Archive

Archive for March, 2005

Landscape Ecology A neutral landscape model (NLM)…

March 29, 2005 Leave a comment

Landscape Ecology

A neutral landscape model (NLM) is any model used to generate patterns in the absence of the specific processes being studied. Predictions from NLMs are intended to define the expected pattern in the absence of a specific process.

A disturbance is defined as a relatively discrete event that disrupts the structure of an ecosystem, community, or population and changes resource availability or the physical environment.

Advertisements
Categories: architecture, school

Landscape Ecology Sample Test mosaic: idea of a h…

March 29, 2005 Leave a comment

Landscape Ecology
Sample Test

mosaic: idea of a holistic landscape created from the interaction of different landscape components. Streams, forest stands, crop fields, roads, ponds and meadows are some examples of these landscape components.
boundary:
connectivity: spatial continuity of a habitat or cover type across a landscape.
matrix: background cover type in a landscape, characterized by extensive cover and high connectivity; not all landscapes have a definable matrix.
patch: surface area that differs from its surroundings in nature or appearance.
corridor: relatively narrow strip of a particular type that differs from the areas adjacent on both sides.
edge: portion of an ecosystem or cover type near its perimeter and w/in which environmental conditions may differ from interior locations in the ecosystem; also used as a measure of the length of adjacency between cover types on a landscape.
metapopulation: composite set of local populations (subpopulations) w/in some larger area, where typically migration from one local population to some other patches is possible.

1. Give one important reason that the diversity of plants and animals is often lost in smaller patches. Briefly explain in two or three sentences.
As patch size decreases, so does usable land area. With smaller land area, there is more competition between plant and animal species to utilize the land. Only so many plants can physically exist in a given area; there are several models to explain competition: direct competition, whereby one species completely takes over; and reaction-diffusion models, whereby several species coexist w/ one or two species precariously maintaining competitive advantage until some natural event shifts favor to a different species.
As patch size decreases, edge increases. This exposes more interior animal and plant species to conditions they would normally not encounter, and therefore greatly reduces their habitat. These species must either adapt, move, or face extinction.

2. Give one important reason that patch edges change their characteristics and/or location over time. Briefly explain in several sentences.
Patch edges may change characteristics due to climate change. A shift in temperature may result in not only hotter or colder conditions, but also may change moisture, wind, and subsequently access to solar radiation. These altered conditions may benefit certain species and be detrimental to others, altering the balance of plant and animal species.

3. A patch with little interior habitat might have what kind of shape? Draw an example. Briefly explain why this is important in landscape ecology.

Certain plant and animal species thrive on interior habitat, and will face drastically reduced population or extinction if faced w/ edge conditions.

4. How could you measure the connectivity of a corridor? Why is this important to do?
Track migration of specific indicator animal species or keystone species between connected patches. While some species require a corridor for to successfully locate food and mates in other patches (corridor serving as life line in an altered habitat), other predatorial species may utilize the corridor in order to venture to previously unexplored patches and find unprotected prey.
Definition:corridor: relatively narrow strip of a particular type that differs from the areas adjacent on both sides.

5. How can a corridor serve as a source for some objects and a sink for others? Give an example of each.
metapopulation: composite set of local populations (subpopulations) w/in some larger area, where typically migration from one local population to some other patches is possible.
A corridor may serve as a source when edge conditions favor the proliferation a certain species. It may also provide a means by which a species may move to another patch where it may proliferate. On the other hand, the edge condition presented by a corridor may not contain the resources necessary to sustain a population in a given patch, resulting in population declination. A corridor may also allow the introduction of a predator species that will result in a diminished prey population.

