Diet for People With Tachycardia

Heartbeat drawing. Photo Credit Peshkova/iStock/Getty Images

Tachycardia is a rapid heart beat. Your heart normally beats from 60 to 100 times per minute when you are in a resting state of non-exertion. A heart rate of more than 100 beats per minute at rest is considered too fast, and is a case of tachycardia. The condition can be temporary or a more chronic issue stemming from heart disease or lifestyle choices. Following a healthy diet may be part of your doctor's plan for treatment.

Caffeine-Free Beverages

Drink tea over caffeinated beverages. Photo Credit Duncan Smith/Photodisc/Getty Images

Stimulant use can be a cause of tachycardia. Certain medications, street drugs and caffeine are all stimulants. To reduce your episodes of tachycardia or to eliminate the risk of developing the abnormality, incorporate only decaffeinated, non-alcoholic beverages into your diet. Some teas and coffees that are labeled as decaffeinated may contain trace amounts of the substance, so check with your doctor to determine how much caffeine, if any, you can safely consume.

Low Fat

Choose low fat yogurt. Photo Credit George Doyle/Stockbyte/Getty Images

Heart disease is both a cause of and a risk factor for developing a rapid heart beat. High cholesterol levels and obesity, two conditions that often go hand in hand, can increase your risk of heart disease and complications like tachycardia. Follow a low-fat diet to lower your cholesterol and lose weight if needed. Choose part-skim cheese, drink nonfat or reduced fat milk instead of full fat varieties. Low-fat or fat-free yogurt is a good source of calcium and protein to help keep you healthy. Cut back on fried foods, snack items and pastries that may be very high in fat.

DASH Diet

Fruits and fresh vegetables. Photo Credit Medioimages/Photodisc/Photodisc/Getty Images

Following the DASH diet -- Dietary Approaches to Stop Hypertension -- may be appropriate for many tachycardia sufferers. High blood pressure can lead to an abnormally fast heartbeat. The DASH diet encourages the inclusion of fruits, vegetables, low fat dairy, protein and whole grains to keep your blood pressure at normal levels and to reduce your risk of heart disease and stroke. Low sodium is a key to the DASH diet; the National Heart Blood and Lung Institute recommends limiting your sodium intake to between 1,500 and 2,300 milligrams daily to lower hypertension. You can achieve these lower levels of sodium by using other types of herbs to season your meats and vegetables and rinsing canned vegetables with water before serving. Reduce your consumption of processed, packaged foods like lunch meats, frozen dinners and salty snacks to limit your salt intake as well.

Reduced Sugar

Reduce sugar intake. Photo Credit Medioimages/Photodisc/Photodisc/Getty Images

Monitor your sugar intake if you have tachycardia. Cleveland-area physician Dr. James Frackelton explains that when you eat a lot of sugar on an empty stomach, your body reacts by over-producing pancreatic enzymes, insulin and adrenaline. Your heart can begin to beat more quickly with the surge of hormones running through your bloodstream. It may not be necessary to cut out all sugar, but to eat sweets in moderation with your normal meals to keep your blood sugar on an even keel.

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The Effects of a Fast Heart Rate

If your heart starts beating too fast it can strain your system. Photo Credit Stockbyte/Stockbyte/Getty Images

Overview

Tachyarrhythmia is the medical term used when your heart rate gets too fast. Tachycardia is diagnosed when your heart rate, or pulse, exceeds 100 beats per minute on a regular basis. You can also develop fibrillation, which means your heart is beating faster than 350 beats per minute. In some cases, such as when you are working out, recovering from an illness or responding to an emergency, your heart rate may temporarily get high, but then it returns to normal when the activity or situation stops. This is a normal reaction. However, heart disease and other conditions can lead to chronic tachyarrhythmia, which in severe cases can be fatal.

Palpitations

A rapid heart rate may occur occasionally, such as when you are stressed or when you ingest caffeine. St. Jude Medical Center states that not all cases of tachyarrhythmia cause harmful effects or require treatment. However, a fast heart rate may lead to palpitations, in which you can feel your heart pounding or beating quickly in your chest or throat. If you notice that your heart is beating faster than it should with no known cause, you need to see your doctor for tests to rule out a serious condition.

Additional Effects

According to Medtronic, if your heart starts beating too fast it can strain your system, because your heart can not effectively deliver the blood and oxygen your body needs. This can lead to feeling tired, dizzy, lightheaded and suffering from fainting spells. This condition can occur in any one of the heart's four chambers. No matter what part of your heart muscle is affected, your symptoms will be the same. However, if this condition originates in the upper chambers of the heart (atrium), you are less likely to have severe symptoms.

Severe Symptoms

The Heart Rhythm Society states that irregular heart rates that originate in the lower two chambers of the heart (ventricles), are usually the most dangerous. If you have heart disease or are at a high risk for it, talk to your doctor if you experience chest pain or pressure, extreme fatigue or weakness, vision changes or trouble breathing. If these symptoms occur alone or along with a rapid pulse, they can be a warning sign of a life-threatening event.

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Weight Loss for a 52-Year-Old

A mature woman is in a yoga pose. Photo Credit Janie Airey/Photodisc/Getty Images

Weight loss often becomes difficult by age 52, simply because your metabolism decreases with age. Sticking within an age-appropriate daily caloric allotment and getting regular exercise, however, will help you lose weight after you reach this point in life -- and keep the weight off long-term. Using a meal plan makes it easier to control your overall caloric intake for effective weight loss.

