Subtle methodological variations substantially impact correlation test results in ecological time series
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Curated by eLife
eLife Assessment
This study presents a valuable in-depth comparison of statistical methods for the analysis of ecological time series data, and shows that different analyses can generate different conclusions, emphasizing the importance of carefully choosing methods and of reporting methodological details. The evidence supporting the claims, based on simulated data for a two-species ecosystem, is solid, although testing on more complex datasets could be of further benefit. This paper should be of broad interest to researchers in ecology.
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Abstract
Correlation analyses using ecological time series can indicate phenomena such as interspecific interactions or an environmental factor that affects several populations. However, methodological choices in these analyses can significantly impact the results, potentially leading to spurious correlations or missed true associations. In this study, we explore how different decisions affect the performance of statistical tests for correlations between pairs of time series in simulated two-species ecosystems. We show that when performing nonparametric “surrogate data” tests, both the choice of statistic and the method of generating the null distribution can affect true positive and false positive rates. We also show how seemingly closely related methods of accounting for lagged correlation produce vastly different false positive rates. For methods that establish a null model by simulating the dynamics of one of the two species, we show that the choice of species simulated can influence test behavior. Additionally, we identify scenarios where the outcomes of analyses can be highly sensitive to the initial conditions of an ecosystem, even under simple mathematical models. Our results indicate the importance of thoughtful consideration and documentation of the statistical choices investigated here. To make this work broadly accessible, we include visual explanations of most methods tested in an appendix.
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eLife Assessment
This study presents a valuable in-depth comparison of statistical methods for the analysis of ecological time series data, and shows that different analyses can generate different conclusions, emphasizing the importance of carefully choosing methods and of reporting methodological details. The evidence supporting the claims, based on simulated data for a two-species ecosystem, is solid, although testing on more complex datasets could be of further benefit. This paper should be of broad interest to researchers in ecology.
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Reviewer #1 (Public review):
Summary:
The manuscript investigates methods for the analysis of time series data, in particular ecological time series. Such data can be analyzed using a myriad of approaches, with choices being made in both the statistical test performed and the generation of artificial datasets for comparison. The simulated data is for a two-species ecosystem. The main finding is that the rates of false positives and negatives strongly depend on the choices made during analysis, and that no one methodology is an optimal choice for all contexts. A few different scenarios were analyzed, including analysis with a time lag and communities with different species ratios.
Strengths:
The paper sets up a clear problem to motivate the study. The writing is easy to follow, given the dense subject matter. A broad range of approaches …
Reviewer #1 (Public review):
Summary:
The manuscript investigates methods for the analysis of time series data, in particular ecological time series. Such data can be analyzed using a myriad of approaches, with choices being made in both the statistical test performed and the generation of artificial datasets for comparison. The simulated data is for a two-species ecosystem. The main finding is that the rates of false positives and negatives strongly depend on the choices made during analysis, and that no one methodology is an optimal choice for all contexts. A few different scenarios were analyzed, including analysis with a time lag and communities with different species ratios.
Strengths:
The paper sets up a clear problem to motivate the study. The writing is easy to follow, given the dense subject matter. A broad range of approaches was compared for both statistical tests and surrogate data generation. The appendix will be helpful for readers, especially those readers hoping to implement these findings into their own work. The topic of the manuscript should be of interest to many readers, and the authors have put in extra effort to make the writing as clear as possible.
Weaknesses:
The main conclusions are rather unsatisfying: "use more than one method of analysis", "be more transparent in how testing is done", and there is a "need for humility when drawing scientific conclusions". In fact, the findings are not instructions for how to analyze data, but instead highlight the extreme dependence of the interpretation of results on choices made during analysis. The conclusions reached in this study would be of interest to a specialized subset of researchers focused on the biostatistics of ecological data. Ending the article with a few specific recommendations for how to apply these conclusions to a broad range of datasets would increase the impact of the work. -
Reviewer #2 (Public review):
Summary:
This manuscript tackles an important and often neglected aspect of time-series analysis in ecology - the multitude of "small" methodological choices that can alter outcomes. The findings are solid, though they may be limited in terms of generalizability, due to the simple use case tested.
Strengths:
(1) Comprehensive Methodological Benchmarking:
The study systematically evaluates 30 test variants (5 correlation statistics × 6 surrogate methods), which is commendable and provides a broad view of methodological behavior.
(2) Important Practical Recommendations:
The manuscript provides valuable real-world guidance, such as the superiority of tailored lags over fixed lags, the risks of using shuffling-based nulls, and the importance of selecting appropriate surrogate templates for directional tests.
(3) …
Reviewer #2 (Public review):
Summary:
This manuscript tackles an important and often neglected aspect of time-series analysis in ecology - the multitude of "small" methodological choices that can alter outcomes. The findings are solid, though they may be limited in terms of generalizability, due to the simple use case tested.
Strengths:
(1) Comprehensive Methodological Benchmarking:
The study systematically evaluates 30 test variants (5 correlation statistics × 6 surrogate methods), which is commendable and provides a broad view of methodological behavior.
(2) Important Practical Recommendations:
The manuscript provides valuable real-world guidance, such as the superiority of tailored lags over fixed lags, the risks of using shuffling-based nulls, and the importance of selecting appropriate surrogate templates for directional tests.
(3) Novel Insights into System Dependence:
A key contribution is the demonstration that test results can vary dramatically with system state (e.g., initial conditions or abundance asymmetries), even when interaction parameters remain constant. This highlights a real-world issue for ecological inference.
(4) Clarification of Surrogate Template Effects:
The study uncovers a rarely discussed but critical issue: that the choice of which variable to surrogate in directional tests (e.g., convergent cross mapping) can drastically affect false-positive rates.
(5) Lag Selection Analysis:
The comparison of lag selection methods is a valuable addition, offering a clear takeaway that fixed-lag strategies can severely inflate false positives and that tailored-lag approaches are preferred.
(6) Transparency and Reproducibility Focus:
The authors advocate for full methodological transparency, encouraging researchers to report all analytical choices and test multiple methods.
Weaknesses / Areas for Improvement:
(1) Limited Model Generality:
The study relies solely on two-species systems and two types of competitive dynamics. This limits the ecological realism and generalizability of the findings. It's unclear how well the results would transfer to more complex ecosystems or interaction types (e.g., predator-prey, mutualism, or chaotic systems).
(2) Method Description Clarity:
Some method descriptions are too terse, and table references are mislabeled (e.g., Table 1 vs. Table 2 confusion). This reduces reproducibility and clarity for readers unfamiliar with the specific tests.
(3) Insufficient Discussion of Broader Applicability:
While the pairwise test setup justifies two-species models, the authors should more explicitly address whether the observed test sensitivities (e.g., effect of system state, template choice) are expected to hold in multi-species or networked settings.
(4) Lack of Practical Summary:
The paper offers great insights, but currently spreads recommendations throughout the text. A dedicated section or table summarizing "Best Practices" would increase accessibility and application by practitioners.
(5) No Real-World Validation:
The work is based entirely on simulation. Including or referencing an empirical case study would help illustrate how these methodological choices play out in actual ecological datasets.
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