Acorn Production Patterns

Abstract

Acorns—the fruits of oaks—are a key resource for wildlife in temperate forests throughout the Northern Hemisphere. Acorns are also economically important for extensive livestock rearing, and as a staple food have supported indigenous human populations. Consequently, differences in how individual trees and populations of oaks invest in acorn production, both in terms of the size of the acorn crop and of the size of individual acorns, are of interest both ecologically and economically. Acorn production by oaks in both California and Spain tends to be highly variable and spatially synchronous. We summarize studies conducted in the two regions that investigate the factors influencing acorn production. One hypothesis explored is that, as a consequence of management, acorn production tends to be affected by different environmental factors in the two regions; another hypothesis is that acorn production in oaks in Spanish dehesas produce larger and more predictable acorn crops than trees in less managed Spanish forests or in California woodlands. Other factors potentially influencing acorn production are summarized, including biotic factors, trade-offs with growth, trade-offs with acorn size, and pollen limitation. We conclude with a discussion of spatial synchrony and acorn production at the community level. There remain many questions concerning the mating systems of oaks, trade-offs between different oak life-history characters, and the patterns and drivers of spatial synchrony. Environmental conditions in the two regions are similar, but understanding how their subtle differences influence acorn production is likely to yield important insights about the proximate and ultimate factors affecting acorn production and masting behavior.

Box 1. Methods for Estimating Acorn Production

Despite decades of attention from wildlife managers and forest researchers, there is still no consensus as to the best way to quantify acorn production. As result, researchers use many different techniques, not all of which yield data that are readily comparable. Here we provide a brief review of these methods, dividing them into “direct” and “indirect” methods.

1.1 B.1. Direct Methods

Direct methods involve sampling in the crown or harvesting from the ground. They are more accurate for calculating real (or absolute) acorn production than indirect methods, although they suffer from the disadvantage of potentially ignoring acorns removed by birds or other wildlife prior to maturing. These methods include the following.

• 1. Knocking down the acorns and collecting them under the crown—This traditional method is also used for harvesting olives and some other fruits. Its primary disadvantages are that it is labor intensive, time consuming, and, in the case of large trees or of dense tree stands where individual canopies grow entangled, logistically difficult. It also potentially underestimates the crop by missing immature acorns that are not yet ready to fall. This is generally not a viable option if assessing many trees is desired, which is often the case due to large within-population variation and among population differences.

• 2. Containers or traps method—This method consists of placing containers or traps under the crown of the trees where acorns are removed on a regular basis (Fig. 7.B1). Many different kinds of containers have been used, varying in shape and construction. Containers may be on or attached to the ground, or hung from branches with ropes or wire to avoid consumption of acorns by large herbivores (wild ungulates or livestock). Typically, several containers are placed either regularly at different orientations or under the crown in a randomized design. Total acorn production per tree is obtained by adding, at the end of the dissemination period, the fruits periodically counted or weighed and then multiplying by the estimated fraction of the crown cover sampled by the traps.

Livestock and wild ungulates can be a problem for using containers since cattle and deer can easily knock over most traps. When livestock are present it is therefore a good idea to plan on protecting traps with fencing or use a design such as hanging containers in the tree that will minimize their impact.

The container method is also labor-intensive requiring considerable setup and repeated maintenance. Only a small proportion of the canopy is sampled, and only acorns that fall into the containers are counted or weighed, so arboreal acorn removal by animals is not considered—something that can be a serious problem in certain years (Koenig et al. 1994a). If the goal, however, is to determine the acorn crop available for livestock, ground predators such as deer, or ground dispersers such as mice, this method should be seriously considered.

Acorn production measured with containers was quite consistent with the total acorn yield (measured by knocking down all acorns in the tree) divided by the crown surface (R ² = 0.82, F _{1, 39} = 184, P < 0.001; Alejano et al. 2008).

• 3. Visual surveys—This method, which may involve a timed or complete survey of acorns on individual trees, is a nondestructive method allowing the subsequent harvesting of fruits. Other advantages include:

  • (a) Counts are made just once during the dissemination period, so it is quicker and far less labor intensive than other direct methods.

  • (b) Depending on the species and area, it can be performed one to two months before acorns mature, and thus to some extent allows crop prediction. It is important not to delay counting until after acorns start falling, since the method will then underestimate the crop unless caps remain on the tree and can be included in the survey.

  • (c) Assuming the timing is right, counts will include most acorns that might later be removed from the crown by seed predators prior to acorn fall, and thus it potentially provides a more accurate measure of overall productivity than methods that quantify acorns that fall, such as the container method.

Visual surveys have been found to be consistent with the acorns harvested by using the container method (Koenig et al. 1994a), and has been widely used both in California and in Spain.

There are, however, disadvantages: counts are likely to be affected by factors influencing the ease with which acorns are seen such as light conditions, canopy cover, leaf density, and acorn coloration. The main disadvantage of timed visual surveys that do not completely sample the acorn crop, however, is that it only provides a measure of the relative, rather than the absolute, crop size. Counts are typically performed in an unknown area of the crown, so transforming this number into total number of acorns per tree or even total weight of acorns per tree is not an easy task.

