Peer Community In Ecology最新文献_第2页

Marks lost in action, biased estimations. 在行动中损失的分数，有偏见的估计。

Peer Community In Ecology Pub Date : 2023-11-09 DOI: 10.24072/pci.ecology.100416

Sylvain Billiard

引用次数: 0

Mullerian and Batesian mimicry can influence population and community dynamics 缪勒式和贝叶斯式模仿可以影响人口和社区动态

Peer Community In Ecology Pub Date : 2023-11-06 DOI: 10.24072/pci.ecology.100511

Amanda Franklin

引用次数: 2

Are Moas ancient Lazarus species? Moas是古代拉撒路的物种吗?

Peer Community In Ecology Pub Date : 2023-10-23 DOI: 10.24072/pci.ecology.100582

Werner Ulrich

{"title":"Are Moas ancient Lazarus species?","authors":"Werner Ulrich","doi":"10.24072/pci.ecology.100582","DOIUrl":"https://doi.org/10.24072/pci.ecology.100582","url":null,"abstract":"The Moa (Aves: Dinornithiformes) are an extinct group of the ratite clade from New Zealand. The overkill hypothesis asserts that the first New Zealand settlers hunted the Moa to extinction by 1450 CE, whereas the staggered survival hypothesis allows for Moa survival until after Europeans began to arrive on New Zealand. Alleged Moa sightings post-1450 CE may shed light on these competing hypotheses. A dataset of 97 alleged Moa sightings from circa 1675 CE to 1993 CE was constructed, with sightings given subjective quality ratings corresponding to various statistical probabilities. Cumulative probabilities of Moa persistence were calculated with a conservative survival model using these probabilistic sighting-records; a method recently applied to sightings of the Thylacine. Cumulative persistence probability fell sharply after 1408 CE, and across pessimistic and optimistic variations of the model, it was more likely than not that the Moa were extinct by 1770 CE. Probabilistic sighting-record models favour the overkill hypothesis, and give very low probabilities of Moa persistence around the time of European arrival. Eyewitness data on Moa sightings are amenable to scientific study, and these methods may be applied to similar animals.","PeriodicalId":186865,"journal":{"name":"Peer Community In Ecology","volume":"322 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disentangling microbial exopolymer dynamics in intertidal sediments 潮间带沉积物中微生物外聚合物动力学解缠

Peer Community In Ecology Pub Date : 2023-10-11 DOI: 10.24072/pci.ecology.100519

Ute Risse-Buhl, Nils Rädecker

引用次数: 1

Biodiversity databases are ever more numerous, but can they be used reliably for Species Distribution Modelling? 生物多样性数据库越来越多，但它们能可靠地用于物种分布建模吗?

Peer Community In Ecology Pub Date : 2023-10-03 DOI: 10.24072/pci.ecology.100539

Nicolas Schtickzelle

引用次数: 1

Assessing seasonality of tick abundance in different climatic regions 评估不同气候区域蜱虫丰度的季节性

Peer Community In Ecology Pub Date : 2023-10-01 DOI: 10.24072/pci.ecology.100507

Nigel Yoccoz

引用次数: 0

A deep learning model to unlock secrets of animal movement and behaviour 一个深度学习模型来解开动物运动和行为的秘密

Peer Community In Ecology Pub Date : 2023-09-29 DOI: 10.24072/pci.ecology.100531

Cédric Sueur

{"title":"A deep learning model to unlock secrets of animal movement and behaviour","authors":"Cédric Sueur","doi":"10.24072/pci.ecology.100531","DOIUrl":"https://doi.org/10.24072/pci.ecology.100531","url":null,"abstract":"The movement of animals is a central component of their behavioural strategies. Statistical tools for movement data analysis, however, have long been limited, and in particular, unable to account for past movement information except in a very simplified way. In this work, we propose MoveFormer, a new step-based model of movement capable of learning directly from full animal trajectories. While inspired by the classical step-selection framework and previous work on the quantification of uncertainty in movement predictions, MoveFormer also builds upon recent developments in deep learning, such as the Transformer architecture, allowing it to incorporate long temporal contexts. The model predicts an animal’s next movement step given its past movement history, including not only purely positional and temporal information, but also any available environmental covariates such as land cover or temperature. We apply our model to a diverse dataset made up of over 1550 trajectories from over 100 studies, and show how it can be used to gain insights about the importance of the provided context features, including the extent of past movement history. Our software, along with the trained model weights, is released as open source.","PeriodicalId":186865,"journal":{"name":"Peer Community In Ecology","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The impact of process at different scales on diversity and ecosystem functioning: a huge challenge 不同尺度的过程对多样性和生态系统功能的影响:一个巨大的挑战

Peer Community In Ecology Pub Date : 2023-09-12 DOI: 10.24072/pci.ecology.100402

David Alonso

引用次数: 0

Addressing the daunting challenge of estimating species interactions from count data 解决从计数数据估计物种相互作用的艰巨挑战

Peer Community In Ecology Pub Date : 2023-08-31 DOI: 10.24072/pci.ecology.100522

T. Coulson, David Alonso

引用次数: 1

High-order interactions in food webs may strongly impact persistence of species 食物网中的高阶相互作用可能强烈影响物种的持久性

Peer Community In Ecology Pub Date : 2023-08-29 DOI: 10.24072/pci.ecology.100527

C. Gaucherel

引用次数: 1