Do richness and rarity hotspots really matter for orchid conservation in light of anticipated habitat loss?

Abstract

AimThe aim of this analysis was to identify strategies that will maximize efficiency and effectiveness in conservation planning. As many orchids are threatened with extinction for various reasons, our primary objective was to combine hotspots analyses with stochastic extinction modelling to highlight possible conservation priorities for Lepanthes spp. (Orchidaceae) based on patterns of richness, rarity and threat. Our subsequent objective was to identify potential conservation surrogates and variables that are the best predictors of extinction probabilities. The ultimate goal was to determine which factors should be emphasized in conservation planning to prevent species extinctions.Location Latin America; the Caribbean.Methods We used herbarium records and ArcGIS to map the distribution of Lepanthes spp. and to identify hotspots of richness and rarity. We forecasted extinction patterns with Koopowitz's stochastic extinction model and calculated extinction probabilities in each country. We used a randomForest regression model in R to assess the importance of richness, rarity and threat for explaining extinction probabilities.Results Hotspots of Lepanthes richness and rarity occurred in north-western South America and southern Central America and largely overlapped with each other. The highest extinction probabilities occurred in northern Central America, Haiti and Ecuador, and generally, hotspots of richness and rarity did not correspond with patterns of threat. Habitat loss was the most important variable for explaining extinction probabilities, followed by measures of rarity.Main conclusions Conservation efforts will be most efficient in richness and rarity hotspots, and because they overlap, rarity hotspots could act as surrogates for protecting overall Lepanthes diversity. Hotspots rarely occurred in the most threatened areas, and therefore, conservation efforts are more urgent in non-hotspot areas. Conservation efforts will be most effective if they combine ex situ strategies in locations with high habitat conversion rates with reservation strategies in rarity and richness hotspots, particularly where they overlap.

Contribution of rare and common species to subterranean species richness patterns

“…First, preserving ecosystem functions (Harvey, Gounand, Ward, & Altermatt, ) requires additional understanding what role in ecosystems play rare and common species, as well understanding interactions between different taxa (e.g., beetles from families Leiodidae vs. Carabidae) and ecological groups (e.g., aquatic vs. terrestrial). Second, an optimal conservation strategy should incorporate all available information into an analytic framework that will, in addition to species richness consider alternative metrics of biodiversity, such as rarity, weighted endemism, β diversity, and their relation to threat (Crain & Tremblay, ; Myers, Mittermeier, Mittermeier, da Fonseca, & Kent, ; Yu et al, ; Zhao, Li, Liu, & Qin, ). This analytic framework based on multiple criteria would possible consider common species, and not only rare species, in conservation planning of subterranean habitats.…”

Orchid conservation in the biodiversity hotspot of southwestern China

“…; Cozzolino & Widmer ; Jacquemyn et al. ; Crain & Tremblay ), possibly because of their mycorrhizal specificity, pollinator specialization, and germination limitation (Gravendeel et al. ; McCormick & Jacquemyn ).…”

Spatial Patterns of Irradiance and Advanced Reproduction along a Canopy Disturbance Severity Gradient in an Upland Hardwood Stand

“…Acer rubrum was not a primary nearest neighbor, but was a species of interest (i.e., part of the "Quercus-to-Acer" transition) therefore it was also analyzed to determine its spatial dependence. Getis-Ord Gi* was used to find the extent to which each feature was surrounded by similar high or low values to determine patterns of distribution (i.e., clusters; [79]. All spatial data were examined at three resolutions: 5ˆ5 m, 10ˆ10 m, and 20ˆ20 m, to observe how patterns changed across spatial scale.…”