被动声学监测（PAM）已成为调查鸟类的重要工具，对从 PAM 记录中获取鸟类数量和行为信息的方法的需求也在不断增长。鸟类种群的变化是通过计算记录的鸟叫声和发声活动率（VAR，即每次记录时间内的鸟叫声数量）来评估的。然而，鸟类鸣叫的计数方式多种多样，而且根据不同的物种特征，这些鸣叫计数可以让我们对鸟类的数量、发声行为和/或栖息地使用情况有不同的了解。我们的研究有两个目标(1）提出并评估两个基于鸣叫计数的新指数，即检测率（DR，即检测到目标发声存在的 1 分钟记录数）和每分钟最大计数（MAX，即 1 分钟记录中发现的最大鸣叫数）；以及（2）提出一个概念框架，说明对 VAR、DR 和 MAX 的解释如何取决于指数和物种特征。对于具有不同特征的三种新热带鸟类，我们根据尤卡坦半岛（墨西哥）25 个地点的 PAM 数据计算了 VAR、DR 和 MAX，这些地点的人为生境干扰程度各不相同。我们发现这些指数之间存在中度到高度的相关性，与 DR 和 MAX 相比，VAR 的时间变异性更高。我们还发现，生境干扰对三种物种和指数的影响大小不同。我们认为，对于鸣叫行为具有高提示率的物种来说，DR可能是比VAR更可靠的相对丰度指数，而MAX可能适用于估计群居鸟类的家族或鸟群规模。我们的研究结果表明，基于叫声计数开发新的指数对于提出生态假设和评估鸟类丰度与行为的变化具有潜在的实用性。
{"title":"How to count bird calls? Vocal activity indices may provide different insights into bird abundance and behaviour depending on species traits","authors":"Anja Hutschenreiter, Ellen Andresen, Margarita Briseño-Jaramillo, Alejandra Torres-Araneda, Eduardo Pinel-Ramos, Jacqueline Baier, Filippo Aureli","doi":"10.1111/2041-210X.14333","DOIUrl":"10.1111/2041-210X.14333","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Systematic review and best practices for drone remote sensing of invasive plants","authors":"Kunwar K. Singh, Thilina D. Surasinghe, Amy E. Frazier","doi":"10.1111/2041-210X.14330","DOIUrl":"10.1111/2041-210X.14330","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natalie Cooper, Adam T. Clark, Nicolas Lecomte, Huijie Qiao, Aaron M. Ellison
Large language models (LLMs) are a type of artificial intelligence (AI) that can perform various natural language processing tasks. The adoption of LLMs has become increasingly prominent in scientific writing and analyses because of the availability of free applications such as ChatGPT. This increased use of LLMs not only raises concerns about academic integrity but also presents opportunities for the research community. Here we focus on the opportunities for using LLMs for coding in ecology and evolution. We discuss how LLMs can be used to generate, explain, comment, translate, debug, optimise and test code. We also highlight the importance of writing effective prompts and carefully evaluating the outputs of LLMs. In addition, we draft a possible road map for using such models inclusively and with integrity.LLMs can accelerate the coding process, especially for unfamiliar tasks, and free up time for higher level tasks and creative thinking while increasing efficiency and creative output. LLMs also enhance inclusion by accommodating individuals without coding skills, with limited access to education in coding, or for whom English is not their primary written or spoken language. However, code generated by LLMs is of variable quality and has issues related to mathematics, logic, non‐reproducibility and intellectual property; it can also include mistakes and approximations, especially in novel methods.We highlight the benefits of using LLMs to teach and learn coding, and advocate for guiding students in the appropriate use of AI tools for coding. Despite the ability to assign many coding tasks to LLMs, we also reaffirm the continued importance of teaching coding skills for interpreting LLM‐generated code and to develop critical thinking skills.As editors of MEE, we support—to a limited extent—the transparent, accountable and acknowledged use of LLMs and other AI tools in publications. If LLMs or comparable AI tools (excluding commonly used aids like spell‐checkers, Grammarly and Writefull) are used to produce the work described in a manuscript, there must be a clear statement to that effect in its Methods section, and the corresponding or senior author must take responsibility for any code (or text) generated by the AI platform.