Calorie Recommendations

At age 52, most women need about 1,200 calories daily -- and many men require about 1,600 calories daily -- for effective weight loss. The National Heart, Lung and Blood Institute suggests that 1,200- to 1,600-calorie diets are appropriate for weight loss in adults, and Harvard Health Publications points out that women need a daily minimum of 1,200 calories and men at least 1,500 calories daily unless under medical supervision. Active 52-year-old adults need more calories than those who are sedentary. If you’re losing more than 2 pounds weekly, slightly boost your intake.

Meal Plans

Using meal plans included in the publication "Dietary Guidelines for Americans, 2010" is an excellent way to stay within your weight-loss calorie allotment. The 1,200-calorie plan includes 1.5 cups of vegetables, 1 cup of fruit, 3 ounces of protein-rich foods -- such as lean meats, egg whites, seafood, soy products, poultry, nuts and seeds -- 4 ounces of grains, 2.5 cups of dairy foods and 4 teaspoons of oils. The 1,600-calorie plan consists of 2 cups of vegetables, 1.5 cups of fruit, 5 ounces of protein foods, 5 ounces of grains, 3 cups of dairy products and 5 teaspoons of oils daily. Both of these meal plans allow for 121 additional calories from foods of your choice.

Sample 1,200-Calorie Menu

For breakfast, try 1 cup of oatmeal, 1 cup of plain Greek yogurt, 1/2 cup of strawberries and 1/3 ounce of sliced almonds. A healthy mid-morning snack might include 1.5 ounce of reduced-fat cheese and 1/2 cup of sliced apples. At lunchtime, go for 2.5 ounces of grilled chicken, 1/2 cup of brown rice, 2 cups of leafy greens and 1 tablespoon of Italian salad dressing. Snack on a cup of low-fat cottage cheese, five whole-grain crackers and 2/3 ounce of peanuts in the afternoon. Make a dinner of 2.5 ounces of grilled salmon, 1/2 cup of quinoa and 1/2 cup of steamed broccoli.

Sample 1,600-Calorie Menu

A nutritious breakfast might include 1 cup of ready-to-eat, whole-grain breakfast cereal, 1 cup of low-fat milk, 1 cup of honeydew melon and three egg whites. For a morning snack, try 1 cup of Greek yogurt, 1/2 cup of blueberries and 1 ounce of mixed nuts. A small turkey burger on a whole-grain bun, one slice of reduced-fat cheese and 1 cup of tomato wedges topped with 1 tablespoon of Italian salad dressing adds up to a healthful lunch. Pop 3 cups of popcorn for your afternoon snack. A nutritious dinner might include 3 ounces of grilled chicken, 1 cup of cooked asparagus, 1 teaspoon of vegetable oil and 1/2 cup of whole-grain couscous.

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Does Going for a Walk Immediately After Dinner Help You Lose Weight?

Woman walking on a trail in autumn Photo Credit Anetlanda/iStock/Getty Images

Finding a moderate-intensity exercise you enjoy and building it into your daily routine can help you lose weight. Walking at a regular time, like first thing in the morning or right after dinner, can increase your chances of sticking to your exercise plan on a long-term basis. That is key to successful weight loss.

The Facts

To lose a pound of fat, you need to burn 3,500 calories, according to Harvard Health Publications. Generally speaking, a 1-mile walk burns about 100 calories, but you can lose more by walking faster or longer. Maximize your walk’s fat-burning potential by walking at a pace of 3 to 4 mph. You will need to walk on most, if not all, days of the week to achieve weight-loss results through walking.

Exercising on a Full Stomach

A popular myth cautions against exercising right after you eat due to the likelihood of cramps or other digestive woes. Your body diverts as much as 25 percent of its blood flow to the digestive track right after eating, which can cause competition with your muscles if you engage in high-intensity exercise, like jogging, right after eating. Robert McMurray, a media spokesperson for the American College of Sports Medicine, tells the AARP Bulletin that this competition is not an issue with light-to-moderate intensity exercise.

Post-Dinner Exercise and Increased Fat Burning

A 2007 study by Surrey University and Imperial College London published in the “Journal of Endocrinology” found that exercising after dinner might help elevate hormones that suppress appetite. Twelve adult volunteers consumed the same breakfast. After the meal, half of them rode an exercise bike for an hour while the other half did not exercise. Researchers then encouraged the volunteers to eat whatever they liked. The exercise group did eat more after their exercise session, but — after taking into account the calories burned during their exercise session — they consumed fewer calories overall than the idle group. The researchers found that the levels of the hormones PYY, GLP-1 and PP, which tell the stomach when it is full, increased during and after exercise.

Considerations

Combining exercise with dietary changes will yield quicker, more effective weight loss than walking alone. Boost your consumption of whole grains, fruits and vegetables and cut out refined sugars and fats. Giving up just one treat a day, like a cookie or glass of wine, can cut 100 calories a day from your diet. Once you lose the weight, you will need to continue your after-dinner walk to keep it off.

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Do You Burn Fat Running for 30 Minutes?

Running Photo Credit Kanawa_Studio/iStock/Getty Images

Carbohydrates and fat are your body's primary sources of fuel for long-distance running. The amount of each nutrient you use depends on duration and exercise intensity. As long as your body has a steady supply of oxygen and carbohydrates, fat will be used as the preferred fuel source to keep you running.

Aerobic Respiration: The Fat Burner

Your body doesn't automatically break down large amounts of fat immediately when you start your 30-minute run. It uses different amounts of carbohydrates and fats during different times in your run, which is similar to how your car changes gears as it accelerates or decelerates. Although your body uses fat as the main fuel source during a 30-minute run, it needs energy immediately to power your muscles and nervous system during the first couple of minutes of the run. Therefore, carbohydrates in the form of glucose are used as the main fuel source. As you continue to run, your body gradually increases the amount of fat used. According to ExRx.net, it can take between 20 to 30 minutes of continuous aerobics for an average person to use 50 percent of his energy expenditure from fat and 50 percent from carbohydrates. People who are more conditioned usually take less to time to achieve this state of energy use.

