Identifying climate refugia for 30 Australian rainforest plant species,from the last glacial maximum to 2070

Landscape Ecology - Climate refugia—areas that remain suitable for species during periods of climate disruption—have played an important role in species persistence over time....     

Correction to: Individual based modelling of fish migration in a 2-D river system: model description and case study

Landscape Ecology - In the original publication of the article, the third author name has been misspelt. The correct name is given in this Correction. The original version of this article was revised.     

Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression

Landscape Ecology - Land surface temperature (LST) directly responds to incoming solar radiation and is strongly influenced by vertical urban structures, such as trees and buildings that provide...     

Prioritising sites for pollinators in a fragmented coastal nectar habitat network in Western Europe

Landscape Ecology - Habitat loss and fragmentation contribute significantly to pollinator decline and biodiversity loss globally. Conserving high quality habitats whilst restoring and connecting...     

The value of linking paleoecological and neoecological perspectives to understand spatially-explicit ecosystem resilience

Context

Predicting ecosystem resilience is a challenge, especially as climate change alters disturbance regimes and conditions for recovery. Recent research has highlighted the importance of spatially-explicit disturbance and resilience processes to long-term ecosystem dynamics. “Neoecological” approaches characterize resilience mechanisms at relatively fine spatio-temporal resolutions, but results are difficult to extrapolate across broad temporal scales or climatic ranges. Paleoecological methodologies can consider the effects of climates that differ from today. However, they are often limited to coarse-grained spatio-temporal resolutions.

Methods

In this synthesis, we describe implicit and explicit examples of studies that incorporate both neo- and paleoecological approaches. We propose ways to build on the strengths of both approaches in an explicit and proactive fashion.

Results

Linking the two approaches is a powerful way to surpass their respective limitations. Aligning spatial scales is critical: Paleoecological sampling design should incorporate knowledge of the spatial characteristics of the disturbance process, and neoecological studies benefit from a longer-term context to their conclusions. In some cases, modeling can incorporate non-spatial data from paleoecological records or emerging spatial paleo-data networks with mechanistic disturbance/recovery processes that operate at fine spatiotemporal scales.

Conclusions

Linking these two complementary approaches is a powerful way to build a complete understanding of ecosystem disturbance and resilience.

     

Digestibility of Quinoa (<Emphasis Type="Italic">Chenopodium quinoa</Emphasis> Willd.) Protein Concentrate and Its Potential to Inhibit Lipid Peroxidation in the Zebrafish Larvae Model

Quinoa protein concentrate (QPC) was extracted and digested under in vitro gastrointestinal conditions. The protein content of QPC was in the range between 52.40 and 65.01% depending on the assay used. Quinoa proteins were almost completely hydrolyzed by pepsin at pH of 1.2, 2.0, and 3.2. At high pH, only partial hydrolysis was observed. During the duodenal phase, no intact proteins were visible, indicating their susceptibility to the in vitro simulated digestive conditions. Zebrafish larvae model was used to evaluate the in vivo ability of gastrointestinal digests to inhibit lipid peroxidation. Gastric digestion at pH 1.2 showed the highest lipid peroxidation inhibition percentage (75.15%). The lipid peroxidation activity increased after the duodenal phase. The digest obtained at the end of the digestive process showed an inhibition percentage of 82.10%, comparable to that showed when using BHT as positive control (87.13%).