Moving forward: insights and applications of moving‐habitat models for climate change ecology

Abstract

Summary Predicting and managing species’ responses to climate change is one of the most significant challenges of our time. Tools are needed to address problems associated with novel climatic conditions, biotic interactions and greater climate velocities. We present a spatially explicit moving‐habitat model (MHM) and demonstrate its versatility in tackling critical questions in climate change research, including dispersal in multiple spatial dimensions, population stage structure, interspecific interactions, asymmetric range shifts, Allee effects and the presence of infectious diseases. The model utilizes integrodifference equations to track changes in population density over time in a habitat that is moving. The model is quite flexible and can accommodate variation in demography, dispersal patterns, biotic interaction and stochasticity in the velocity of climate change. The methods provide a general mechanistic understanding of the underlying ecological processes that drive a system. Field data can be readily incorporated into the model to give insight into specific populations of interest and inform management decisions. Synthesis. Moving‐habitat models unite ecological theory, data‐centred modelling and conservation decision support under a single framework. Their ability to generate testable hypotheses, incorporate data and inform best management practices proves that these models provide a valuable framework for climate change biologists.

Lags in the response of mountain plant communities to climate change

“…This agenda will require methodological developments (e.g. Harsch et al., ; Urban et al., ) and the collection of appropriate demographic data, e.g. using experiments (Alexander, Diez, Hart, & Levine, ).…”

“…A greater variation in growth rate between warm-and cool-adapted species also accelerated the rate of temporal turnover in community composition (Figure 3a require methodological developments (e.g. Harsch et al, 2017;Urban et al, 2016) and the collection of appropriate demographic data, e.g. using experiments (Alexander, Diez, Hart, & Levine, 2016).…”

Agroforestry Status and Its Role to Sequester Atmospheric Co2 Under Semi-Arid Climatic Conditions in Pakistan

“…Due to several activities like combustion of fossil fuels and deforestation during the last 100 years, chemical makeup of this flimsy layer of the atmosphere has been extremely altered (Pold et al, 2017). Such kind of modifications in chemical composition has a wide range of significant harmful results on the long term weather conditions of the planet, the ecological systems which are being supported by the climate of the Earth, and the welfare of human beings and economy (CDIAC, 2000; Harsch et al, 2017 Global warming is among the greatest terrible horrors of the modern times (Salvatore et al, 2017). It is believed that carbon is among the most significant casual factors which cause global warming (Kerr, 2007;Franzluebbers et al, 2017;Jones et al, 2017).…”

Skewed temperature dependence affects range and abundance in a warming world

“…To explore how fitness curve skewness may affect a species' range size and abundance during climate change-induced range shifts, we formulate a 'moving-habitat' integrodifference equation (IDE) model 23 . The IDE framework simultaneously considers both dispersal and net reproduction, and produces 'travelling wave' solutions 24 that describe how a species' distribution moves across the landscape via reproduction, dispersal, colonization, and mortality.…”

“…'Moving-habitat' IDE models consider a fundamental niche that shifts due to climate warming, and analyses show that the speed of climate change, the size of a species' niche, as well as its population growth rate and dispersal ability interact to determine if a species will keep pace with its moving niche [25][26][27] . Moving-habitat IDE models describe the dynamics of both the shifting niche and the shifting species distribution, and are a suitable framework for understanding when populations will lag behind their fundamental niches as a result of climate warming.…”