Percentage Used

The amount of fat used during a 30-minute run can vary, depending on your heart rate, running method and fitness status. During low-intensity exercise for 30 minutes, such as walking or jogging at 20 to 25 percent of your maximum heart rate, a majority of your energy comes from fats, usually about 60 percent. As exercise intensity increases, your body relies more on carbohydrates for energy, which can get as high as 60 percent at 50 percent or more of your maximum heart rate.

Lactate Threshold

When glucose is converted to pyruvate during fat metabolism, it enters your cells' mitochondria -- the energy generators of your body -- to initiate the breakdown of fats. However, when running intensity increases, your body relies more on carbohydrate metabolism to produce more energy. Instead of converting to pyruvate, glucose converts to lactate. When the rate of lactate accumulation in your muscles exceeds the rate of lactate removal, you have reached the lactate threshold. Once this point is reached, your muscles reduce their contraction rate and fatigue settles in, forcing you to slow down or stop to catch your breath. If you're not conditioned in running, your body is more likely to use more carbohydrates than fat for energy. However, a study performed at the University of Guelph in Ontario, Canada, showed that untrained subjects can improve their fat metabolism significantly by about 60 percent after six weeks of high-intensity interval training three days a week.

Exercise Afterburn

Your body continues to burn fat after exercise, not just during the 30-minute run. After a bout of exercise, your body enters a condition called excess post-exercise oxygen consumption -- or EPOC -- in which energy from fats and carbohydrates is used to heal damaged tissues and restore your body to its pre-exercise state. This can last between 15 minutes to 48 hours, according to exercise physiologist Len Kravitz. In a study performed at Appalachian State University in North Carolina, researchers measured the number of calories burned after 10 male cyclists completed 45 minutes of high-intensity cycling. The subjects sustained a high metabolic rate for 14 hours after the exercise session was completed. This concept can be applied to all exercises, including running.

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How Many Carbohydrates Are Burned Running?

Individual factors affect carbohydrate expenditure during runs. Photo Credit fatchoi/iStock/Getty Images

Carbohydrates are the preferred energy source for physical activity but not the only source. When you work out at high intensity, stored carbohydrates, or glycogen, provide fuel for around 20 minutes. However, during long, steady runs, glycogen stores supply energy for approximately 90 to 120 minutes. The difference is attributable to the relationship between exercise intensity and energy source. You use a higher percentage of calories from fat during a distance run than during a faster one-mile run, for example. To ascertain the amount of carbohydrate you burn during running, you need to consider several factors.

Run Fast, Run Far

Your speed and distance affect the total number of calories you burn during a run, as well as the percentage of carbohydrates and fat used to fuel the workout. Another factor that contributes to the amount of carbohydrate burned is your body weight. Your diet may also factor in, particularly if you severely limit carbohydrates. If you follow a typical diet that provides roughly 45 percent to 65 percent of calories from carbs, however, you can estimate the amount of carbohydrate you burn by considering your overall energy expenditure, exercise intensity and duration.

Calories Burned

Calories burned during running vary widely based on your speed and body weight. At a pace of 5 mph, for example, a 240-lb. person burns roughly 872 calories per hour, while a 160-lb. person burns around 584 calories. At 8 mph, energy expenditure for these body weights increases to approximately 1,472 and 986 calories per hour, respectively. The American Council on Exercise provides a physical activity calculator that estimates the number of calories burned per run based on your body weight, speed and the duration of your workout.

Fuel Sources

To determine the amount of carbohydrate you burn during a run based on your estimated calorie expenditure, you need to know approximately what percentage of the calories expended come from carbohydrates. To find this out, it helps to gauge your effort or exercise intensity. Because carbohydrates require less oxygen to burn than fat, you use more energy from glycogen as exertion intensifies. Sprinting, for example, may use fuel exclusively from carbohydrates but can be maintained for only brief periods due to oxygen limitations.

Calories and Heart Rate

You can measure exercise intensity by considering your maximum heart rate. Subtract your age from 220 to predict the maximum heart rate you're likely to achieve when exercising at your highest intensity. Check your pulse during a standard running workout to help you gauge exertion in comparison to your maximum heart rate. At 70 percent of your maximum heart rate, for example, approximately 50 percent of your fuel comes from carbohydrates. At 75 to 80 percent of maximum heart rate, carbohydrate expenditure increases to 65 percent of calories burned. If you burn 500 calories during a run at 70 percent of your maximum heart rate, then, you can estimate that 250 calories come from carbohydrates.

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Is Jogging on an Empty Stomach Healthy?

A light snack before your jog prevents you from stopping prematurely. Photo Credit George Doyle/Stockbyte/Getty Images

Gather a dozen joggers together and ask whether they believe it's healthy to jog on an empty stomach, and you'll likely find the group split right down the middle. While staying away from meals or snacks in the hours leading up to your jog won't likely cause you harm, the consensus among many health experts is that fueling your body with the right food before your workout is the healthiest choice.

Pre-Jog Snacking is Best

If you jog on an empty stomach, it's possible that you'll experience fatigue and cut your jog short -- which will only harm your effort to burn calories. Although some people exercise on an empty stomach with the intention of burning fat quicker, this belief isn't necessarily true. In an article in "The New York Times," Dr. David Prince notes that exercising without eating forces your body to pull energy from its carbohydrate and protein stores before it uses fat. Instead of facing this situation, you're better off to consume a light snack rich in carbohydrates and protein about an hour before your jog. Examples of suitable snacks include a banana and peanut butter or yogurt and granola.