6. You are the newly hired landscape ecologist in the Xyz County Planning Office (congratulations!). In the first office meeting, you are asked to explain what is meant by “edge effect” and why it is of oconcern for environmentally sound planning. In the space below, provide your answer (about 2 paragraphs or 15 sentences). Feel free to draw a figure(s) if that would be helpful.
edge: portion of an ecosystem or cover type near its perimeter and w/in which environmental conditions may differ from interio locations in the ecosystem; also used as a measure of the length of adjacency between cover types on a landscape.
Edge effect occurs at the border between 2 distinct patches. It can have both abiotic and biotic causes and effect, meaning that it can result from naturally occurring or man-made phenomena (road building, construction, weather – abiotic) or from the interaction between plants, animals, or microorganisms (biotic). The edge effect defines how these 2 patches interact – for instance, if a road is placed through a forest, the forest edge will be inhabited by plant species that prefer more direct sunlight (as opposed to living under the cover of forest canopy) and animal species prefer more open space or the food provided by the edge plant species. This edge is neither distinctly ‘forest’, nor is it obviously road. It inhabits a stage of in-betweeness that buffers between 2 distincly different patches.

Categories: architecture, school

Landscape Ecology Chapter 5: Quantifying Landscape…

March 27, 2005 Leave a comment

Landscape Ecology
Chapter 5: Quantifying Landscape Pattern
all quotes obtained from Landscape Ecology, Turner et al., 2001

presently, more can be quantified about landscape pattern than is understood about its ecological importance.

many analyses of landscape pattern are conducted on land use/land cover data that have been digitized and stored in GIS. aerial photography, digital remote sensing; censuses are applicable for older data (aerial photography only goes back to roughly the 1930’s, and its quality is correlated w/ age). also field mapped data for smaller landscapes. Anderson classification system: Level I = agricultural land, Level II = cropland/pasture, orchards/groves/vineyards/horticulture, confined feeding operations, other agricultural land; etc. breakdown between raster and vector based digital format. important to consider accuracy of source – age of data, extent of aerial coverage, map scale, political bounderies, resolution, positional accuracy, etc.

must be careful when examining and comparing data that it is relevant, and there must be a clear idea of what is being compared. danger of pseudoreplication, which occurs when comparison are made among samples that are not truly independent.

importance of proper classification scheme. one map may look entirely different if broadly categorized (non-forest, lodgepole pine, whitebark pine VS non-forest, early successional, mid successional, late successional, late successional/non-forest). depends on the question of course, and classification can start broadly with further breakdown and elaboration of detail.

scale must be clearly defined – the smaller the scale, the less detail; coarser grain size can obscure or misconstrue actual boundaries between cover types, and rare or small amounts of vegatation may not appear. ‘the grain size of the map should be two to five times smaller than the spatial features being analyzed, and map extent should be two to five times larger than the largest patches.

how to identify a patch? a patch is defined as ‘a nonlinear surface area differing in appearance from its surroundings.’ a variety of rules for defining a patch in a map, such as the 4-neighbor rule.

Categories: architecture, school

Landscape Ecology Chapter 4: Causes of Landscape P…

March 27, 2005 Leave a comment

Landscape Ecology
Chapter 4: Causes of Landscape Pattern
all quotes obtained from Landscape Ecology, Turner et al., 2001

‘broad-scale variability in the abiotic environment sets the constraints within which biotic interactions and disturbances act.’

‘Climate refers to the composite, long-term, or generally prevailing weather of a region, and climate acts as a strong control on biogeographic patterns through the distribution of energy and water.’ climate affects and determines the landform over long periods (thousands of years), while the landform may control the climate over short periods (immediate) – feedback loop. paleoecology is the study of individuals, populations, and communities of plants and animals that lived in the past and their interactions with and dynamic responses to changing environments – this helps to understand the current interaction of everything in nature and can help to predict the outcome of current interactions or future hypothetical situations.

climate varies w/ latitude and continental position. 32 degrees N coastal may have entirely different climate than 32 degrees N interior. elevation also alters temperature: low elevation coastal water temperature may be drastically different from low elevation interior water temperature, while low elevation coastal and high elevation coastal have different temperature – combination of latitude and continental position create entirely different biomes, and can create different climates locally.

