Characterizing species co‐occurrence patterns of imperfectly detected stream fishes to inform species reintroduction efforts

2019 ◽  
Vol 33 (6) ◽  
pp. 1392-1403 ◽  
Author(s):  
Karl A. Lamothe ◽  
Alan J. Dextrase ◽  
D. Andrew R. Drake
Hydrobiologia ◽  
2021 ◽  
Author(s):  
Lidia Brasil Seabra ◽  
Naraiana Loureiro Benone ◽  
Luciano Fogaça de Assis Montag

Diversity ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 13
Author(s):  
Thomas H. White ◽  
Wilfredo Abreu ◽  
Gabriel Benitez ◽  
Arelis Jhonson ◽  
Marisel Lopez ◽  
...  

The family Psittacidae is comprised of over 400 species, an ever-increasing number of which are considered threatened with extinction. In recent decades, conservation strategies for these species have increasingly employed reintroduction as a technique for reestablishing populations in previously extirpated areas. Because most Psittacines are highly social and flocking species, reintroduction efforts may face the numerical and methodological challenge of overcoming initial Allee effects during the critical establishment phase of the reintroduction. These Allee effects can result from failures to achieve adequate site fidelity, survival and flock cohesion of released individuals, thus jeopardizing the success of the reintroduction. Over the past 20 years, efforts to reestablish and augment populations of the critically endangered Puerto Rican parrot (Amazona vittata) have periodically faced the challenge of apparent Allee effects. These challenges have been mitigated via a novel release strategy designed to promote site fidelity, flock cohesion and rapid reproduction of released parrots. Efforts to date have resulted in not only the reestablishment of an additional wild population in Puerto Rico, but also the reestablishment of the species in the El Yunque National Forest following its extirpation there by the Category 5 hurricane Maria in 2017. This promising release strategy has potential applicability in reintroductions of other psittacines and highly social species in general.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Siul A. Ruiz ◽  
Samuel Bickel ◽  
Dani Or

AbstractEarthworm activity modifies soil structure and promotes important hydrological ecosystem functions for agricultural systems. Earthworms use their flexible hydroskeleton to burrow and expand biopores. Hence, their activity is constrained by soil hydromechanical conditions that permit deformation at earthworm’s maximal hydroskeletal pressure (≈200kPa). A mechanistic biophysical model is developed here to link the biomechanical limits of earthworm burrowing with soil moisture and texture to predict soil conditions that permit bioturbation across biomes. We include additional constraints that exclude earthworm activity such as freezing temperatures, low soil pH, and high sand content to develop the first predictive global map of earthworm habitats in good agreement with observed earthworm occurrence patterns. Earthworm activity is strongly constrained by seasonal dynamics that vary across latitudes largely due to soil hydromechanical status. The mechanistic model delineates the potential for earthworm migration via connectivity of hospitable sites and highlights regions sensitive to climate.


Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1115
Author(s):  
Gilseung Ahn ◽  
Hyungseok Yun ◽  
Sun Hur ◽  
Si-Yeong Lim

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.


2021 ◽  
pp. 112311
Author(s):  
Giulia Poma ◽  
Yukiko Fujii ◽  
Siebe Lievens ◽  
Jasper Bombeke ◽  
Beibei Gao ◽  
...  

2020 ◽  
Vol 30 (3) ◽  
pp. 565-576 ◽  
Author(s):  
Rowshyra A. Castañeda ◽  
Olaf L.F. Weyl ◽  
Nicholas E. Mandrak

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