2019
DOI: 10.1590/0001-3765201820180308
|View full text |Cite
|
Sign up to set email alerts
|

Anticipating the response of the Brazilian giant earthworm (Rhinodrilus alatus) to climate change: implications for its traditional use

Abstract: How to cite: HUgHeS FM, CÔRTeS-FIgUeIRA Je AND DRUMOND MA. undefined. 2019. Anticipating the response of the Brazilian giant earthworm (Rhinodrilus alatus) to climate change: implications for its traditional use. An Acad Bras Cienc 91: e20180308.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
4
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 63 publications
(57 reference statements)
0
4
0
Order By: Relevance
“…Potential distribution modeling/niche characterization [ 30 , 31 , 32 ] and genetic diversity monitoring [ 33 ] have been established as important tools for evaluating the conservation status of vertebrates. Ecological niche modeling has been used successfully on earthworms; in spite of their peculiarities (difficulty of sampling, patchy distributions), MaxEnt [ 34 , 35 , 36 , 37 ], Random Forests [ 38 ], and other algorithms have achieved high predictive power when using large-scale variables to predict the earthworms’ potential distribution. Implementing these approaches to earthworm biodiversity conservation would facilitate the necessary research on this key component of soil fauna.…”
Section: Introductionmentioning
confidence: 99%
“…Potential distribution modeling/niche characterization [ 30 , 31 , 32 ] and genetic diversity monitoring [ 33 ] have been established as important tools for evaluating the conservation status of vertebrates. Ecological niche modeling has been used successfully on earthworms; in spite of their peculiarities (difficulty of sampling, patchy distributions), MaxEnt [ 34 , 35 , 36 , 37 ], Random Forests [ 38 ], and other algorithms have achieved high predictive power when using large-scale variables to predict the earthworms’ potential distribution. Implementing these approaches to earthworm biodiversity conservation would facilitate the necessary research on this key component of soil fauna.…”
Section: Introductionmentioning
confidence: 99%
“…Also, the study conducted by Berman and Meshcheryakova [42] indicated a similar result: in higher temperatures with higher soil moisture, earthworm numbers increased rapidly. e study conducted by Hughes et al [43] showed that higher air temperatures with lower soil moisture could reduce the earthworm population. e abundance of earthworms usually increases when climatic conditions are favourable, such as when the temperature is moderate and the soil moisture content is higher [17].…”
Section: Rise In Soilmentioning
confidence: 99%
“…Among the "presence-only" models, one of the most widely used models is the Maximum Entropy method, implemented in the Maxent program. The Maxent model is based on machine learning algorithms and has been validated in numerous studies as an effective algorithm for predicting species distribution, including for the study of earthworms [24][25][26][27][28][29].…”
Section: Introductionmentioning
confidence: 99%