long term climate change: each glacial-interglacial period lasts roughly 100,000 years: ~90,000 yrs of gradual cooling followed by rapid heating and ~10,000 yrs of warmth. earth is currently at the end of a warmth period, meaning the temp should begin dropping soon (part of the 90,000 yr gradual cooling), but buildup of CO2 will offset the cooling and instead incite at least a 2degreeC temp increase.

earth’s biota must respond to temp changes. plant species can respond to climate change by either: evolving and speciating; migrating long distances; or become extinct. glacial-interglacial cycles trigger the disassembly of communities followed by a reassembly that is unpredictable in terms of either species composition or abundance. disturbance regimes are very sensitive to changes in climate : the 44yr fire cycle of northwestern Minnesota become an 88yr cycle with the onset of cooler, moisture conditions created by the onset of the Little Ice Age after 1700CE. different plant species respond to changes in climate according to their own characteristics – climate change does not necessarily affect entire biomes, but the overall composition of the biome may change according to the abilities of each species to withstand change.

landforms range from flat plains to rolling hills to craggly mountains that are nastier than yo mom’s crater face! damn! ‘if different areas are composed of similar landforms w/ similar geology, then soil catenas (a chain of connected objects so arranged that each member is closely related to the preceding and following members) and vegetation types may also be expected to be similar.’
four general effects of landform on ecosystem:
1. elevation, aspect, parent materials, and slope of landforms affect air and ground temperature and the quantities of moisture, nutrients, and other materials available at sites w/in a landscape. (south slopes receive more solar radiation, hence drier, warmer conditions – better conditions for plants.
2. landforms affect the flow of many quantities, including organisms, propagules (A part of a plant that can produce another plant: including seeds, roots and rhizomes), energy, and matter through a landscape. steep non-porous slopes will funnel water more effectively and may be a conduit or path for the dissemination of seeds or erosion paths.
3. landforms affect the frequency and spatial pattern of natural disturbances such as fire, wind, or grazing. certain species may act as a natural fire break; forests decrease wind speed; rivers and creeks delineate fields…
4. landforms constrain the spatial pattern and rate or frequency of geomorphic processes, the mechanical transport of organic and inorganic material, that alter biotic characteristics and processes. landforms significantly contribute to the development and maintenance of spatial heterogeneity across a landscape through their multiple effects on soils, vegetation, and animals.

interaction among organisms may result either homogenous or heterogeneous landscapes. 2 species may compete w/ one resulting victor and a homogenous landscape. other times there may be multiple stable states, where multiple species coexist, but one is dominant. a small change in landscape dynamics may shift conditions to where a different species becomes dominant. example of ecotone edge, where a finger of forest may intrude into a grassland and a finger of grassland may intrude into a forest. conditions are left to chance, and the relationship may change so that the forest overtakes the finger of intruding grass, or vice versa.

competition between vegetation may also form ecotones, even where climatic conditions may favor the growth of both species. as temp. changes from north to south, one may assume that two species of tree would intermingle before becoming exclusively species 1 or species2. but competition between the 2 species may create a distinct boundary resulting in clearly defined separate ecotones.

reaction-diffusion models – growing and competing populations are also dispersing across a uniform environment. the landscape takes on a patchy, periodic spatial distribution. predator-prey models will look patchy if the predator is more successful than the prey at diffusive distribution – the patchiness is all that survives of the prey. this is called diffusive instability.

keystone species: Holling (1992) believes that ‘all ecosystems are controlled and organized by a small number of key plant, animal, and abiotic processes that structure the landscape at different scales.’ example of starfish keeping mussels in check – starfish is the keystone species.

dominant organisms can also affect the composition of the entire landscape, within the confines of the abiotic template. certain tree species may become dominant w/in a landscape, and their shade, water intake and evapotransporation, and leaf and seed dispersal may influence the growth and survival of less dominant surround plant species. a beaver dam creates a pond that may flood and alter up to 13% of the inundated landscape. it can saturate soils, resulting in different plant composition along the shore. american bison used to affect the vegetation along their migration paths b/c of what they ate, recycled, trampled. ‘large mammals will act as a mechnaism in pattern formation. more generally, large mammals often direclty alter vegeation and rates of nutrient recylcing.’ human civilazation? hmmm??

