(Non)local logistic equations with Neumann conditions

冯·诺依曼建筑 逻辑函数 数学 应用数学 统计 纯数学
作者
Serena Dipierro,Edoardo Proietti Lippi,Enrico Valdinoci
出处
期刊:Annales de l'Institut Henri Poincaré C, Analyse non linéaire [Elsevier BV]
被引量:9
标识
DOI:10.4171/aihpc/57
摘要

We consider here a problem of population dynamics modeled on a logistic equation with both classical and nonlocal diffusion, possibly in combination with a pollination term. The environment considered is a niche with zero-flux, according to a new type of Neumann condition. We discuss the situations that are more favorable for the survival of the species, in terms of the first positive eigenvalue. Quite surprisingly, the eigenvalue analysis for the one dimensional case is structurally different than the higher dimensional setting, and it sensibly depends on the nonlocal character of the dispersal. The mathematical framework of this problem takes into consideration the equation $$ -\alpha\Delta u +\beta(-\Delta)^su =(m-\mu u)u+\tau\;J\star u \qquad{\mbox{in }}\; \Omega,$$ where $m$ can change sign. This equation is endowed with a set of Neumann condition that combines the classical normal derivative prescription and the nonlocal condition introduced in [S. Dipierro, X. Ros-Oton, E. Valdinoci, Rev. Mat. Iberoam. (2017)]. We will establish the existence of a minimal solution for this problem and provide a throughout discussion on whether it is possible to obtain non-trivial solutions (corresponding to the survival of the population). The investigation will rely on a quantitative analysis of the first eigenvalue of the associated problem and on precise asymptotics for large lower and upper bounds of the resource. In this, we also analyze the role played by the optimization strategy in the distribution of the resources, showing concrete examples that are unfavorable for survival, in spite of the large resources that are available in the environment.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
学术牛马发布了新的文献求助10
1秒前
饱满南松发布了新的文献求助10
1秒前
田様应助小梁要加油采纳,获得10
1秒前
hbzyydx46发布了新的文献求助10
2秒前
huhdcid发布了新的文献求助50
3秒前
3秒前
科研混子发布了新的文献求助30
3秒前
TT小天完成签到,获得积分10
3秒前
张广瀚发布了新的文献求助10
4秒前
过时的远侵完成签到,获得积分10
4秒前
4秒前
香菜味钠片完成签到,获得积分10
4秒前
actor2006完成签到,获得积分10
5秒前
lgs1412发布了新的文献求助10
5秒前
7秒前
科研通AI5应助wyh0922采纳,获得10
8秒前
斯文败类应助ASUNA采纳,获得10
8秒前
10秒前
机智的紫丝完成签到,获得积分10
10秒前
搜集达人应助livresse采纳,获得10
12秒前
13秒前
hbzyydx46完成签到,获得积分10
14秒前
14秒前
purejun发布了新的文献求助10
15秒前
研友_VZG7GZ应助NSK采纳,获得10
15秒前
科研通AI5应助noya采纳,获得10
16秒前
Oatmeal5888完成签到,获得积分10
17秒前
111完成签到,获得积分10
17秒前
CucRuotThua应助taran采纳,获得10
17秒前
阿星捌完成签到 ,获得积分10
19秒前
tqmx完成签到,获得积分10
20秒前
钱百万发布了新的文献求助10
20秒前
ruochenzu发布了新的文献求助30
20秒前
空白的卡卡完成签到,获得积分10
21秒前
cdercder应助哆啦采纳,获得20
21秒前
斯文败类应助张广瀚采纳,获得10
22秒前
26秒前
26秒前
loulan完成签到,获得积分10
26秒前
科研通AI5应助passerby采纳,获得30
27秒前
高分求助中
All the Birds of the World 3000
Weirder than Sci-fi: Speculative Practice in Art and Finance 960
Resilience of a Nation: A History of the Military in Rwanda 500
IZELTABART TAPATANSINE 500
Introduction to Comparative Public Administration: Administrative Systems and Reforms in Europe: Second Edition 2nd Edition 300
Spontaneous closure of a dural arteriovenous malformation 300
Not Equal : Towards an International Law of Finance 260
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3726387
求助须知:如何正确求助?哪些是违规求助? 3271420
关于积分的说明 9971932
捐赠科研通 2986848
什么是DOI,文献DOI怎么找? 1638544
邀请新用户注册赠送积分活动 778131
科研通“疑难数据库(出版商)”最低求助积分说明 747469