Influence of herbivores on a perennial plant: variation with life history stage and herbivore species

Herbivores have diverse impacts on their host plants, potentially altering survival, growth, fecundity, and other aspects of plant performance. Especially for longer-lived plant species, the effects of a single herbivore species can vary markedly throughout the life of the host plant. In addition, the effects of herbivory during any given life history stage of a host plant may also vary considerably with different types of herbivores. To investigate the effects of herbivory by black-tailed deer (Odocoileus hemionus columbianus) and snails (Helminthoglypta arrosa and Helix aspersa) on a nitrogen-fixing shrub, Lupinus chamissonis, we established three exclosure experiments in a sand dune system on the coast of northern California. These experiments documented that deer browsing significantly reduced the volume and growth rate of lupines in the seedling and juvenile life stages. Since plant volume was strongly correlated with aboveground dry biomass for lupines, such herbivore-induced reductions in volume should translate into losses of aboveground biomass. Deer browsing also significantly altered the likelihood of attack by and density of a leaf-galling cecidomyid fly (Dasineura lupinorum), suggesting that a vertebrate herbivore indirectly affected an invertebrate herbivore in this system. Although deer did not significantly affect the survival of lupine seedlings and juveniles, individuals protected from deer had consistently greater survival in the two separate experiments. Our results revealed that snails did not have a significant effect on the survival or growth of juvenile plants, despite being common on and around lupines. An exclosure experiment revealed that herbivory by deer significantly reduced the shoot lengths of mature shrubs, but led only to a minimal reduction in growth rates. In addition, we found that browsed shrubs had significantly greater inflorescence production, but also produced individual seeds with significantly reduced mass. Collectively, these data indicate that deer and snails have widely differing effects on their shared host plant; browsing by deer indirectly affects insect herbivores, and the impacts of deer change markedly with the life history stage of their host plant.     

Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data

Aim

There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma (Puma concolor), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data.

Location

Southern California, USA.

Methods

We estimate resistance using presence‐only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas.

Results

We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory.

Main conclusions

We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance‐based approaches, such as least‐cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.

     

Domain structure of riboflavin synthase.

Riboflavin synthase of Escherichia coli is a homotrimer of 23.4 kDa subunits catalyzing the formation of the carbocyclic ring of the vitamin, riboflavin, by dismutation of 6,7-dimethyl-8-ribityllumazine. Intramolecular sequence similarity suggested that each subunit folds into two topologically similar domains. In order to test this hypothesis, sequence segments comprising amino-acid residues 1-97 or 101-213 were expressed in recombinant E. coli strains. The recombinant N-terminal domain forms a homodimer that can bind riboflavin, 6,7-dimethyl-8-ribityllumazine and trifluoromethyl-substituted 8-ribityllumazine derivatives as shown by absorbance, circular dichroism, and NMR spectroscopy. Most notably, the recombinant domain dimer displays the same diastereoselectivity for ligands as the full length protein. The minimum N-terminal peptide segment required for ligand binding comprises amino-acid residues 1-87. The recombinant C-terminal domain comprising amino-acid residues 101-213 is relatively unstable and was shown not to bind riboflavin but to differentiate between certain diastereomeric trifluoromethyl-8-ribityllumazine derivatives. The data show that a single domain comprises the intact binding site for one substrate molecule. The enzyme-catalyzed dismutation requires two substrate molecules to be bound in close proximity, and each active site of the enzyme appears to be located at the interface of an N-terminal and C-terminal domain.     

STRUCTURE-FUNCTION RELATIONSHIPS IN THE ADIPOSE CELL : III. Effects of Bovine Serum Albumin on the Metabolism of Glucose and the Release of Nonesterified Fatty Acids and Glycerol by the Isolated Adipose Cell

Serum albumin stimulates the uptake of U-glucose-¹⁴C and the incorporation of ¹⁴C-counts into triglyceride glycerol and inhibits the incorporation of ¹⁴C-counts into triglyceride fatty acids by isolated adipose cells; insulin and epinephrine enhance these effects. In the absence of hormones, these responses to albumin increase with increasing albumin concentration. In the presence of insulin, a qualitatively similar pattern of increasing responses to albumin is observed; the enhancement of each response by insulin is, however, only slightly potentiated by higher albumin concentrations. In contrast, in the presence of epinephrine, these responses to albumin are maximal at the lowest albumin concentration tested, 0.1%; the enhancement of each response by epinephrine is similarly maximal at 0.1% albumin, but decreases rapidly as the albumin concentration is raised. Increasing serum albumin concentrations do, however, stimulate the release of fatty acids and glycerol by epinephrine-treated cells increasingly until a plateau, determined by the epinephrine dose, is reached. These data support the suggestion that intracellular fatty acid levels function in the regulation of adipose cell activity, and further suggest that serum albumin plays a role in determining the metabolic fate of these fatty acids.     

STRUCTURE-FUNCTION RELATIONSHIPS IN THE ADIPOSE CELL : I. Ultrastructure of the Isolated Adipose Cell

A method is described for preparing isolated rat adipose cells for electron microscopy. The ultrastructure of such cells and their production of ¹⁴CO₂ from U-glucose-¹⁴C were studied simultaneously in the presence of insulin or epinephrine. Each adipose cell consists of a large lipid droplet surrounded by a thin rim of cytoplasm. In addition to typical subcellular organelles, a variety of small lipid droplets and an extensive system of membranes characterize the cell's cytoplasm. A fenestrated envelope surrounds the large, central lipid droplet. Similar envelopes surround cytoplasmic lipid droplets occurring individually or as aggregates of very small, amorphous droplets. Groups of individual droplets of smaller size also occur without envelopes. The system of membranes consists of invaginations of the cell membrane, vesicles possibly of pinocytic origin, simple and vesiculated vacuoles, vesicles deeper in the cytoplasm, flattened and vesicular smooth surfaced endoplasmic reticulum, and Golgi complexes. Neither insulin nor epinephrine produced detectable ultrastructural alterations even when cells were incubated under optimal conditions for the stimulation of ¹⁴CO₂ evolution. Structural responses of the isolated adipose cell to hormones, if such occur, must, therefore, be dynamic rather than qualitative in nature; the extensive system of smooth surfaced membranes is suggestive of compartmentalized transport and metabolism.     

A comparison of individual‐based genetic distance metrics for landscape genetics

A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual‐based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (although several other metrics performed similarly), but only when they were derived from multiple principal components axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.