Development of tree snail protection enclosures: From design to implementation

Reports were scanned in black and white at a resolution of 600 dots per inch and were converted to text using Adobe Paper Capture Plug-in.The Hawaiian land snails in the endangered, endemic genus Achatinella have experienced major declines in population and distribution over the last 100 years. Threats to Achatinella today include invasive, non‐native predators (Euglandina rosea, Rattus rattus and Trioceros jacksonii), habitat degradation due to human disturbance and possibly climate change, and historically, collection by humans. The O‘ahu Army Natural Resources Program (OANRP) is required to stabilize select remaining populations of A. mustelina. Stabilization goals are to maintain 300 mature snails at eight managed sites and control threats within sites. This report describes OANRP efforts to combat invasive predators by means of predator‐free and ‐proof snail enclosures. A couple of prior attempts at excluding predatory snails were marginally successful but the identification of additional predators required substantial additional barriers. The design and construction of the enclosure at Pu‘u Hapapa is used as a case study. This report includes detailed information on the physical development of predator‐proof barriers, construction and costs. Additional needs for monitoring and maintenance, predator removal, Achatinella reintroduction, Achatinella population monitoring, and habitat improvement were also developed.Funded by: U.S. Army via U.S. Army Corps of Engineers Cooperative Agreement W9126G-10-2-001

Dominance and rarity in tree communities across the globe: Patterns, predictors and threats

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species? Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large‐scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large‐scale management and conservation practices

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions

Metadata html file

This is a metadata html file that was composed using R Markdown and executes R code. The required demographic parameter estimates can be uploaded from the associated Rda files associated with this archived Dryad Digital Repository http://doi:10.5061/dryad.4k7d5n7 (Bialic-Murphy et al. 2019). This file uses parameter coefficients calculated in a separate R script (not provided in our supplement) and builds annual integral projection models from 2010-2016 for four treatments: A) deer access and garlic mustard ambient, B) deer access and garlic mustard weeded, C) deer exclusion and garlic mustard ambient, and D) deer exclusion and garlic mustard weeded (i.e., removal of both stressors). Using the component matrices of the discretized kernel matrix (K=P+F), this script generates the annual population growth rates, the cumulative growth rates from 2010 to 2016 (λc), and the time-averaged geometric mean growth rate, which captures the time-averaged population growth rate over a single transition year (λper year = sixth root of λc)

Metadata and associated R code

This file uses parameter estimates calculated in a separate R script (not provided in our supplement) and builds three integral projection models (one at each level of deer impact). Using the component matrices of the discretized kernel matrix K=P+F+C, this script also projects even-aged cohorts of either newborn seedlings or clones using matrix P as an individual-based model. This document displays the R code we used to generate all demographic estimates reported in our article and also most of the figures. The required demographic parameters are listed in two comma-separated value files (.csv) that are included in with this supplement. Additionally, we included the output from the individual-based model projections to expedite computation. They are saved as R objects in R data files (.Rdata)

R data of individual-based model projections for sexually produced offspring

Section IV of the metadata HTML file

The reproductive coefficients

The reproductive coefficients csv file that is loaded in section 1 of the HTML file

Cyrtandra_dentata_matrices_2010_2014

This file contains mean transition matrices from 2010–2011, 2011–2012, 2012–2013, and 2013–2014 from a geographically isolated population of a long lived shrub, Cyrtandra dentata, from the Kahanahāiki Management Unit (36 ha), located in the northern Wai‘anae Mountain Range, on the island of O‘ahu (21° 32’ N, -158°12’ W

P.array2014

2014-2015 demographic parameter coefficients for Trillium for four treatments (see metafile)