Despite this caveat, however, tests of this method have generally been favorable. Perry and Thill (1999) tested five visual surveys methods and found the Koenig et al. (1994a) method to be the most efficient. Carevic et al. (2009; Fig. 7.B2) compared visual surveys and containers and obtained a regression that would be the starting point for estimating acorn weight from acorns counted for a particular species and geographical area. Residuals tended to deviate from expectations when many acorns were counted, and, to a lesser extent, when few acorns were seen. Counting for a longer period when acorns are rare or hard to see might improve the relationship between visual surveys and the “real” acorn crop when acorns are sparse; it is less clear how to distinguish between acorn crops at the upper end of the spectrum. To the extent that the acorn crop is good and such separation is desirable, an alternative method is probably needed.

Fig. 7.B1

Containers for estimating acorn production under a flowering holm oak (Q. ilex) in Huelva, Spain. Note the dendrometers on the oaks for measuring radial growth. (Photograph by R. Alejano)

Fig. 7.B2

Regression of acorn production estimated from visual surveys (APVS) on acorn production estimated from container traps (APC, measured in g m⁻² of crown area) for dehesas of holm oak in Huelva, Spain (from Carevic et al. 2009)

The visual survey method proposed by Espárrago et al. (1992) and later modified by Vázquez (1998) has been used in Spanish dehesas as well. For its application, acorns within a 20 cm² wooden frame placed in front of different areas of the crown are counted. The average of at least 50 such counts per tree are done and used as an index of tree production. Several models have been proposed to translate the resulting acorn number into the total acorn crop assuming the crown to be a cylinder. Fernandez et al. (2008) checked the consistency of the method obtaining good results. A training period was desirable, however, since the experience of observers was found to influence the results.

• 4. Ranking methods—Several methods have been used to evaluate acorn crops in dense oak forests in the USA. Sharp and Chisman (1961), studying white oak (Q. alba), proposed a qualitative method consisting of classifying a tree as a poor, good or extraordinary producer. Acorns in the end of the branches in the upper third of the crown were counted and averaged to yield acorn production per tree or per stand. A second method was proposed by Whitehead (1969) involving three qualitative parameters: the percentage of the crown containing seeds (0–3), the percentage of shoots within the crown producing seeds (also 0–3), and the average number of acorns per shoot (0–4). The Whitehead index is then obtained by adding the three values (thus 0–10), and was found by Perry and Thill (1999) to be highly correlated with the total number of acorns m⁻² of crown area.

In Spain, Pulido and Díaz have developed a ranking method for long-term monitoring of acorn and pollen production of holm oak populations (see www.globimed.net/investigacion/Veceria01.htm, Díaz et al. 2011). Production is ranked into five categories: 0: no acorns or catkins; 1: <10% of the canopy covered by acorns/catkins; 2: 10–50%; 3: 50–90% and 4: >90%. Catkins are estimated in spring, when most trees are in full bloom, and acorns are estimated in early fall, after aborted seeds and those infested by insects have fallen. Several tests have demonstrated strong among-observers consistency in rank estimates after a short training period. Data taken in 2007–2010 from 145 trees provided with seed traps in Cabañeros National Park showed a strong correlation between this index and measures of the production of acorns in terms of the number of sound seeds m⁻² (r = 0.55, P = < 0.001, N = 374; Díaz et al. 2011). This method enables rapid estimates of the among-years and among-individuals variation in the production of acorns and catkins, and also of the production of new shoots and leaves in spring and of the proportion of the canopy with leaves dry or lost for large number of trees, either isolated or growing in dense stands.

1.2 B.2. Indirect Methods

Several indirect methods have been described or mentioned for estimating acorn crops. We mention them here for completeness.

• 1. Pollen—A positive correlation has been reported between the amount of airborne Mediterranean oak pollen released to the atmosphere and the size of the acorn harvest (García-Mozo et al. 2007). This finding supports the hypothesis that pollen may be limiting, at least under some conditions, and have an important effect on subsequent acorn production in these wind-pollinated species, similar to its effects in many anemophilous species (Galán et al. 2004). To the extent this is true, integration of aerobiological, phenological and meteorological data could represent an important step forward in forest fruit production research (García-Mozo et al. 2007).

• 2. Remote sensing—Several recent studies have employed remote sensing techniques, including hyperspectral imaging, to estimate acorn yields (Yao et al. 2008; Panda et al. 2010; Yao and Sakai 2010). Such methods can at least in theory allow the mapping of acorn production over large geographic areas so as to yield within-stand abundance and spatial synchrony of acorn production. Remote sensing methods have yet to be applied to studies in either California or Spain, although they may eventually offer a powerful and less labor-intensive tool for assessing acorn production in our Mediterranean oak forests.

• 3. Dendrochronology—Based on the assumption of a tradeoff between growth and reproduction, Speer (2001) proposed a technique for mast reconstruction using dendrochronology for non-Mediterranean oaks. Although his results provided some optimism for this approach, it has not been used or tested by later authors. One problem is that in some cases it is likely that a negative correlation between growth and acorn production may be due to correlated effects of environmental variables rather than a trade-off per se (Knops et al. 2007). Nonetheless, the strong negative correlation between growth and reproduction observed in many species (Drobyshev et al. 2010) means that growth can potentially provide information useful for predicting subsequent acorn production in some species, regardless of the mechanism involved.

• 4. Fattening of pigs (for Spanish dehesas)—A traditional way to estimate acorn crops in Spanish dehesas is based on the degree to which pigs fatten during the dissemination period when they feed almost exclusively on acorns. Historical records with yearly controls would be required for this method to be practical.