{"title":"Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution","authors":"Natalie Cooper, Adam T. Clark, Nicolas Lecomte, Huijie Qiao, Aaron M. Ellison","doi":"10.1111/2041-210x.14325","DOIUrl":"https://doi.org/10.1111/2041-210x.14325","url":null,"abstract":"<jats:list> <jats:list-item>Large language models (LLMs) are a type of artificial intelligence (AI) that can perform various natural language processing tasks. The adoption of LLMs has become increasingly prominent in scientific writing and analyses because of the availability of free applications such as ChatGPT. This increased use of LLMs not only raises concerns about academic integrity but also presents opportunities for the research community. Here we focus on the opportunities for using LLMs for coding in ecology and evolution. We discuss how LLMs can be used to generate, explain, comment, translate, debug, optimise and test code. We also highlight the importance of writing effective prompts and carefully evaluating the outputs of LLMs. In addition, we draft a possible road map for using such models inclusively and with integrity.</jats:list-item> <jats:list-item>LLMs can accelerate the coding process, especially for unfamiliar tasks, and free up time for higher level tasks and creative thinking while increasing efficiency and creative output. LLMs also enhance inclusion by accommodating individuals without coding skills, with limited access to education in coding, or for whom English is not their primary written or spoken language. However, code generated by LLMs is of variable quality and has issues related to mathematics, logic, non‐reproducibility and intellectual property; it can also include mistakes and approximations, especially in novel methods.</jats:list-item> <jats:list-item>We highlight the benefits of using LLMs to teach and learn coding, and advocate for guiding students in the appropriate use of AI tools for coding. Despite the ability to assign many coding tasks to LLMs, we also reaffirm the continued importance of teaching coding skills for interpreting LLM‐generated code and to develop critical thinking skills.</jats:list-item> <jats:list-item>As editors of MEE, we support—to a limited extent—the transparent, accountable and acknowledged use of LLMs and other AI tools in publications. If LLMs or comparable AI tools (excluding commonly used aids like spell‐checkers, Grammarly and Writefull) are used to produce the work described in a manuscript, there must be a clear statement to that effect in its Methods section, and the corresponding or senior author must take responsibility for any code (or text) generated by the AI platform.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure","authors":"Benoît Pichon, Rémy Le Goff, Hélène Morlon, Benoît Perez-Lamarque","doi":"10.1111/2041-210X.14328","DOIUrl":"10.1111/2041-210X.14328","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Ruf, Claudio Signer, Walter Arnold, Sebastian G. Vetter, Claudia Bieber
