scholarly journals Geographic mosaics of phenology, host preference, adult size and microhabitat choice predict butterfly resilience to climate warming

Oikos ◽  
2014 ◽  
Vol 124 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Nichole L. Bennett ◽  
Paul M. Severns ◽  
Camille Parmesan ◽  
Michael C. Singer
Keyword(s):  
Climate Warming ◽  
Host Preference ◽  
Adult Size ◽  
Microhabitat Choice

Persistence of giants: population dynamics of the limpet Scutellastra laticostata on rocky shores in Western Australia

2020 ◽  
Vol 646 ◽  
pp. 79-92
Author(s):  
RE Scheibling ◽  
R Black
Keyword(s):  
Population Dynamics ◽  
Western Australia ◽  
Size Structure ◽  
Survival Rates ◽  
Maximum Size ◽  
High Rate ◽  
Adult Survival ◽  
Rocky Shores ◽  
Adult Size ◽  
Decadal Time Scale

Population dynamics and life history traits of the ‘giant’ limpet Scutellastra laticostata on intertidal limestone platforms at Rottnest Island, Western Australia, were recorded by interannual (January/February) monitoring of limpet density and size structure, and relocation of marked individuals, at 3 locations over periods of 13-16 yr between 1993 and 2020. Limpet densities ranged from 4 to 9 ind. m-2 on wave-swept seaward margins of platforms at 2 locations and on a rocky notch at the landward margin of the platform at a third. Juvenile recruits (25-55 mm shell length) were present each year, usually at low densities (<1 m-2), but localized pulses of recruitment occurred in some years. Annual survival rates of marked limpets varied among sites and cohorts, ranging from 0.42 yr-1 at the notch to 0.79 and 0.87 yr-1 on the platforms. A mass mortality of limpets on the platforms occurred in 2003, likely mediated by thermal stress during daytime low tides, coincident with high air temperatures and calm seas. Juveniles grew rapidly to adult size within 2 yr. Asymptotic size (L∞, von Bertalanffy growth model) ranged from 89 to 97 mm, and maximum size from 100 to 113 mm, on platforms. Growth rate and maximum size were lower on the notch. Our empirical observations and simulation models suggest that these populations are relatively stable on a decadal time scale. The frequency and magnitude of recruitment pulses and high rate of adult survival provide considerable inertia, enabling persistence of these populations in the face of sporadic climatic extremes.

Climate Warming and Long-Term Trends in Saskatchewan Hay Yield

2016 ◽  
Vol 2 (12) ◽  
pp. 765-768 ◽  
Author(s):  
Paul G. Jefferson
Keyword(s):  
Climate Warming ◽  
Long Term Trends

Is winter coming? How climate warming disrupts the diapause response

2016 ◽  
Author(s):  
Scott A. L. Hayward
Keyword(s):  
Climate Warming

Possible vegetation change in Mountain Altai under climate warming and compiling the prognosis maps

2000 ◽  
pp. 26-31
Author(s):  
E. I. Parfenova ◽  
N. M. Chebakova
Keyword(s):  
Climate Warming ◽  
Vegetation Change ◽  
Climate Change Scenario ◽  
Change Scenario ◽  
Forest Steppe ◽  
Vegetation Map ◽  
Climatic Indices ◽  
Bioclimatic Model ◽  
Open Woodland ◽  
Mountain Taiga

Global climate warming is expected to be a new factor influencing vegetation redistribution and productivity in the XXI century. In this paper possible vegetation change in Mountain Altai under global warming is evaluated. The attention is focused on forest vegetation being one of the most important natural resources for the regional economy. A bioclimatic model of correlation between vegetation and climate is used to predict vegetation change (Parfenova, Tchebakova 1998). In the model, a vegetation class — an altitudinal vegetation belt (mountain tundra, dark- coniferous subalpine open woodland, light-coniferous subgolets open woodland, dark-coniferous mountain taiga, light-coniferous mountain taiga, chern taiga, subtaiga and forest-steppe, mountain steppe) is predicted from a combination of July Temperature (JT) and Complex Moisture Index (CMI). Borders between vegetation classes are determined by certain values of these two climatic indices. Some bioclimatic regularities of vegetation distribution in Mountain Altai have been found: 1. Tundra is separated from taiga by the JT value of 8.5°C; 2. Dark- coniferous taiga is separated from light-coniferous taiga by the CMI value of 2.25; 3. Mountain steppe is separated from the forests by the CMI value of 4.0. 4. Within both dark-coniferous and light-coniferous taiga, vegetation classes are separated by the temperature factor. For the spatially model of vegetation distribution in Mountain Altai within the window 84 E — 90 E and 48 N — 52 N, the DEM (Digital Elevation Model) was used with a pixel of 1 km resolution. In a GIS Package IDRISI for Windows 2.0, climatic layers were developed based on DEM and multiple regressions relating climatic indices to physiography (elevation and latitude). Coupling the map of climatic indices with the authors' bioclimatic model resulted into a vegetation map for the region of interest. Visual comparison of the modelled vegetation map with the observed geobotanical map (Kuminova, 1960; Ogureeva, 1980) showed a good similarity between them. The new climatic indices map was developed under the climate change scenario with summer temperature increase 2°C and annual precipitation increase 20% (Menzhulin, 1998). For most mountains under such climate change scenario vegetation belts would rise 300—400 m on average. Under current climate, the dark-coniferous and light-coniferous mountain taiga forests dominate throughout Mountain Altai. The chern forests are the most productive and floristically rich and are also widely distributed. Under climate warming, light-coniferous mountain taiga may be expected to transform into subtaiga and forest-steppe and dark-coniferous taiga may be expected to transform partly into chern taiga. Other consequences of warming may happen such as the increase of forest productivity within the territories with sufficient rainfall and the increase of forest fire occurrence over territories with insufficient rainfall.

Amount and timing of permafrost carbon release in response to climate warming

Tellus B ◽  
2011 ◽  
Vol 63 (2) ◽  
Author(s):  
Kevin Chaefer ◽  
Tingjun Zhang ◽  
Lori Bruhwiler ◽  
Andrew P. Barrett
Keyword(s):  
Climate Warming ◽  
Carbon Release
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