John F. Wilmshurst

PREDICTIVE MODELS OF MOVEMENT BY SERENGETI GRAZERS

Many animal species are unevenly distributed across the landscape, in spatial patterns that continually shift over time. Such a shifting mosaic is thought to have profound implications for the persistence and stability of ecosystems. Management and conservation of natural systems would be enhanced if we could accurately predict movement. Such prediction has not yet been possible. Here we use an extensive set of field data on food abundance and quality, combined with experimentally derived measures of nutritional value, to predict the spatial distribution of Thomsons gazelles (Gazella thomsoni thomsoni Gunter) on the Serengeti Plains of East Africa. Twelve plausible models, based on alternate foraging objectives or movement rules, were assessed against field data on food and grazer abundance gathered at biweekly intervals (every two weeks) over the course of the wet seasons in two different years. Nomadic movements of gazelles closely tracked changes in the spatial distribution of short grass swards. Gazelles left short grass patches when local daily energy intake dropped below the expected intake averaged across the landscape. Subsequent redistribution of gazelles among neighboring patches was proportional to daily rates of energy intake in each patch. Thus, nomadic movements by Thomsons gazelles were predictable on the basis of local energy gain. This suggests that adaptive behavioral models can provide useful predictive tools for understanding the dynamics of complex natural systems.

The allometry of patch selection in ruminants

An axiomatic feature of food consumption by animals is that intake rate and prey abundance are positively related. While this has been demonstrated rigorously for large herbivores, it is apparent from patch selection trials that grazers paradoxically tend to prefer short, sparse swards to tall, dense swards. Indeed, migratory herbivores often shift from areas of high to low sward biomass during the growing season. As nutritional quality is an inverse function of grass abundance, herbivores appear to sacrifice short–term intake for nutritional gains obtainable by eating sparse forage of higher quality. Explicit models of this trade–off suggest that individual ruminants maximize daily rates of energy gain by choosing immature swards of intermediate biomass. As body mass is related positively to both ruminant cropping rates and digestibility, there should be an allometric link between grass abundance and energy maximization, providing a tool for predicting patterns of herbivore habitat selection. We used previously published studies to develop a synthetic model of trade–offs between forage abundance and quality, predicting that optimal sward biomass should scale allometrically with body size. The model predicts size–related variation in habitat selection observed in a guild of grazing ungulates in the Serengeti ecosystem.

WHAT CONSTRAINS DAILY INTAKE IN THOMSON’S GAZELLES?

We tested whether cropping or digestion by Thomsons gazelles ( Gazella thomsoni) constrains daily energy intake under sward conditions normally encountered during the growing season. Distinguishing between these alternatives is important in un- derstanding grass-grazer interactions and modeling grazer energetics. Grazing trials on artificial swards showed that gazelles had a monotonically saturating functional response, but that relationships between grazing rate and forage density changed with grass height. Grazing rate was positively related to biomass on short swards, yet there was no significant relationship for tall swards. Bite mass and bite rate also differed in their relationship to biomass across sward heights, with the strongest relationships being found on short swards. Bite rate and bite mass were inversely related, as predicted by current theory for dense grass swards. Voluntary energy intake on a daily basis was a positive function of the digestible energy content of forage, but a negative function of sward biomass. Therefore, our results indicate that daily energy intake is constrained by digestive processes on swards with biomass .25 g/m 2 , whereas intake is constrained by cropping processes at lower sward biomass. Our data additionally suggest that variation in bite rate and bite mass with sward height could permit a small ruminant to select high-quality grass, thereby achieving high energy gain on immature swards.

Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices

Hyperspectral remote sensing data with a greater number of bands and narrower bandwidths can be effectively exploited for the study of ecosystem patterns and processes. Hyperspectral remote sensing of semiarid mixed grassland faces the following two challenges, however: (i) providing a good understanding of the performance of different vegetation indices (VIs) in estimating biophysical properties of grassland with a small amount of green vegetation, a large amount of dead material on the ground, and variable soil–ground conditions; and (ii) examining the spatial characterization of hyperspectral remotely sensed data to optimize sampling procedures and address scaling issues. Using ground-based hyperspectral and biophysical data, this study has compared the predictive capability of VIs for estimation of grassland leaf area index (LAI) (this paper) and examined the spatial variation of grassland LAI (the companion paper). The results in this paper indicate that the relationships between grassland LAI and VIs are significant. The performance of the renormalized difference vegetation index (RDVI), adjusted transformed soil-adjusted vegetation index (ATSAVI), and modified chlorophyll absorption ratio index 2 (MCARI2) was slightly better than that of the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating the cellulose absorption index (CAI) as a litter factor in ATSAVI, a new VI was computed (L-ATSAVI), and it improved the LAI estimation capability in our study area by about 10%.

Patch selection by red deer in relation to energy and protein intake: a re-evaluation of Langvatn and Hanley's (1993) results

Langvatn and Hanley (1993) recently reported that patch use by red deer (Cervus elaphus) was more strongly correlated with short term rates of intake of digestible protein than dry matter. Such short term measures overlook effects of gut filling, which may constrain intake by ruminants over longer time scales (i.e., daily rates of gain). We reanalyzed Langvatn and Hanleys data using an energy intake model incorporating such a processing constraint, to determine whether their conclusions are robust. We found that the use of patches by red deer was just as strongly correlated with an estimate of the daily rate of intake of digestible energy as one of digestible protein during four out of seven trials, but slightly lower in three out of seven trials. In all cases, daily intake of digestible energy was a much better predictor of patch preference by red deer than was the intake of dry matter. Our reanalysis suggests that the daily intake of energy was highly correlated with that of protein in these trials, as may often be the case for herbivores feeding on graminoids. Hence the observed pattern of patch use by red deer could simultaneously enhance rates of both protein and energy intake.

