RELATIONSHIPS BETWEEN AVIAN DIVERSITY AND AGRICULTURAL LANDSCAPE HETEROGENEITY

Aksan, Şengül

doi:10.1590/1806-908820230000005

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Scientific Article • Rev. Árvore 47 • 2023 • https://doi.org/10.1590/1806-908820230000005 copy

RELATIONSHIPS BETWEEN AVIAN DIVERSITY AND AGRICULTURAL LANDSCAPE HETEROGENEITY

RELAÇÕES ENTRE DIVERSIDADE AVIÁRIA E HETEROGENEIDADE DA PAISAGEM AGRÍCOLA

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Studies on birds and their habitats are usually conducted in natural areas (protected areas, forests, wetlands, etc.). In this study, the relationship between agricultural landscape diversity and the species diversity of birds was investigated in an agricultural zone surrounded by natural and forested areas. Observations were carried out in 60 sample grid squares. Presence/absence data for birds and cultivated plants at each sample site were recorded. The Shannon-Wiener diversity index for bird species and landscape metrics for agricultural areas were used in the sample site. A multiple linear regression analysis was performed to explain the correlation between agricultural landscape diversity and bird species diversity. According to the results, the area-weighted mean shape index (AWMSI) and the number of patches (NP) were found to be particularly effective at predicting bird species diversity (R2 = 0.66). In addition, as the patch number and patch shape ratio increased in a sample area, the diversity of bird species (R = 0.83) expanded. It can be concluded that agricultural zones consisting of small, different patches are rich areas for bird species diversity. Bird species diversity is lessened in agricultural areas with uniform or similar landscape structures consisting of large patches. If the NP in the area is high, but not distributed in a mosaic pattern, then the diversity of bird species is considered weak. Despite the increasing NP and patch types, bird species diversity declines if there is intense human activity in the area.

Keywords:
Agricultural area; Avian diversity; landscape metrics

Table 1 Patch types and codes recorded in sample sites.

Codes	Patch type	Codes	Patch type	Codes	Patch type	Codes	Patch type
1	%100 P. somniferum L.	21	%70 Malus sp. %30 Vacant	41	%100 Broad-leaved trees	61	%30 C. libani A. Rich %70 Weed
2	%50 Pyrus sp. %50 Vacant	22	%90 Malus sp. %10 Vacant	42	%90 Broad-leaved trees %10 Shrub	62	%40 C. libani A. Rich %60 Weed
3	%100 H. Vulgare L.	23	%100 Malus sp.	43	%100 Broad-leaved trees, coniferous	63	%50 C. libani A. Rich %50 Weed
4	%10 P. dulcis Mill .%90 Vacant	24	%60 Prunus sp. %40 Vacant	44	%10 Populus nigra L.%90 Weed	64	%10 P. persica(L.) Batsch %90 Vacant
5	%10 P. dulcis Mill. %90 Weedt	25	%15 Tree nursery %85 Vacant	45	%40 Populus nigra L. %60 Weed	65	%50 P. persica (L.) Batsch %50 Vacant
6	%90 P. dulcis Mill. %10 Vacant	26	%100 V. sativa L.	46	%50 Populus nigra L. %50 Weed	66	%60 P. persica (L.) Batsch%40 Vacant
7	%90 Vitis sp. %10 Weed	27	%80 R. damascena Mill. %20 Vacant	47	%60 Populus nigra L. %40 Weed	67	%90 P. persica (L.) Batsch %10 Vacant
8	%100 R. idaeus L.	28	%10 P. nigra Arnold %90 Weed	48	%70 Populus nigra L. %30 Weed	68	%80 C. sempervirens L.%20 Weed
9	%100 T. Aestivum L.	29	%40 P. nigra Weed Arnold%60	49	%80 Populus nigra L. %20 Weed	69	%90 C. sempervirens L. %10 Weed
10	%100 Pebble	30	%90 Coniferous %10 Vacant	50	%100 Populus nigra L.	70	%90 S. alba L. %10 Weed
11	%70 Juglans sp. %30 Vacant	31	%10 Mixed fruit trees %90 Vacant	51	%90 Q. coccifera L. %10 Weed	71	%100 S. alba L.
12	%80 Juglans sp. %20 Vacant	32	%10 Mixed fruit trees %90 Weed	52	%10 P. avium L.%90 Vacant	72	%100 Dam
13	%90 Juglans sp. %10 Vacant	33	%10 Mxed fruit trees %90 Vegetables	53	%30 P. avium L.%70 Vacant	73	%100 Stream
14	%100 Juglans sp.	34	%20 Mixed fruit trees %80 Vacant	54	%60 P. avium L.%40 Vacant	74	%100 Pool
15	%100 Weed	35	%25 Mixed fruit trees %75 Vacant	55	%80 P. avium L.%20 Vacant	75	%100 Water-trench
16	%100 Shrub	36	%30 Mixed fruit trees %70 Vacant	56	%90 P. avium L.%10 Vacant	76	%100 Ploughed field
17	%100 Natural rock, stone, weed	37	%40 Mixed fruit trees %60 Vacant	57	%100 P. avium L.	77	%80 P. cerasus L.%20 Vacant
18	%10 Malus sp. %90 Vacant	38	%80 Mixed fruit trees %20 Vacant	58	%100 Housing	78	%90 P. cerasus L.%10 Vacant
19	%30 Malus sp. %70 Vacant	39	%90 Mixed fruit trees %10 Vacant	59	%100 Gravel	79	%100 Road
20	%50 Malus sp. %50 Vacant	40	%100 Mixed fruit trees	60	%100 Laid fallow	80	%100 M. Sativa L.