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Top 10 Jogging Strollers

A family jogs in the park together with a stroller. Photo Credit David P. Lewis/iStock/Getty Images

Overview

Whether you are an avid runner, jogger or walker, or you are just trying to get in shape after the birth of your baby, having a jogging stroller, for many parents, is nothing short of a necessity. You will find the prices range depending on the features, but start at approximately $150 for a single jogger stroller. However, the good thing about many jogging strollers is the many years of use that you can get out of them, as many can easily transport your child up to age 5.

Independence Push Chair

The Independence Push Chair by Baby Jogger gets the number one spot according to Allegro Medical. Features of the Independence Push chair include a one-hand easy fold system, quick release rear wheels and adjustable five-point safety harness with rear wheel parking brakes. This chair can accommodate a small passenger who weighs up to 100 lbs., but comes with a hefty price at approximately $900.

BOB Ironman

The BOB Ironman stroller comes equipped with 16-inch wheels and a hand brake, but does not have the swivel option. It collapses in two easy steps, opens with one hand and has a weight capacity of up to 70 lbs. Priced at approximately $350, this jogging stroller is one of the top 10, especially if you want a hip stroller to take your child on long training runs, according to RunningTimes.com.

BOB Revolution

Babble.com rates the BOB Revolution on top of the jogging stroller list with an approximate retail cost of $390. The stroller maneuvers with one hand and folds in two easy steps. The stroller accommodates your child weighing up to 70 lbs. and offers a knob to change the front wheel from a fixed to a swivel position.

Phil and Teds Sport Buggy

Phil and Teds Sport Buggy is a popular pick at JoggingStroller.com. You will find the jogging stroller equipped with a five-point safety harness, foot break, adjustable height handle and the ability to convert for a single seat to two-passenger tandem double stroller. The average retail cost is $450, and the stroller will accommodate your child up to 55 lbs. in the conversion seat and 88 lbs. in the front seat.

Step Safari

The Step Safari is a single jogging stroller with a 12-inch swivel front wheel for easy handling. The adjustable height handle accommodates runners of all heights, has a weight capacity of 50 lbs. and there is plenty of room to store your items with the under-seat storage space. The stroller comes in with an average cost of $170 and rates on the top of the jogging stroller list at Allegro Medical.

Schwinn Free Runner

The Schwinn Free Runner offers a jiggle-free ride with 16-inch wheels, adjustable handle, five-point harness and a hand brake. You can collapse the stroller quickly and it will accommodate your child up to 50 lbs. The average retail cost for the Free Runner is $220, making this a practical, budget-conscious running stroller, according to RunningTimes.com.

Jeep Overland Limited

According to Babble.com, the Jeep Overland Limited is on top of the list of jogging stroller with features, such as an adjustable smart handle, adjustable five-point harness, trip odometer that monitors speed and distance and an adapter for an iPod for music on the move. The stroller will cost you approximately $220 and can hold your child up to 45 lbs.

Valco Matrix

The Valco Matrix is one of the top picks at JoggingStrollers.com and offers an aluminum frame, 12-inch wheels for rough terrain, adjustable handle height and a locking front swivel wheel. You can find the Matrix for approximately $375 and the stroller can accommodate weight up to 48 lbs.

Instep Run Around

The Instep Run Around rates high on Allegro Medical top list for jogging stroller and is a winner of the iParenting Media Award. At a reasonable price of $116, the jogging stroller can accommodate your child up to 50 lbs. and offers a five-point safety harness, adjustable handle and a one-hand folding design.

Bumbleride Indie

The Bumbleride Indie rates on the top of Babble.com top jogging strollers and is nicknamed the "urban jogger." The Indie offers you a front swivel wheel, adjustable handle, aluminum frame and a quick release five-point safety harness. You will find the jogging stroller will accommodate your child up to 45 lbs. and averages $400.

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Differential growth rates through the seedling and sapling stages of two Nothofagus species underplanted at low-light environments in an Andean high-graded forestdetails log on website

Author

• Pablo J. Donoso, 
• Daniel P. Soto, 
• Claudio Fuentes

Abstract

The Andes of south-central Chile, with the valuable and dominant timber species Nothofagus dombeyi and N. alpina, have been heavily high-graded. Restoring these forests with these species is challenging since they are light demanding and the understory becomes dominated by bamboo (Chusquea culeou), which prevents natural regeneration. In this study we aimed to evaluate the root-collar diameter (RCD) and height growth response of these species during their seedling and sapling stages (years 1, 2, 3 and 6) after outplanting and removing the understory in a relatively low-light environment in a high-graded forest (incident light levels between 2.6 and 12.7 mol m−2 d−1). We fitted regression models to predict annual growth as a function of light availability, and used species as an indicator variable. RCD growth was significantly affected by light since the onset, and height growth only at ages 3 and 6. Species became a significant variable only at years 3 and 6 for RCD growth, and at age 6 for height growth. N. dombeyi grew faster than N. alpina throughout all the period, and both species had their greatest response to light the last years evaluated (adj. r 2 of 0.67 and 0.45 in RCD and height growth, respectively), i.e. an increasing intolerance to shade (ontogenetic change). The increasing variability of the data and the goodness-of-fit of the models at this stage suggest that light is becoming a major driver of growth. These results illustrate a good potential use of these species for restoration of Andean forests.