humans have altered land cover (habitat or vegetation type present, such as forest, agriculture, and grassland) according to varied land use (way in which and the purposes for which humans employ the land and its resources). resulting change in land cover by way of land use is – ready for this? – land use change! landscapes we may perceive to be natural today probably have a history of human influence that dates back a long time. in the 1600s, practically the entire eastern half of the contiguous 48 states was covered in forest. now forest is very sporadic, although there are areas of intense regrowth as industry in the eastern US has scaled down, most especially in the northeast.

Categories: architecture, school

Landscape Ecology Chapter 3: Intro to Models all q…

March 27, 2005 Leave a comment

Landscape Ecology
Chapter 3: Intro to Models
all quotes obtained from Landscape Ecology, Turner et al., 2001

‘a model is an abstract representation of a system or process.’
as landscape ecology is a new field, the bulk of knowledge is incomplete – there are still many holes left making generalizations and predictions difficult. models help to fill this gap by allowing ecologists to use a variety of modeling methods to more accurately predict the outcomes of various interactions. models are used for the same purpose in every field, not just landscape ecology – to help visualize an unknown. ‘…models are employed to explore the consequences of our hypotheses regarding system structure and dynamics.’

as physically modeling landscapes at full scale is near impossible due to the intricacies of and multitude of interactions, ecologists emply experimental manipulations of microlandscapes. these microlandscapes may be completely hypothetical, or they may be physically modeled in the real world. extrapolation of results from microlandscapes to large regions remains a perplexing problem b/c as the size of the model or landscape grows, so too do the amount of interactions. what may be predicted on a small scale may not necessarily correlate on a large scale. ‘models may generate testable hypotheses that can be used to guide field studies by exploring conditions that cannot be manipulated in the field.’

model classification:
deterministic: if the outcome is always the same once all inputs have been assigned
stochastic: if there is a variable of uncertainty in the model, and the model may have a different outcome every time it is run.
analytical: closed form mathematical solution; the result of the model may be easily broadcast to a large sample, i.e. linear, exponential, and logistic growth are equations easily extrapolated.
simulation: open form mathematical solution: the model is so complex that a multitude of complex mathematical equations interact to obtain a result; complexity requires computers; may have different result every time, or may have same result – not necessarily stochastic – all dependant on the interaction of the equations.
dynamic: model represents phenomena that changes through time. simulation models are dynamic.
static: model represents phenomena that do not change through time – lacks a temporal dimension.
-mechanistic: ‘…a mechanistic model attempts to represent dynamics in a manner consistent w/ real world phenomena.’ how is that different from nearly every other model? mechanistic models try to represent real world conditions, as opposed to purely hypothetical situations that may attempt to reproduce results through completely unrealistic processes (situations that may never occur outside the lab).
-process-based: ‘…model components were specifically developed to represent specific ecological processes.’ example: to model how quickly a set # of runners reach the finishing line of a race, a process-based model may map out the amount of runners, their meals and metabolic rates, they rates of dehydration and muscle fatigue, the distance, the weather, and their running histories AS OPPOSED TO defining the amount of runners and a speed variable to determine the average time required to reach the finish line.
-empirical: ‘…a model with formulations based on simple, or correlative, relationships. This term also implies that model parameters may have been derived from date (the usual case…).’
-in reality, most models are a combination of the 3 prior terms, so it is difficult, if not useless, to try to categorize models using these terms.

