دانلود مقاله عدم پیش بینی جمعیت جانوران توسط کیفیت زیستگاه
چکیده
مقدمه
مطالب و روش ها
نتایج
بحث
نتیجه گیری
منابع
Abstract
Introduction
Material and methods
Results
Discussion
Conclusion
References
چکیده
افزایش فراوانی اختلالات انسانی و طبیعی که توسط تغییرات آب و هوایی تشدید می شود، الگوهای حرکت حیوانات و به نوبه خود، توزیع و فراوانی گونه ها را مختل می کند. مدل‌های زیستگاه یک رویکرد ارزشمند برای پیش‌بینی نحوه توزیع یک گونه یا جمعیت در بین زیستگاه‌ها هستند. با این حال، در این مدل‌ها، ویژگی‌های زیست‌محیطی توصیف‌کننده زیستگاه معمولاً در طول زمان ثابت هستند که برای منظره‌ای که اغلب آشفته می‌شود، دقیق نیست. هدف از این مطالعه ارزیابی درجه ای است که اختلالات مکرر می تواند فراوانی و توزیع را تغییر دهد، تا جایی که استنتاج از نقشه های زیستگاه ساکن دیگر مرتبط نیست. ما حرکت حیوانات فردی را در یک چشم‌انداز آشفته پویا شبیه‌سازی کردیم و فراوانی نسبی فضایی مشاهده‌شده را با پیش‌بینی‌شده از یک مدل زیستگاه ایستا متناسب با مسیرهای شبیه‌سازی شده مقایسه کردیم. ما متوجه شدیم که خطای پیش‌بینی زمانی که منظره آشفته است بیشتر از جایی است که هیچ اختلالی وجود ندارد. این نتیجه حتی با افزودن تاریخچه زندگی و فرآیندهای جمعیت شناختی به مدل که کشف زیستگاه جدید را با افزایش حرکت وابسته به تراکم تسهیل می کند، برقرار است و در نتیجه توزیع فضایی افراد را به نسبتی از کیفیت زیستگاه ناشی از جمعیت نزدیک می کند. در فرض تعادلی که در اکثر مدل‌های زیستگاه وجود دارد. در زمینه برنامه‌ریزی بازیابی گونه‌های در معرض خطر، استفاده از مدل‌های زیستگاه با متغیرهای کمکی محیطی ثابت برای پیش‌بینی مکان‌های حیوانات در مناطقی با اختلالات مکرر در نتیجه تغییرات آب و هوایی احتمالاً پیش‌بینی‌های نادرستی ایجاد می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Increases in frequency of anthropogenic and natural disturbances exacerbated by climate change are disrupting animal movement patterns and, in turn, species distribution and abundance. Habitat models are a valuable approach for predicting how a species or population is distributed across habitats. However, in these models, the environmental characteristics describing habitat are typically constant over time which is inaccurate for a frequently disturbed landscape. The goal of this study is to assess the degree to which frequent disturbances can alter abundance and distribution, to the point of which inferences from static habitat maps are no longer relevant. We simulated individual animal movement in a dynamically disturbed landscape and compared the observed spatial relative abundance to the one predicted from a static habitat model fit to the simulated trajectories. We found that the prediction error is higher when the landscape is disturbed than where there are no disturbances. This result holds even with the addition of life-history and demographic processes to the model which would facilitate the discovery of novel habitat by increasing density-dependent movement, thereby bringing the spatial distribution of individuals closer to a proportion of habitat quality arising from the population at equilibrium assumption underlying most habitat models. In the context of species at risk recovery planning, using habitat models with constant environmental covariates to forecast animal locations in areas with more frequent disturbances as a result of climate change will likely produce inaccurate predictions.
Introduction
Large and unprecedented anthropogenic changes to the terrestrial systems are altering the distribution and abundance of global biodiversity (Raffa et al., 2008, Allen et al., 2010, Pereira et al., 2012, Boucher et al., 2018, Cadieux et al., 2020). Natural and anthropogenic disturbances are increasingly putting forest ecosystem and value at risk. The combined effect of multiple stressors exacerbated by climate change makes it difficult to understand and predict species responses to changes in the availability of essential resources (Mahon and Pelech, 2021). In boreal forest ecosystems for example, wildfires, insect outbreaks, drought, storms, and landslides are predicted to increase in frequency, intensity, and/or duration in the near future (Dale et al., 2001, Price et al., 2013). Our study focuses on these disturbances, whether natural or anthropogenic and their interactions, that present irregular spatio-temporal patterns so that animals are not able to anticipate them. As environmental conditions shift due to human activity and climate change, the modification of the size and location of natural habitats creates novel habitat conditions such as new land cover types, where species responses include rapid population decline or proliferation and changes in phenology or distribution (Barnosky et al., 2011, Bellard et al., 2012, Pereira et al., 2012). Effective management of species-at-risk requires forecasting the quality and distribution of future habitats and the resulting viability of species populations (Unglaub et al., 2015, Heinrichs et al., 2017, Masood et al., 2017). Whereas relating populations to habitats is a central goal in ecology, predicting population abundance and demography in novel conditions remains challenging (He and Gaston, 2007, Weber et al., 2017). Model predictions of habitat quality in novel areas may be misleading due to unmeasured conditions (Johnson et al., 2020), leading to population sinks or ecological traps (Fretwell, 1969). However, a more fundamental concern for predicting population abundance across wide ranging and potentially disconnected novel habitats is the need to estimate whether, and over what time-span, a species population will discover and use habitat that emerges in currently unused areas. Indeed, whereas some species have strong negative responses to disturbances, others react positively to new opportunities created by some disturbance types (Fisher and Burton, 2018). For example, wolverines (Gulo gulo luscus) are attracted to foraging opportunities in areas of active logging (Scrafford et al., 2017). In this study, we focus on these positive relationships between a species and a disturbance type.
Conclusion
Given increasing anthropogenic pressures and natural disturbances exacerbated by climate change, managing populations of invasive and/or species at risk requires flexible decision-making tools to adapt to the uncertainty and complexity of rapidly changing landscapes. In this study, we show that local variations in ecosystem dynamics, such as unpredictable disturbances, as well as animal location and movement impact emergent properties at a population level such as spatial abundance. We provide a quantitative assessment of the error associated with the assumption that population are at equilibrium in a static landscape in habitat models. The implications for species at risk recovery planning are twofold. First, given foraging and density-dependence assumptions, animal movement responds dramatically to the presence of disturbances. Second, in an area that is subject to more severe or frequent climate-derived disturbances, using habitat models to forecast animal location or abundance will likely produce inaccurate projections. However, researchers should not discard static habitat components when they are the best information available. Instead, they should determine the potential for bias given the specifics of their study area. Population management and conservation plans require habitat and population abundance forecasts. Without knowledge or understanding of the effect of landscape dynamics on animal space use, a population may be projected to do well or poorly in a domain with insufficient or mismatched resources (Heinrichs et al., 2017). Therefore, it is essential that land managers and researchers collaborate to produce models that improve decision-making (Bodner et al., 2021), i.e. models providing accurate predictions by including the relevant dynamical aspects of the study area and species.