Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management
Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management
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合作野生动物与雪科学：整合野生动物和雪专业知识以改进研究和管理
DOI：
10.1002/ecs2.4094
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发表时间：
2022
期刊：
影响因子：
2.7
通讯作者：
Brinkman, Todd J.
中科院分区：
文献类型：
--
作者：
Reinking, Adele K.;Højlund Pedersen, Stine;Elder, Kelly;Boelman, Natalie T.;Glass, Thomas W.;Oates, Brendan A.;Bergen, Scott;Roberts, Shane;Prugh, Laura R.;Brinkman, Todd J.
对于居住在雪地环境中的野生动物，雪的属性，如发病日期，深度，强度和分布可以影响生态学的许多方面，包括运动，群落动态，能量消耗和饲料可及性。因此，雪在个体适应性和最终种群动态中起着相当大的作用，因此，雪的评价对于全面了解雪区的生态系统过程非常重要。这种理解，特别是对野生动物与雪的关系如何变化的研究，随着冬季过程在全球气候变化下变得越来越不可预测，而且往往更加极端，变得更加紧迫。然而，研究和监测野生动物与雪的关系仍然具有挑战性，因为表征雪，一种固有的复杂和不断变化的环境特征，以及在适当的空间和时间尺度上识别，访问和应用相关的雪信息，通常需要详细了解物理雪科学和技术，这些科学和技术通常超出野生动物研究人员和管理人员的专业知识范围。我们认为，彻底评估雪在野生动物生态学中的作用，需要在这两个领域都有专业知识的研究人员之间进行实质性的合作，利用野生动物和雪专业人员带来的特定学科知识。为了促进这种合作并鼓励更有效地探索野生动物-雪问题，我们提供了一个五步协议：（1）识别相关的雪属性信息;（2）指定空间，时间和信息要求;（3）建立必要的数据集;（4）实施质量控制程序;（5）将雪信息纳入野生动物分析。此外，我们探索的类型的雪信息，可以在这个合作框架内使用。我们举例说明，在两个例子的背景下，实地观测，遥感数据集，和四个示例建模工具，模拟时空雪属性分布，并在某些情况下，演变。对于每种类型的雪数据，我们强调了野生动物和雪专业人员在设计雪数据收集工作、处理雪遥感产品、制作定制的雪数据集以及将所得雪信息应用于野生动物分析时的合作机会。我们寻求为野生动物专业人士提供一条明确的道路，以解决野生动物-雪的问题，并通过与雪专业人士合作，整合最好的雪科学来改善生态推断。
For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.
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DOI：
10.1029/2019jc015913
发表时间：
2020-10
期刊：
Journal of geophysical research. Oceans
影响因子：
--
作者：
Liston GE;Itkin P;Stroeve J;Tschudi M;Stewart JS;Pedersen SH;Reinking AK;Elder K
通讯作者：
Elder K
影响因子：
2.3
作者：
J. Hupp;C. Braun
通讯作者：
C. Braun
影响因子：
2.7
作者：
Jackson, Nathan J.;Stewart, Kelley M.;Rowland, Mary M.
通讯作者：
Rowland, Mary M.
影响因子：
1.7
作者：
Gilbert, Sophie L.;Hundertmark, Kris J.;Boyce, Mark S.
通讯作者：
Boyce, Mark S.
DOI：
10.1002/2015jf003593
发表时间：
2015
期刊：
Journal of Geophysical Research: Earth Surface
影响因子：
--
作者：
Heilig;C. Mitterer;L. Schmid;N. Wever;J. Schweizer;H.-P. Marshall;O. Eisen
通讯作者：
O. Eisen