spatial models are used when ‘the variables, inputs, or processes have explicit spatial locations.’ while not all landscape models require a spatial component, spatial models have become increasingly popular in recent years due to the rise of cheaper, more capable computers, and the fact that spatial locations do, in fact, bear useful information for most landscape questions. there are 3 general conditions for developing a spatial model:
1. ‘spatial pattern may be one of the independent variables in the analysis…how some ecological response variable changes as a funtion of the configuration of landscape elements.’ using a map may be enough for the inclusion of spatial patterning, although it does not necessarily include a temporal dimension.
2. ‘a spatial model is needed when predicting spatial variation of an attribute of interest and how it changes through time.’ an example then shows yearly maps of great britain overlayed w/ color coded density chart showing the change of a species population throughout the landscape. this is somewhat the opposite, or backward analysis of #1. whereas #1 may change the landscape to disover changes in how species interact, #2 observes how species interact w/ an existing landscape over a period of time.
3. ‘a spatial model is required when the question involves sets of processes or biotic interactions that generate pattern.’ the model starts off w/ a blank slate and lets a pattern develop based on the interaction of 2 or more species in response to a set of stimulus.

building a model:
1. define the problem: basically a mission statement; a model may or may not be necessary depending on the complexity of the problem; additionally, this statement helps determine how complex the model needs to be.
2. develop the conceptual model: essentially identify the size of the model, expected interactions and variables, driving variables (which are external to the model – they effect the model but are not themselves affected); also define expected outcomes, scale/grain/resolution – the same interactions may have different outcomes based on the scale/resolution of the model. how many interactions are necessary to define? few or many? some models start simple and add complexity, while others start complex and prune out the unnecessary. flow diagrams are the bomb at this stage!
3. select model type: this involves selecting from the whole mess of terms defined above. analytic should be used if the model is simple, as results are elegant and…simple! but my personal belief is that models should always be stochastic, dynamic, simulations – as that is as close to reality as these terms allow. this is equivalent to decreasing grain size and increasing resolution – reality is composed of as many complex interactions as you can muster, so may as well attempt to model them. PROBLEM – the increase in variables may royally screw the model as what is simulated may not resemble reality due to certain excluded or unrealized variables that do exist in nature.
4. model development: create the model structure through varieties of mathematical equations. it is unclear which equations may be the best, so constant revision may occur. among the model types available: graph theory, diffusion theory, game theory, percolation theory, fractal geometry, chaos theory, optimization theory, aspects of probability theory such as Markov chains or Bayesian models.
5. computer implementation: do existing programs suffice, or are new programs and languages required to create the model? accuracy is supremely important! any mess-up in the coding will screw the model. documentation is also supremely important, either through notation in the code or a manual, b/c what seems logical at the time of coding may not appear logical in the future.
6. parameter estimation: selection of value of model parameters, inputs, and initial values. not the same as calibration, which just tweaks the model. parameter estimation requires that initial values be inserted that are known to be historically accurate – this is the starting point. if the starting values are off-base, then the entire model will be skewed, and no amount of tweaking will help.
7. model evaluation: this step involves comparing the model to real-life examples. did it perform as expected? were the assumptions reasonable? was the input data acceptable? how sensitive is the model behavior to the assumptions? models may be compared, graphically, statistically, or in tabular form. comparisons should be based on model objectives. sensitivity analysis is the evaluation of the relative importance of particular parameters within the model’ – a slight change in one parameter may have a drastic effect on the outcome of the model, while a major change in a different parameter will have a barely perceptible effect.
8. experimentation and prediction: now’s the time to put all the hard preparation to work! if the model seems to work, go forth and conquer – make predictions and analyze landscapes. as models continue to evolve and are able to handle increasingly complex simulations, modeling will move from testing of hypotheses to actual planning, conservation, and design tools.

final notes on models:
1. know thy model
2. errors propogate
3. all models are simplifications of reality
4. there are never enough data
5. high-tech methods to not guarantee a good model
6. keep an open mind

Categories: architecture, school

Nate-dog likes the fro

March 27, 2005 Leave a comment


profile pic
Posted by Hello

Categories: family, haha

napolean and birdman

March 27, 2005 Leave a comment


napoleon3
Posted by Hello

Categories: Austin, haha