Carlos Manuel Guilherme Lage Teixeira

Bird diversity

State of the art

Taking these elements into consideration, the need to develop frameworks for understanding and predicting the effect of habitat alteration on biodiversity or assessing the relative vulnerability of species to land-use change, became clear and urgent.

During the last decade and up to the moment, several theoretical frameworks and models have been developed, aiming to predict extinction probabilities and rates after land-use change. These studies focus mainly on habitat alteration whether it corresponds to conversion of native habitat to human-dominated uses, changes in agricultural practices or abandonment. They also usually include demographic models such as species–area relationship (SAR) or population viability analysis (PVA) models. Whereas the former do not discriminate among species, the latter can be applied to species individually but typically demand a large amount of life-history data.

Models used to estimate the maximum population growth rate, such as that developed by Cole, require values of fecundity and mortality rates. Other necessary elements include the age at first breeding and survivorship (proportion of individuals surviving to the beginning of each age class). Fecundity rates require such elements as the number of broods per female, per year (which may require the breeding season length and the length of brood time) and an estimate of female eggs per clutch. The best estimate for mortality rates may result from maximum life span information.

Considering even the most well known animal groups, such as the birds, there is a considerable amount of information lacking for most of the species. This may include a variety of necessary elements, from age at first breeding to maximum life span.

Ideally the necessary data to fill these information gaps would continue to be collected on the field. Considering present less ideal conditions, reliable data estimates are required, in order to fill the gaps and model the effects of land use change, in an optimized amount of time.

It has become quite common to calculate allometric relationships based on the available data for two life-history traits of a group of well known species, in order to estimate equivalent data for another group of less documented species. However, allometric scaling relationships, of an exclusively mathematical nature, assume similarities between species based mostly on body mass. They are highly sensitive to the inclusion or exclusion of particular groups of organisms. One example are bats (Chiroptera), that exhibit some life-history traits typically inconsistent with their small body size (long life spans, small litter sizes, and relatively long litter intervals).

Objectives

1. To apply the Dynamic Energy Budget (DEB) Theory in order to provide the estimates lacking for an extensive number of bird species of the western Palearctic ecozone
2. To perform sensitivity analyses with the estimates previously calculated and considering different groups of bird species using taxonomy, dimensions, behavior and ecological strategies as criteria
3. To perform an assessment of the land-use change vulnerability of a group of bird species that coexist in a selected region, constituting a study case.

Methodology

1. Collecting data
2. Estimating DEB primary parameters
3. Applying DEB primary, secondary or tertiary scaling relationships
4. Performing sensitivity analyses for different groups of bird species
5. Using estimated data for land-use change vulnerability assessment of bird species in a selected region (case study).

1) Collecting data

2) Estimating DEB primary parameters

1. Adult body length measurements
2. Adult weight
3. Egg length measurements and weight

Some life-history traits corresponding to primary DEB parameters:

Life-history traits	DEB quantities
Adult body length measurements	Observed ultimate length (L_inf_w)
Adult weight	Observed wet weight at ultimate length (L_inf_w)
Length of brood time (incubation)	Age at birth
Age at first breeding	Age at puberty
Nests per year/clutch size	Mean reproduction rate

When the necessary data is available some calculations may be performed. If we consider a specific density (d_V_w) typically close to 1 g cm^-3, we can relate the physical volume (V_w) to wet weight (W_w = d_V_w . V_w). Using the scaling relationships, physical volume and physical ultimate length, we can estimate the shape coefficient, which relates physical length to structural length.

Length at birth and length at puberty require measurements done at the specific ages. However, these are usually not available. A series of compound parameters also depend on these estimates.

The age at birth and age at puberty will possibly be estimated considering the respective maturity thresholds (E^b_H and E^p_H). The calculations necessary in order to estimate these thresholds may not be possible for all the species considered.

3) Applying DEB primary, secondary or tertiary scaling relationships

However, in order to estimate the zoom factor, values for maximum length are required (z = L_m_ref / L_m). Estimates for primary parameters such as the surface-area-specific maximum assimilation rate ({p_Am}) can be estimated applying the correct proportionality relationship with the zoom factor.

Making use of the same rationale as for the primary scaling relationships, the application of secondary scaling relationships can be used to estimate necessary life-history traits / parameters such as:

• a) Length at birth (L_b)
• b) Length at puberty (L_p)
• c) Age at birth (minimum embryonic period)
• d) Age at puberty (minimum juvenile period)
• e) Maximum life span

The following conditions are to be considered initially:

• a) Constant food density (scaled functional response f = 1)
• b) Scaled reserve density set at equilibrium (e = 1)

4) Performing sensitivity analyses for different groups of bird species

5) Using estimated data for land-use change vulnerability assessment of bird species in a selected region (case study)