Introduction

The Andean Nothofagus-dominated forests of the southern cone of America are among the most productive and diverse among these coastal temperate forests (Donoso et al. 1999; Donoso and Lusk 2007). Within this area, 40–50 m tall Nothofagus dombeyi ((Mirb.) Blume) and Nothofagus alpina ((Phil.) Dimitri and Milano) trees used to dominate old-growth Andean forests of Chile (the western side of the Andes) from 37 to 41°S and in general between 500 and 1000 m a.s.l. (Donoso et al. 1986). The great biomass that these forests have when mature (Donoso et al. 1986; Donoso and Lusk 2007), and the high quality timber value of these Nothofagus species, especially from N. alpina trees, led to a severe overexploitation and high-grading of these forests during the twentieth century. These degraded stands have characteristics like those described by Nyland (2006): few large trees of desirable species, good vigor or good form remain, and there is a patchy distribution of residual trees, limited usable volume, and thick understories. In this case, the understories are dominated by the aggressive bamboo Chusquea culeou (Muñoz et al. 2012). Compared to the original forests, these forests are now degraded since they have lost their productive capacity and the habitat has deteriorated (sensu Perry et al. 2008).

It is strategically important to find alternatives to regenerate these high-graded forests with Nothofagus due to the economic, social and environmental negative effects of this situation in a large area of south-central Chile. Underplanting of these species could be a plausible option, in spite of the fact that N. dombeyi is considered a shade-intolerant species and N. alpina mid-tolerant to shade (Weinberger and Ramírez 2001). Two recent papers (Donoso et al. 2013; Soto et al. 2014) illustrate that these species had little mortality rates after two growing seasons since planted under remnant tree covers that allow <50 % light availability in the understory, but where the understory competing vegetation was controlled. Similarly, Pollmann and Veblen (2004) have reported that when forests lack a thick understory (especially of Chusquea spp.), N. dombeyi is able to regenerate even under the closed canopy of old-growth Andean forests. These findings illustrate that the control of understory competition reduces stresses to these species growing under low-light levels beneath the tree canopy, dramatically altering their capacities to tolerate low light environments (sensu Valladares and Niinemets 2008).

Although both N. dombeyi and N. alpina had a nearly complete survival 2 years after planted in a high-graded forest, they showed differences in growth, with N. dombeyi having greater rates of growth than N. alpina in all light conditions, e.g. small (<200 m2), medium (200–450 m2) and large (450–735 m2) canopy gaps (Donoso et al. 2013). In addition both species showed increasing growth with greater light levels, but this was only significant for N. dombeyi. In all these studies conducted in open conditions N. dombeyi has shown similar or faster growth rates than N. alpina (Donoso et al. 2011, and references therein). The faster growth rates of light-demanding species (N. dombeyi more intolerant than N. alpina) is common in all light environments, but this advantage may be short lived (Lusk et al. 2011). With ontogeny plants reduce their photosynthetic capacity, and increase their relative respiratory requirements and their self-shading, which overall increase the minimum light requirements for survival and growth (Valladares and Niinemets 2008; Lusk et al. 2011). Differences in minimum light requirements for survival of co-occurring species, such as these two Nothofagus, are central to understand ecosystem dynamics (Baltzer and Thomas 2007; Valladares and Niinemets 2008). Therefore, it is relevant to study the growth performance of these two species once they have moved from the seedling (average <2 m after two growing seasons, as reported in Donoso et al. 2013) to the sapling stage (>2 and <5 m in height, as reported in the present study).

Plantations with N. species in degraded forest with understory competition control seems an interesting option to test in order to rehabilitate or promote a rapid recovery of these degraded forests. In addition, this looks like a potential alternative for these species that suffered severe mortality due to freezing temperatures as they were planted in the open at elevations >600 m a.s.l. in in the Andes mountains (Soto et al. 2009). Finding suitable environmental conditions for plantations of Nothofagus in degraded forests could become a great opportunity for the development of the forest sector in this region in Chile. In addition to matching site and species for these plantations, social acceptance is another relevant issue to promote restoration (Aronson et al. 2006).

In this study we evaluate the performance of underplanted N. dombeyi and N. alpina during the seedling and sapling stages (6 year period) in a high-graded forest where the understory was manually removed. The objective of this study was to measure the magnitude of the response to light of underplanted N. dombeyi and N. alpina along the seedling and sapling (juvenile) stages in Andean high-graded forests. This paper contributes to the understanding of tree plant performance of species considered mostly intolerant to shade when planted in free-of-competition understory conditions.

Materials and Methods

Study site

The study site is located in a 10-ha high-graded south-facing stand in the San Pablo de Tregua Experimental Forest of the Universidad Austral de Chile, between 600 and 650 m a.s.l. in the Andes of Chile (39°35′S, 72°05′W). The location is within the N. dombeyi-N. alpina-Laureliopsis philippiana forest type (Veblen et al. 1980; Donoso et al. 1986). The area is dominated by hardwood trees of L. philippiana and conifer trees of Saxegothaea conspicua of about 25 m in height and 50–100 cm in diameter at breast height with some N. alpina trees of similar height but smaller diameters that correspond to former young trees when the selective cut was done about 50 years ago. This forest stand has about one-third of the expected basal area of this forest type in a mature or old-growth condition. Details on the residual overstory conditions and diameter size distribution can be found in Donoso et al. (2013).

According to Köppen, climate in this region is coastal oceanic with a Mediterranean influence, having short and dry summers and humid winters. The annual precipitation, mostly rainfall, ranges between 3000 and 5000 mm, with <1 month of snowfall a year (Oyarzún et al. 2011). The mean annual temperature is 11 °C, with a mean temperature of 5 °C for the coldest month (August) and 16 °C for the warmest one (February). San Pablo has been reported to have around 30–50 annual frosts, concentrated from August through September (Soto et al. 2009).

