Takamitsu Ohigashi
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taka-ohi.bsky.social
Takamitsu Ohigashi
@taka-ohi.bsky.social
9 followers 15 following 1 posts
Postdoc (JSPS PD) at Tohoku University (currently studying at HKUST) | soil microbes, material cycling, eDNA, nonlinear time series analysis, etc.
https://taka-ohi.github.io/
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taka-ohi.bsky.social
www.nature.com/articles/s41...
Marine fishes experiencing high-velocity range shifts may not be climate change winners - Nature Ecology & Evolution
Abundance data for marine fish populations show that those shifting poleward rapidly due to climate change experience substantial population declines, suggesting that rapid range shifts are not suffic...
www.nature.com
March 27, 2025 at 11:42 AM
Reposted by Takamitsu Ohigashi
zzeynepersoy.bsky.social
📢 GLOSSAQUA is out in Ecology @esajournals.bsky.social. Big collaboration w/40+ coauthors led by myself, @chaevangelista.bsky.social & @ignasiarranz.bsky.social. It compiles global size spectra parameters from aquatic ecosystems globally👉: doi.org/10.1002/ecy.... #datapaper #aquaticecology 🌍🧪🧵(1/4)
Locations of aquatic studies included in the dataset (n = 135 studies) classified within eight biogeographic realms. The sizes of the circles are proportional to the number of studies per country in each biogeographic realm.
March 24, 2025 at 4:24 PM
Reposted by Takamitsu Ohigashi
esajournals.bsky.social
🆕 in Ecological Monographs' "Method" section: Inspired by work showing "a community is more than the sum of its parts," a new geometry-based measure of beta diversity captures changes in species interactions

📄A geometric approach to beta diversity
esajournals.onlinelibrary.wiley.com/doi/full/10....
Illustration of the geometric approach to beta diversity. Panel (A) shows 2 hypothetical metacommunities; the 2 communities of metacommunity 1 consist of species A only and species B only. The 3 communities of metacommunity 2 consist of species A only, species B only, and species A,B together, respectively. Panel (B) shows that classic measures of beta diversity assert that metacommunity 1 has a higher beta diversity than metacommunity 2. Panel (C) illustrates the key steps of our geometric measure: turning the metacommunities into matrix representations, a geometric embedding of the metacommunity, and measuring the beta diversity as the normalized hypervolume of the geometric object. Within this measure, metacommunity 2 has a lower beta diversity than metacommunity 2, opposite to the classic measures.
March 20, 2025 at 2:02 PM
Reposted by Takamitsu Ohigashi
ong8181.bsky.social
Our new preprint published! (My first Hong Kong paper!)

We developed a new eDNA primer set, named µCeta, to detect cetaceans. It can avoid amplification of non-target eDNA such as human and fishes, enabling more sensitive and efficient detection of cetacean eDNA.　www.biorxiv.org/content/10.1...
μCeta: a set of cetacean-specific primers for environmental DNA metabarcoding with minimal amplification of non-target vertebrates
Biodiversity monitoring is crucial for understanding ecosystem dynamics and species distributions, particularly in the context of anthropogenic impacts and climate change. Cetaceans, as key indicator ...
www.biorxiv.org
March 21, 2025 at 12:37 AM
Reposted by Takamitsu Ohigashi
frodsan.bsky.social
New #statistics #ecopubs @esajournals.bsky.social:

Predictive models are indeed useful for causal inference doi.org/10.1002/ecy....

Sounds like a response to 'Predictive models aren't for causal inference' doi.org/10.1111/ele....
The subject of investigating causation in ecology has been widely discussed in recent years, especially by advocates of a structural causal model (SCM) approach. Some of these advocates have criticized the use of predictive models and model selection for drawing inferences about causation. We argue that the comparison of model-based predictions with observations is a key step in hypothetico-deductive (H-D) science and remains a valid approach for assessing causation. We draw a distinction between two approaches to inference based on predictive modeling. The first approach is not guided by causal hypotheses and focuses on the relationship between a (typically) single response variable and a potentially large number of covariates. We agree that this approach does not yield useful inferences about causation and is primarily useful for hypothesis generation. The second approach follows a H-D framework and is guided by specific hypotheses about causal relationships. We believe that this has been, and continues to be, a useful approach to causal inference. Here, we first define different kinds of causation, arguing that a “probability-raisers-of-processes” definition is especially appropriate for many ecological systems. We outline different scientific “designs” for generating the observations used to investigate causation. We briefly outline some relevant components of the SCM and H-D approaches to investigating causation, emphasizing a H-D approach that focuses on modeling causal effects on vital rate (e.g., rates of survival, recruitment, local extinction, colonization) parameters underlying system dynamics. We consider criticisms of predictive modeling leveled by some SCM proponents and provide two example analyses of ecological systems that use predictive modeling and avoid these criticisms. We conclude that predictive models have been, and can continue to be, useful for providing inferences about causation.
March 13, 2025 at 2:12 PM