affordable turned out to be the best approach for unknown response shapes. Replication was found to be beneficial when a priori knowledge exists about the underlying, simple (e.g. linear or humped) response shape.

The online version contains supplementary material available at 10.1038/s41598-026-38541-4.