Developing a global atlas of ungulate mi-grations will require unprecedented collabo-ration to assemble the required knowledge,data, and analytical tools.
Does the mapping of migration actually lead to better population outcomes, or is the protection of mapped migration corridors more important?
Lost migrations also need to be invento-ried and mapped by leveraging Indigenous,local, and expert knowledge.
Local/indigenous knowledge is an interesting component to further understand lost migration patterns. I love the connection of "living history" here.
When animal tracks are overlaidon dynamic maps of seasonal resources,they reveal diverse migration patterns, fromlong-distance movements across climaticgradients, to shorter elevational movementsto access alpine habitats.
I think this would be interesting to overlap with research on seed dispersal, and if the migration area vs non-migratory areas differ in plant diversity, composition, or functional traits.
(a) SPEI waterbalances are normalized differences between precipitation and potentialevapotranspiration (PET). Values show the SPEIbase version 2.9 of SPEI, whichquantifies PET by the FAO-56 Penman-Monteith model. Positive (or negative)SPEI values indicate water balances that are wetter (or drier) than those observednear the study site throughout the reference period, which was January 1901 toDecember 2022. When estimating resistance and recovery to the observedextreme wet event, Ye, Ye+1 , and Ye+2 were respectively based on the averageproductivity levels in 2002, 2003, and 2004. This was the most extreme eventof the time series and had normal years just before and after it. When estimatingresistance to the observed extreme drought in 2021, Ye was based on productivityin 2021. In all cases, Yn was based on productivity averaged across all normalyears that did not follow an extreme event: 1996, 2001, 2005, 2007, 2009, 2012,2013, and 2018
shows recovery only contributes a little to predictive information
monotonicrecovery
definition: a process where a system, signal, or property returns to a normal or optimal state in a steady, continuous direction meaning always improving or increasing without reversing, dipping, or fluctuating midway.
The erroron these predictions decreased with the number of years of data andat least 17 years of data were needed before the relationship becamestatistically significant at the ecosystem level
this is such a long time, I wonder if these results can be found in shorter time periods with other prediction models?
At the species level,resilience was positively correlated with resistance, as predicted, butnot significantly with recovery (Fig. 2b)
its interesting that their recovery speed is not stronger or even connected to their overall resilience and resistance to disturbance. these things seem like they would be correlated!
since 1996
what an amazing study to have data over this much time!
CNNs have been widely applied in AMP prediction due to their ability to detect localsequence motifs and spatial patterns associated with antimicrobial activity
Can CNNs identify new membrane-interaction motifs that researchers have not previously discovered?
When integrated with predictive models and wet-lab valida-tion, RL-based design pipelines significantly accelerate the development of novel peptidessuitable for combating multidrug-resistant ESKAPE and fungal pathogens
How much time and money does using the predictive models save when they need to be validated with wet-lab experiments? Is it THAT much more cost effective?
Is this all Biofilm? Including healthy bacteria?