Energy gains predict the distribution of plains bison across populations and ecosystems

Developing tools that help predict animal distribution in the face of environmental change is central to understanding ecosystem function, but it remains a significant ecological challenge. We tested whether a single foraging currency could explain bison (Bison bison) distribution in dissimilar environments: a largely forested environment in Prince Albert National Park (Saskatchewan, Canada) and a prairie environment in Grasslands National Park (Saskatchewan, Canada). We blended extensive behavioral observations, relocations of radio-collared bison, vegetation surveys, and laboratory analyses to spatially link bison distribution in the two parks and expected gains for different nutritional currencies. In Prince Albert National Park, bison were more closely associated with the distribution of plants that maximized their instantaneous energy intake rate (IDE) than their daily intake of digestible energy. This result reflected both bisons intensity of use of individual meadows and their selection of foraging sites within meadows. On this basis, we tested whether IDE could explain the spatial dynamics of bison reintroduced to Grasslands National Park. As predicted, bison distribution in this park best matched spatial patterns of plants offering rapid IDE rather than rapid sodium intake, phosphorus intake, or daily intake of digestible energy. Because the two study areas have very different plant communities, a phenomenological model of resource selection developed in one area could not be used to predict animal distribution in the other. We were able, however, to successfully infer the distribution of bison from their foraging objective. This consistency in foraging currency across ecosystems and populations provides a strong basis for forecasting animal distributions in novel and dynamic environments.

Comparison of different methods for measuring leaf area index in a mixed grassland

Available LAI instruments have greatly increased our ability to estimate leaf area index (LAI) non-destructively. However, it is difficult to infer from existing studies which instrument has the advantages in measuring LAI over other instruments for grassland ecosystems. The objective of our study was to compare the LAI estimates by two instruments (AccuPAR, and LAI2000), and correlate the LAI measurements to remote sensing data for a mixed grassland. Leaf area index of four grass communities was measured by both the destructive method and instruments. Ground canopy reflectance was measured and further calculated to be LAI-related vegetation indices. Statistical analysis showed that destructively sampled LAI ranged from 0.61 to 5.7 in the study area. Both instruments underestimated LAI in comparison with the destructive method. However, the LAI2000 is better than AccuPAR for estimating LAI. Comparison of four grass communities indicated that the lower the grass LAI, the greater the underestimated percenta...

Studying mixed grassland ecosystems II: optimum pixel size

It was determined in a companion paper that the litter-corrected adjusted transformed soil-adjusted vegetation index (L-ATSAVI) was the best leaf area index (LAI) indicator in a mixed grassland ecosystem. To optimize the sampling procedures and address the scaling issues for the mixed grass ecosystem, this study examined the dominant scale of spatial variation in both LAI and L-ATSAVI using two methods, namely Mexican hat wavelet analysis and semivariogram analysis. The results showed that both methods can identify grassland spatial variation, and the cyclicity (the nature repetition in a dataset) of grassland LAI was about 140 m along the central transect of five parallel transects within the study area. The advantage of wavelet analysis over semivariogram analysis for spatial pattern interpretation was that it could identify the exact location of the transition. The wavelet analysis demonstrated that the cyclicity of L-ATSAVI also corresponded well with features of grassland LAI along the transect. Therefore, following the sampling theorems, a pixel size of less than 35 m will retain most of the spatial variation of grassland LAI in our study area. In terms of this optimum pixel size, the scale of ground-based hyperspectral data and LAI along the transect was simulated using a low-pass filtering procedure with a 30 m moving window. Statistical analysis indicated that scale-simulated L-ATSAVI could significantly explain more grassland LAI (r2 up to 89%) than the original 3 m resolution. This conclusion can be further applied to select the optimal pixel size of remote sensing images and detect the hierarchical characteristics in a grassland landscape.

Reflectance measures of grassland biophysical structure

The goal of this study is to develop an efficient method to retrieve vegetation biophysical properties based on ground LAI measurements and satellite data, and thus avoid the labour‐intensive and time‐consuming process for collecting biomass and canopy height in the future. The field data was conducted in Grasslands National Park (GNP), Saskatchewan, Canada. The two vegetation indices, ATSAVI and RDVI, were derived from SPOT 4 HRV images to estimate LAI and to prepare LAI and biophysical maps for the GNP. The results demonstrated strong relationships between LAI and selected vegetation indices. However, a detailed accuracy assessment indicated that ATSAVI was likely to be better in estimating and mapping LAI than the RDVI. The accuracy of the LAI map was calculated to be 66.7%. The significant relationship between measured LAI and the biophysical data solves the difficulty for mapping biophysical information due to insufficient sampling coverage for GNP.

Comparison of Laboratory and Field Remote Sensing Methods to Measure Forage Quality

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 °C, Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r2 = 0.63). However, the relationship between canopy reflectance and the other forage quality variables was not strong.