Table 2 Landscape metrics (AWMSI, NP) and Shannon_H values in sample site.

Sample site no	Bird species	AWMSI	NP	Shannon H	Numberof patch types	Patch type codes
1	18	1,987	2	3,045	2	28, 72
2	22	1,625	10	3,178	7	30, 40, 53, 58, 68, 69, 79
3	20	1,421	35	3,091	16	6, 9, 12, 15, 24, 38, 39, 46, 52, 53, 54, 55, 57, 64, 76, 79
4	22	2,147	30	3,258	14	9, 10, 28, 29, 30, 55, 58, 60, 61, 62, 63, 69, 74, 79
5	13	1,468	15	2,708	8	3, 15, 18, 20, 34, 60, 76, 79
6	15	1,766	19	2,833	11	7, 15, 39, 50, 54, 55, 57, 60, 67, 76, 79
7	13	1,392	8	2,639	4	3, 9, 76, 79
8	23	1,675	21	3,258	15	4, 5, 8, 12, 28, 59, 60, 61, 62, 63, 70, 71, 72, 76, 79
9	19	1,521	5	2,996	3	44, 49, 79
10	18	1,860	23	3,135	13	15, 16, 20, 21, 22, 38, 42, 47, 49, 50, 71, 73, 79
11	21	1,435	31	3,258	17	3, 9, 15, 19, 20, 31, 36, 41, 52, 53, 54, 55, 56, 60, 65, 66, 67
12	6	1,246	6	1,792	5	9, 15, 26, 60, 76
13	16	1,326	20	2,833	7	3, 9, 15, 26, 60, 76, 79
14	15	1,602	35	2,944	14	3, 4, 9, 15, 27, 28, 31, 35, 36, 58, 60, 68, 77, 79
15	21	1,443	47	3,091	13	3, 4, 7, 9, 11, 15, 19, 27, 28, 31, 37, 58, 79
16	17	1,391	21	2,89	13	3, 9, 15, 20, 27, 39, 52, 53, 56, 60, 76, 79, 80
17	23	1,549	30	3,296	14	4, 6, 9, 15, 20, 21, 22, 41, 42, 47, 49, 50, 70, 79
18	15	1,515	51	2,773	22	4, 9, 11, 15, 17, 18, 19, 20, 21, 23, 27, 34, 37, 38, 40, 50, 53, 54, 56, 57, 60, 79,
19	19	1,282	7	3,091	7	15, 18, 19, 20, 21, 58, 79
20	18	1,162	4	2,996	3	15, 19, 60
21	12	1,760	10	2,639	6	18, 19, 20, 22, 24, 89
22	7	1,477	9	2,197	5	15, 28, 51, 73, 79
23	16	1,461	10	2,833	5	4, 15, 51, 54, 60
24	16	2,122	45	3,045	16	9, 10, 15, 16, 27, 30, 41, 42, 43, 44, 55, 58, 62, 63, 76, 79
25	24	1,425	39	3,434	21	33, 56, 35, 7, 15, 16, 20, 24, 27, 34, 36, 38, 41, 42, 54, 55, 56, 57, 76, 78, 79
26	19	1,681	18	3,135	12	4, 7, 15, 16, 27, 34, 41, 42, 46, 58, 75, 79
27	18	2,370	23	3,091	13	14, 15, 17, 30, 50, 51, 58, 59, 63, 71, 73, 74, 79
28	13	1,643	21	2,773	9	3, 9, 15, 20, 28, 58, 59, 60, 79
29	11	1,128	1	2,565	1	9
