5️⃣ Influence of observed species?

As more species were added, elevational richness became more distinctly unimodal, converging toward the MDE predictions.

This effect also varies with extent and position of elevational gradients.

4️⃣ Is this just a random geometric effect?

We tested the Mid-Domain Effect (MDE) null model and found substantial deviations from its predictions.

This means plant distribution in the Himalayas is NOT a simple game of chance!

3️⃣ What did we find?

While species richness often showed complex non-linear unimodal (hump-shaped) patterns, simple linear decreasing pattern was also observed.

Patterns vary with extent and position of elevational gradients!

2️⃣ We compiled the elevational ranges of >1100 vascular plant species from three #ProtectedAreas — Morni Hills, Chail WLS, and Churdhar WLS. This contributes to knowledge gap for the #WesternHimalayas.

🌿 Where are greatest number of #PlantSpecies in #Mountains? 🏔️

Our recent study in @mdpiopenaccess.bsky.social explored elevational patterns of plant #SpeciesRichness. A quick thread 👇

#Biodiversity #Biogeography #Ecology #ElevationalGradients #Himalayas #ProtectedAreas

Direct effects are stronger than the indirect effects

Indirect effects are strongly mediated by water-energy dynamics than energy availability

Overall, our study supported:
1️⃣Climate tolerance hypothesis > 2️⃣Water-energy dynamics > 3️⃣Species-energy hypothesis

Climate is more important than the topographic heterogeneity at broader scales

Richness is regulated by multiple mechanisms involving direct indirect and interaction effects of environmental variables

Species richness seems to be regulated by 💦 liquid-water availability, i.e. interaction between temperature and water-deficit.

This year's contribution to Citizen Science 🌱👇