过去几十年来，人们对长期心率记录，尤其是自由生活动物的心率记录越来越感兴趣。这主要是因为组织的大部分新陈代谢活动都是基于心脏的氧气输送。然而，使用记录仪记录的人类和动物的心率或其他生理变量往往含有噪声。有时会通过人工或滤波器剔除虚假测量值，滤波器会根据信号的形状或频率剔除变量。我们介绍了一个 R 软件包 boxfilter，它能让用户通过计算滑动窗口内近邻的数量来消除噪声。根据所选的截止值，邻域比例较低的焦点将作为噪声被剔除。所有三个参数，即窗口宽度和高度以及截止值，都可以自动计算。窗口宽度和高度以及截断值这三个参数都可以自动计算。截断值的选择至关重要，超过这个值的数据点将被丢弃。当然，软件包方框滤波器无法解决完全错误的测量所造成的问题。与人眼一样，该滤波器也会偏好那些属于某种模式（如密集带）的点，而剔除孤立的值。除心率外，方框过滤器还可用于其他非瞬时变化的测量值，如体温、血压或睡眠参数。
{"title":"Filtering heart rates using data densities: The boxfilter R package","authors":"Thomas Ruf, Claudio Signer, Walter Arnold, Sebastian G. Vetter, Claudia Bieber","doi":"10.1111/2041-210X.14301","DOIUrl":"10.1111/2041-210X.14301","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meghan A. Balk, John Bradley, M. Maruf, Bahadir Altintaş, Yasin Bakiş, Henry L. Bart Jr, David Breen, Christopher R. Florian, Jane Greenberg, Anuj Karpatne, Kevin Karnani, Paula Mabee, Joel Pepper, Dom Jebbia, Thibault Tabarin, Xiaojun Wang, Hilmar Lapp
{"title":"A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics","authors":"Meghan A. Balk, John Bradley, M. Maruf, Bahadir Altintaş, Yasin Bakiş, Henry L. Bart Jr, David Breen, Christopher R. Florian, Jane Greenberg, Anuj Karpatne, Kevin Karnani, Paula Mabee, Joel Pepper, Dom Jebbia, Thibault Tabarin, Xiaojun Wang, Hilmar Lapp","doi":"10.1111/2041-210X.14327","DOIUrl":"10.1111/2041-210X.14327","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140676553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A method for sampling the living wood microbiome","authors":"Wyatt Arnold, Jonathan Gewirtzman, Peter A. Raymond, Mark A. Bradford, Claire Butler, Jordan Peccia","doi":"10.1111/2041-210X.14311","DOIUrl":"10.1111/2041-210X.14311","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nilanjan Chatterjee, David Wolfson, Dongmin Kim, Juliana Velez, Smith Freeman, Nathan M. Bacheler, Kyle Shertzer, J. Christopher Taylor, John Fieberg
综合阶跃选择分析（ISSA）经常用于利用动物运动数据研究栖息地选择。目前已开发出将随机效应纳入 ISSA 的方法，从而可以量化动物空间利用模式的变异性。尽管可以对生境选择和运动参数的变异性进行建模，但迄今为止的应用主要集中在前者，尽管运动在决定从个体到生态系统水平的生态过程中发挥着广泛认可的重要作用。造成这种遗漏的一个潜在原因是缺乏现成的软件或实例来演示在具有随机效应的 ISSA 中估计运动参数的方法。我们利用模拟数据集，并通过对包含 35 条红鲷鱼（Lutjanus campechanus）位置的声学遥测数据集进行两个模型拟合，展示了描述个体间运动和生境选择参数变异性的方法。运动核被假定为取决于鱼类所在的底栖珊瑚礁生境类型（模型 1）或鱼类当前位置与最近的边缘生境之间的距离（模型 2）。在这两个模型中，我们还量化了不同底栖生境类别的生境选择和与边缘生境的距离，并利用随机效应考虑了运动和生境选择参数的个体差异性。模拟示例强调了混合效应规格的优点，即我们可以通过借用同类个体的信息来提高估计个体运动参数的精度。在我们的应用实例中，我们发现个体间的栖息地选择和移动参数都存在很大的差异。尽管如此，大多数红鲷鱼还是选择了硬底栖息地和靠近边缘栖息地的地点。它们在硬底栖息地的移动也较少。转弯角度经常接近 ± π，但在远离边缘栖息地时则较为分散。我们提供了代码模板和函数，以便在实施具有随机效应的 ISSA 时量化运动和生境选择参数的变异性。我们希望通过这种方法鼓励生态学家在进行 ISSA 时充分利用其能力，为栖息地选择和运动模式的个体间变异性建模。
{"title":"Modelling individual variability in habitat selection and movement using integrated step-selection analysis","authors":"Nilanjan Chatterjee, David Wolfson, Dongmin Kim, Juliana Velez, Smith Freeman, Nathan M. Bacheler, Kyle Shertzer, J. Christopher Taylor, John Fieberg","doi":"10.1111/2041-210X.14321","DOIUrl":"10.1111/2041-210X.14321","url":null,"abstract":"<p>\u0000 \u0000 </p>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.14321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