The soils in the area, derived from modern volcanic ashes (Acrudoxic hapludand), have a subangular blocky structure in the A and A-B horizons and a massive structure in the B horizon, and medium texture through the entire profile, with a pumice horizon over basaltic-andesitic rocks. This soil has a high water retention capacity (>250 mm in 1 m depth) and total N content (0.97 ± SD 0.07 %), and a C/N relation of 11.6 ± SD 0.3 (Donoso and Lusk 2007). However, it also has a high P retention and Al levels due to the presence of alophan (for further details see Schlatter et al. 1995).

Seedling material

The seeds of N. dombeyi and N. alpina used to obtain the seedlings were collected from local seed-trees (approximately 10) in the same experimental site of San Pablo de Tregua. Seedlings were grown in containers 130 cm³ in volume and 15 cm tall with composted Pinus radiata bark mixed with a slow-release fertilizer as substrate. The containers were placed in a greenhouse from the first week of September until the end of November, and then moved outdoors for hardening during the last month of the growing season (February–March). The morphology of seedlings selected for the study showed no significant differences in productivity variables (root-collar diameter, tree height and stem volume). Seedlings had a range from 35–45 cm in total height and 3–4 mm in root-collar diameter in order to minimize the influence of initial size on seedling growth. The protocol for seedling production is given in Bustos et al. (2008).

Experimental design

Seedlings were planted by the end of October of 2007 in 22 different gaps (sensu Brokaw 1982) that ranged in size from 40 to 734 m² (11 gaps for each species), and the plantation was evaluated during first three growing seasons and then at year six. Before planting, the understory (i.e. mostly C. culeou) in gaps was manually extracted within 1-m wide strips. For the experiment we planted 15 seedlings in a setting of 3 rows (spaced at 2 m) with 5 seedlings per row (spaced at 3 m) in gaps, with no seedlings planted below tree crowns at the edges of the gaps (see planting scheme in Donoso et al. 2013).

For each gap we quantified light availability through hemispherical photographs, using a Coolpix 4500 digital camera (Nikon CO., Japan) with a FCE-8 fisheye lens that has a 182° field of view (Nikon CO., Japan). Three photographs per gap were taken at the apex of the selected seedlings located at the center, north and south position of each gap. Photographs were taken under homogeneous diffuse sky light during late summer. The resulting photographs were analyzed for light transmission indices (daily total, direct and diffuse transmitted radiation through the canopy; mol m⁻² d⁻¹) with the Gap Light Analyzer 2.0 software (Frazer et al. 1999). Due to the similarity in the magnitude and type of response of seedlings to these three types of light transmission indices, we report in this study only the results obtained for total light estimations. There was <10 % mortality during the first growing season, and no mortality thereafter. Growth in root-collar diameter (RCD) and total height were measured after years 1, 2, 3 and 6, so that annual growth corresponds to current annual increment (CAI) or growth for the first three growing seasons and to periodic annual increment (PAI) or growth for the period between years four and six. Since mortality occurred only during the first growing season (Donoso et al. 2013) subsequent measurements were made on the same number of plants (25 in N. dombeyi and 32 in N. alpina).

Statistical analysis

Due to the light-limited environment in the study site (total light transmitted 2.6–12.7 mol m⁻² d⁻¹), we followed the suggestion by Holste et al. (2011) in terms of using linear models instead of non-linear models for modeling growth as a function of light when studying growth under higher light conditions. Specifically we used a linear regression model to determine the effects of light on seedling growth and included the indicator variables as covariates in order to adjust for possible differences between the species under study. The statistical model is of the form

μ{growth|light,species}=β0+β1Light+β2species+β3Light×Species$$\mu growthlightspecies{\beta }_0{\beta }_{1}Light{\beta }_{2}species{\beta }_{3}LightSpecies$$

(1)

where μ{growth|light,species}$\mu growthlightspecies$ denotes the mean response of growth (in RCD and plant height, both in cm) in terms of the quantitative variable Light (mol m⁻² d⁻¹) and Species as an indicator variable to distinguish between N. dombeyi and N. alpina.

In the context of the model, a statistically significant value for the coefficient β ₁will indicate association between the amount of light and growth and the coefficients β ₂ and β ₃ will indicate a difference in this association between the species. Given the nature of the study and the variability of the data, the fitted models do not have high predicting power, but they are sufficient to unveil structural trends and association between the variables of interest, which is the main focus of the study. A natural extension of the model is to consider a mixed effects model adding a random effect to account for correlations between observations from the same gap. However this type of model did not show any substantial differences or improvement over the ones we discussed earlier. Furthermore, the interpretation of the regression coefficients in the models we considered is straightforward and within the scope of the analysis we are interested for this study. Standard residuals analysis was used to validate the models here presented.

In addition to growth modeling, the ratio between plant height and RCD was used to calculate the slenderness index, where lower values tend to reflect plants with better biomass distribution and greater possibilities of better field performance (Bustos et al. 2008). Slenderness was graphed for each species against light for each year and evaluated with Pearsons coefficient of correlation.

Results

Throughout all the years evaluated, N. dombeyi had greater growth rates than N. alpina, and after six growing seasons the former had a median RCD close to 45 versus 22 mm in the latter, and in height 460 versus 290 cm (Fig. 1).