30	19	1,389	58	2,996	20	3, 4, 7, 9, 12, 15, 16, 22, 36, 38, 39, 49, 53, 54, 55, 56, 58, 60, 76, 79
31	9	1,420	10	2,197	5	9, 54, 55, 60, 79
32	12	1,360	9	2,708	4	3, 9, 22, 79
33	25	1,583	39	3,296	12	3, 4, 9, 10, 15, 17, 27, 28, 59, 63, 76, 79
34	13	1,473	9	2,708	8	4, 9, 26, 55, 60, 76, 79, 80
35	20	1,988	27	3,135	18	3, 4, 9, 10, 15, 16, 27, 28, 29, 30, 49, 51, 58, 63, 72, 73, 76, 79
36	20	1,618	21	3,135	13	7, 12, 13, 15, 31, 35, 40, 53, 55, 57, 60, 65, 79
37	10	1,370	8	2,485	5	9, 15, 22, 60, 79
38	15	1,407	9	2,773	5	5, 6, 9, 15, 79
39	13	1,331	22	2,773	15	1, 3, 4, 9, 15, 18, 19, 20, 21, 23, 27, 40, 50, 58, 79
40	13	1,192	3	2,708	3	15, 17, 79
41	22	1,411	8	3,178	5	27, 28, 29, 51, 79
42	16	1,510	22	2,833	11	3, 9, 15, 53, 54, 55, 60, 66, 76, 78, 79
43	10	1,322	11	2,485	7	2, 3, 9, 26, 60, 79, 80
44	15	1,445	22	2,944	11	15, 21, 32, 38, 55, 56, 57, 60, 77, 78, 79
45	24	1,617	16	3,332	10	9, 10, 15, 30, 37, 38, 48, 58, 69, 79
46	18	1,503	13	3,045	8	1, 3, 15, 31, 34, 42, 60, 73
47	13	1,440	42	2,89	15	1, 9, 12, 13, 15, 31, 48, 49, 50, 55, 56, 57, 60, 76, 79
48	12	1,684	20	2,773	13	3, 7, 9, 12, 15, 23, 32, 38, 53, 57, 64, 76, 79
49	17	1,612	8	2,996	5	4, 9, 54, 57, 76,
50	11	1,128	1	2,485	1	9
51	20	1,589	9	3,135	8	5, 13, 32, 38, 40, 56, 75, 79
52	15	1,650	6	2,996	6	4, 9, 15, 33, 51, 76
53	26	1,604	24	3,466	13	1, 4, 9, 14, 15, 19, 31, 38, 57, 60, 76, 79, 80
54	22	1,426	19	3,178	10	3, 4, 9, 15, 17, 26, 27, 60, 76, 79
55	5	1,487	8	1,792	4	9, 60, 76, 79
56	12	1,339	15	2,639	9	4, 9, 15, 47, 50, 56, 57, 73, 79
57	9	1,128	1	2,398	1	9
58	12	1,498	9	2,565	4	30, 51, 63, 79
59	17	1,677	23	2,996	10	4, 15, 32, 34, 37, 54, 55, 56, 65, 79
60	14	1,313	14	2,773	9	3, 9, 15, 21, 49, 60, 70, 76, 79

Table 3 Results of multiple linear regression analysis.

Model Summary
Model	Unstandardized Coefficients		R	R Square	Adjusted R Square	Std. Error of the Estimate
	B	Std. Error
(Constant)	2,246	,087	,825	681	,664	,1711867
AWMSI	,256	,056
NP	,016	,003

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