1 引言 动物移动是指个体空间位置随时间的变化，几乎是所有动物的一个关键行为过程。它不仅能满足生存和繁殖的基本要求，还能影响物种的地理分布，从而影响种群、群落和生态系统的结构、动态和遗传组成（Jeltsch 等人，2013 年；Nathan 等人，2008 年）。虽然直到最近，大多数研究都忽视了动物在移动生态学中可能存在的相互作用，但动物的移动往往不是相互独立的（Nathan 等人，2022 年；Vicsek & Zafeiris，2012 年）。到目前为止，对这种相互作用的研究相对较少，但由于最近追踪技术的改进，可以收集到多个同时移动个体的高分辨率数据，这为此类研究提供了机会（Farley 等人，2018 年；Urbano 等人，2010 年）。然而，分析这些数据需要合适的方法来评估移动个体之间的相互作用。虽然最近开发出了几种推断此类互动的方法（Long 等人，2014 年；Niu 等人，2016 年；Schlaegel 等人，2019 年），但我们仍然不知道这些方法在特定条件下与其他方法相比表现如何。例如，被跟踪个体的比例、感知范围和跟踪持续时间如何影响这些方法推断交互作用的能力。我们通过关注捕食者和猎物的移动个体，并使用模拟来评估三种常用统计方法和两种新型统计方法的性能，填补了这一研究空白。移动个体之间的相互作用可简化为三大类型：回避、吸引和中立（Schlaegel 等人，2019 年）。例如，组成群体的社会性动物会一起向同一目的地移动，群体中的个体通常会表现出对其他群体成员的吸引，或在交配季节进行求偶。相反，猎物通常会逃离捕食者，表现出回避；竞争者相互回避也是如此。我们将捕食者-猎物系统作为研究系统，研究移动个体之间的这种相互作用，因为捕食者-猎物动力学已被广泛研究（Banks 等人，2004 年；Peterson，1999 年；Volterra，1931 年）。以前的研究主要集中在捕食者与猎物之间的相互作用如何影响种群和群落动态（Angerbjorn 等人，1999 年；Drossel 等人，2001 年；Schneider 等人，2012 年），但对捕食者和猎物在移动过程中如何进行行为互动却知之甚少。虽然我们选择研究捕食者-猎物系统中移动个体之间的相互作用，但我们的研究结果更普遍地适用于其他系统，在这些系统中，移动个体可能表现出吸引、回避或彼此独立移动。由于缺乏关于何时使用哪种方法的指导原则，而且有些方法对于没有很强编程能力的研究人员来说并不那么容易实现，这些都阻碍了实证主义者对这些方法的广泛使用。因此，本研究旨在比较现有统计方法的基本假设、数据要求以及在各种条件下的性能。我们这里所说的性能是指该方法检测真实交互作用的能力。为此，我们重点研究了五种统计方法（图 1c）：三种常用的动态交互作用指数（Cs-社会性系数、Cr-相关系数和 DI-动态交互作用指数），它们在 "野生动物 DI "R 软件包（Long et al、Schlaegel等人，2019；以下简称SSF-OD），另一种方法则使用与其他移动个体的距离（Roeleke等人，2022；以下简称SSF-DIST）。这五种方法都是通过个体的运动轨迹来估计相互作用的。为了测试这些方法，我们用基于代理的模型（ABM，Grimm 等人，2006 年；Tang & Bennett，2010 年）模拟了运动数据，因为它允许我们用已知的相互作用类型建立数据模型，并能轻松地改变多种因素，而这是观测数据无法做到的。运动的个体可能处于两种行为状态之一：持续运动和非持续运动，后者与相关随机行走非常相似。 (b) 我们通过改变与野外生态学家相关的四个因素来研究统计方法的性能：捕食者感知范围的四个等级；被追踪猎物比例的五个等级；时间分辨率的两个等级；追踪期持续时间的四个等级。(c) 对生成的运动轨迹采用了五种不同的统计方法。上面的三个（Cs、Cr、DI）是交互指数，下面的两个是基于 SSF 的方法。我们通过在基于代理的模型模拟中操纵四个因素来研究这五种方法的性能（表 1）。其中一个因素反映了所研究物种的生物学特性，代表了捕食者的感知范围。其他三个因素可能会受到野外研究人员的影响，因此，如果以评估相互作用为目的，这三个因素将有助于优化数据收集。第一个因素是被追踪个体的比例。了解其对方法性能的影响至关重要，因为标记所有个体的成本很高，而且在后勤上也很困难。例如，Liu 等人（2015 年）发现，建议分析群体中的大量个体子集，以获得更准确的群体运动特征。接下来，我们研究了时间分辨率和追踪时间对方法的影响。鉴于快速发展的技术（Farley 等人，2018 年）能够以越来越高的时间分辨率追踪个体，因此了解时间分辨率对推断运动互动的影响至关重要。事实上，某些运动行为和模式可能只有在高时间分辨率下才能检测到（Nathan等人，2022年），而低时间分辨率可能会掩盖或削弱重要的生物信息（Mannocci等人，2017年；Postlethwaite & Dennis，2013年）。然而，高分辨率数据也可能是一种负担，因为它往往需要更多的存储和处理能力（McCrea 等人，2023 年），而且必须拟合复杂的模型，解释这些模型具有挑战性。对动物进行追踪的时间也很重要，因为这可能会受到追踪设备寿命甚至动物福利法规的限制（Teague O'Mara 等人，2014 年）。本研究中用于评估运动数据交互作用的五种方法概述。方法参考文献描述解释社会性系数（Cs）Kenward et al.(1993)Differences between mean distance of all simultaneous fixes and mean expected distance obtained with permutations of all fixesCs ~ + 1 AttractionCs ~ - 1 AvoidanceCs ~ 0 NeutralCorrelation index (Cr)Shirabe (2006)Correlation of movement segments of two animals with respect to the overall path means.正相关Cr ~ - 1 负相关Cr ~ 0 中性动态交互指数 (DI)Long 和 Nelson (2013)两只动物运动片段的凝聚力、与距离和方向有关DI ~ + 1 凝聚运动DI ~ - 1 排斥运动DI ~ 0 中性基于出现率分布的阶跃选择函数方法（SSF-OD）Schlaegel 等人(2019)将阶跃选择函数应用于 "湍流 "和 "漩涡 "的研究，发现 "湍流 "和 "漩涡 "在湍流和漩涡的出现率分布上存在显著差异。(2019)Step-selection functions applied to occurrence distributions of individuals obtained through kriging of their movement pathSSF ~ + 1 AttractionSSF ~ - 1 Avoi
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