Fig. 1

Boxplot of Root-collar diameter (RCD) and Height for N. dombeyi and N. alpina plants evaluated for annual growth between years 1 and 3, and for periodic growth between years 4 and 6 (represented as 6 in the box plots)

Models of growth in RCD as a function of light were significant during all years evaluated, with increasing significance with time (e.g. Adj. r 2 0.242 at age 1 and 0.520 at age 6; Table 1). The indicator variable species for RCD was not significant in the model in years 1 and 2, and was increasingly significant in years 3 and 6 (P < 0.05). The models that included light and species as explanatory variables at age 3 and 6 explained an additional 5 and 15 % of RCD growth compared to the model with only light for these years, respectively (Table 1). Figure 2 shows these relationships. The model for the first year had a lower slope compared to the other years that had similar slopes (e.g. compared with year 2). On year 3 growth of both species is similar with the lower light levels, but the individual simple models diverge with increasing light, with N. dombeyi having greater growth rates. For year 6 the magnitude of response of growth to increasing light is similar for both species (parallel slopes), with N. dombeyi having a greater intercept (growth). For years 3 and 6 the individual growth models for N. dombeyi had a higher Adj. r 2 than those for N. alpina, reflecting the greater dependence on light of the former species.

Table 1

Individual growth [in root collar diameter (RCD) and height (h)] parameter probabilities, model adjusted coefficient of determination (Adj. r 2), standard error of estimation (see), and model probability for planted N. dombeyi and N. alpina for years 1, 2, 3 and 6 (PAI for years 4–6)

Year	Variable	Model	β^0$\stackrel{}{\beta }0$	β^1$\stackrel{}{\beta }1$	β^2$\stackrel{}{\beta }2$	β^3$\stackrel{}{\beta }3$	Adj. r 2	See	P value
1	RCD	1	ns	***	–	–	0.242	0.119	***
1	RCD	2	ns	***	ns	–	0.230	0.201	***
1	RCD	3	ns	*	ns	ns	0.224	0.202	***
2	RCD	1	ns	***	–	–	0.530	0.227	***
2	RCD	2	ns	***	ns	–	0.539	0.225	***
2	RCD	3	ns	**	ns	ns	0.539	0.225	***
3	RCD	1	ns	***	–	–	0.480	0.224	***
3	RCD	2	*	***	*	–	0.526	0.214	***
3	RCD	3	ns	***	ns	ns	0.543	0.210	***
6	RCD	1	ns	***	–	–	0.520	0.229	***
6	RCD	2	***	***	***	–	0.665	0.191	***
6	RCD	3	ns	**	ns	ns	0.659	0.193	***
1	h	1	***	ns	–	–	0.002	23.792	ns
1	h	2	ns	ns	ns	–	0.004	23.758	ns
1	h	3	ns	ns	ns	ns	0.042	19.806	ns
2	h	1	*	ns	–	–	0.054	22.788	ns
2	h	2	**	ns	ns	–	0.048	22.856	ns
2	h	3	ns	ns	ns	ns	0.036	23.011	ns
3	h	1	**	***	–	–	0.272	21.826	***
3	h	2	**	***	ns	–	0.292	21.524	***
3	h	3	ns	*	ns	ns	0.283	21.668	***
6	h	1	**	***	–	–	0.351	17.797	***
6	h	2	***	**	**	–	0.240	19.250	***
6	h	3	***	*	**	**	0.445	16.467	***

Best model for each year and dependent variable shown in bold

ns not significant

* P < 0.05; ** P < 0.01; *** P < 0.001

Fig. 2

Root-collar diameter (RCD) and height growth of N. dombeyi and N. alpina underplanted in a high-graded forest in the Andes of south-central Chile. The lines represent the better model fitted for this study, e.g. for the RCD at year 1, light (β^1)$\stackrel{}{\beta }1$ was the most important factor in the growth prediction (one line without distinction between species), where the model with only light explains more variance than more complex models that include species and interaction terms (β^2,β^3)$\stackrel{}{\beta }2\stackrel{}{\beta }3$. When indicator variable species (β^2)$\stackrel{}{\beta }2$ became significant, and increased the model fitness (Adj. r2), two lines represent each species, e.g. for RCD in years 3 and 6, and for height in year 6, the latter also including a significant interaction term (see Table 1 for further details)

Models of height were not significant during years 1 and 2, for year 3 were significant with only light as an explanatory variable, and for year 6 were significant with both variables light and species being significant in these models. However, the Adj r 2 of these height models was lower than values obtained for RCD growth (Table 1). At age 6, when there was an interaction of light and species, growth in height of N. alpina was more dependent on light than N. dombeyi (Fig. 2), with comparatively lower growth rates with the lowest levels of light evaluated and higher growth rates under conditions of more light (i.e. a significant interaction). However, at age 6 the individual model of growth dependent on light for N. alpina had a much greater Adj. r 2 than that for N. dombeyi, with the latter having a marginally significant model of growth dependent on light.

The slenderness index, or ratio between plant height and root-collar diameter, was substantially different in both species (Fig. 3). For N. dombeyi, it decreased significantly with light during all the period evaluated, especially since the second growing season, suggesting that more light availability in this conditions enabled plants to have a more balanced height to diameter ratio (closer to a 1 cm:1 mm ratio). On the contrary, in N. alpina, except for year 3, the slenderness index was not significantly affected with light.

Fig. 3

Effects of light availability on slenderness of N. dombeyi and N. alpina in a high-graded forest in the Andes of south-central Chile after six growing seasons in the field. Lines correspond to significant correlations (P < 0.05)

Discussion

Growth of underplanted N. dombeyi and N. alpina during the seedling and sapling stages

It is well documented that ontogeny can affect plant growth behavior and their physiological and mechanical adaptations, and that light requirements tend to increase faster with increasing age and size in less shade-tolerant species (Valladares and Niinemets 2008; Lusk et al. 2011). In this study we studied growth in diameter and height of two major and highly-valuable Nothofagus tree species of the Chilean Andes, N. dombeyi, one of the most shade-intolerant tree species in these forests, and N. alpina, a species that can behave as pioneer in secondary succession just as N. dombeyi, but that is considered to be of greater shade tolerance (Weinberger and Ramírez 2001; Donoso et al. 2011, 2013). Results from this study show that the initial trends of faster growth of N. dombeyi than N. alpina reported for the two first growing seasons in two different studies with underplanted seedlings in low-light environments in the Andes of Chile (Donoso et al. 2013; Soto et al. 2014) in general continue through the sapling or juvenile stage (Figs. 1, 2). However, some new patterns start to appear.

While during early seedling stages (years 1 and 2) species was not a significant variable in the models of RCD growth, by the time they reached sapling sizes (ages 3 and 6) the differences between species became evident, with RCD growth of N. dombeyi being more dependent on light and greater than N. alpina, and the opposite for height growth. While this pattern for RCD growth seems quite straightforward, for height growth the interaction that occurred at age 6, with growth of N. alpina being more dependent on light than that of N. dombeyi and with greater growth rates with more light, was unexpected. A possible explanation is that light conditions were becoming increasingly restrictive for N. dombeyi. In other words, at age 6 growth in RCD for N. dombeyi continues to be highly dependent on light, but growth in height for this species is weakly dependent on light, while growth in N. alpina is increasingly dependent on light, for both variables RCD and height. This pattern at the sapling stage for these species is different to what has been reported for early seedling stages in underplanting of these species in the Andes of Chile (Donoso et al. 2013; Soto et al. 2014). It suggests that the initial behavior of the more shade-intolerant species (N. dombeyi), with faster growth rates compared with the more shade-tolerant species (N. alpina), is starting to change once seedlings have entered into a sapling or juvenile stage, where N. dombeyi is starting to have decreasing differences in growth as compared to N. alpina. The evergreen character and the more shade-intolerant character of N. dombeyi could partly explain its faster growth rates than N. alpina in low-light conditions especially at earlier stages of development (sensu Valladares and Niinemets 2008).

The increasing variability of the data and the goodness-of-fit of the growth models at this stage suggest that light was becoming a major driver of growth, and that in the near future differences in shade tolerance between both species could become less evident. The increasing dependence of RCD growth on light in the case of both species supports the idea that light requirements increase faster with increasing plant age and size in more shade-intolerant species (Valladares and Niinemets 2008), and reflects the increasing shade-intolerant trait of N. alpina with ontogeny (sensu Boyden et al. 2009). Some studies have shown a similar pattern in another N. species, i.e. N. nitida, which has a similar physiognomy, but a smaller maximum size than N. dombeyi (smaller height and diameter), for which it has been postulated that it has a high ‘‘light acclimation’’ at the leaf level that allows it to tolerate more shade in early years (e.g. seedling stages) than in later successional stages like sapling stages (Coopman et al. 2008). A new evaluation in the coming years should reflect if the changes in growth patterns that are starting to appear by age 6 will become more evident, and also if some mortality will start to occur especially for plants growing in the lowest light conditions and requiring more light to photosynthesize while they get larger (sensu Valladares and Niinemets 2008).

Overall, this study conducted until age 6 supports previous approaches that consider N. dombeyi more intolerant to shade than N. alpina, i.e. this support is provided not only for seedling stages as reported in most studies on shade tolerance (Lusk 2004), but also for the sapling or juvenile stages. As reflected by the slenderness index, especially N. dombeyi has had morphological adaptations to cope with growth (and likely survival) under the lowest light conditions (Fig. 3). Similarly, in an understory environment with 26 % of full sunlight in southwestern Washington, saplings of Pseudotsuga menziessi had morphological adaptations (e.g. number of interwhorlbuds; Devine and Harrington 2009) due to overstory competition, reflecting the growth plasticity of some species to these low-light environments (Valladares and Niinemets 2008). This study also supports previous findings on the significant competitive effects of overstory trees on sapling growth as mediated by the shading effect in temperate forests, indicating that competition for light clearly exists within this forest (Mori and Takeda 2003).

The potential for rehabilitation of Andean high-graded forests with Nothofagus species

Different layers in the vertical profile of forests can affect the performance of tree seedlings (Lhotka and Loewenstein 2013). Lhotka and Lowenstein (2013) report that 7-year-old hardwood plantations established in a hardwood forest had a significant positive response to midstory vegetation control, rather than to understory vegetation. In the current study with understory competition controlled, there were high survival and growth rates for the two N.species evaluated. The increasing effect of light upon the performance of N. dombeyi and N. alpina with time, as reported here, suggests that without a timely control of light through an opening of the overstory or the midstory, the good initial performance of both species could be depressed. Overall, silvicultural manipulations will be likely needed in these understory plantations if we plan to succeed with plantations of these valuable species as a rehabilitation option in high-degraded forests.

Growth rates of both species were lower than those reported for them in open-field plantations at lower elevations (Donoso et al. 2011, and references therein), but survival of underplanted seedlings in degraded forests was higher compared to the severe mortality (and therefore no growth) of these species when planted in open fields at this elevation (Soto et al. 2009) or, in the case of N. alpina, even in north aspect at lower elevations (Donoso et al. 2011). The results of this study provide great expectations to conduct socially acceptable solutions to the rehabilitation of degraded Andean forests, a key issue to success in these types of efforts (sensu Aronson et al. 2006). These are preliminary results and therefore much more is required to investigate and monitor, including a more ample light environment, additional types of understory control, seedling stock, and plantation design to emulate more natural conditions (sensu Oliet and Jacobs 2012).

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